diff --git a/.gitignore b/.gitignore index 639472001..7328b3294 100644 --- a/.gitignore +++ b/.gitignore @@ -33,6 +33,12 @@ tools/Miniconda3-latest-Linux-x86_64.sh tools/activate_python.sh tools/miniconda.sh tools/CRF++-0.58/ +tools/liblbfgs-1.10/ +tools/srilm/ +tools/env.sh +tools/openfst-1.8.1/ +tools/libsndfile/ +tools/python-soundfile/ speechx/fc_patch/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 09e92a667..e3cc36e00 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -50,13 +50,13 @@ repos: entry: bash .pre-commit-hooks/clang-format.hook -i language: system files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$ - exclude: (?=speechx/speechx/kaldi|speechx/patch).*(\.cpp|\.cc|\.h|\.py)$ + exclude: (?=speechx/speechx/kaldi|speechx/patch|speechx/tools/fstbin|speechx/tools/lmbin).*(\.cpp|\.cc|\.h|\.py)$ - id: copyright_checker name: copyright_checker entry: python .pre-commit-hooks/copyright-check.hook language: system files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$ - exclude: (?=third_party|pypinyin|speechx/speechx/kaldi|speechx/patch).*(\.cpp|\.cc|\.h|\.py)$ + exclude: (?=third_party|pypinyin|speechx/speechx/kaldi|speechx/patch|speechx/tools/fstbin|speechx/tools/lmbin).*(\.cpp|\.cc|\.h|\.py)$ - repo: https://github.com/asottile/reorder_python_imports rev: v2.4.0 hooks: diff --git a/README.md b/README.md index a90498293..5093dbd67 100644 --- a/README.md +++ b/README.md @@ -280,10 +280,14 @@ paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input input.wav For more information about server command lines, please see: [speech server demos](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/demos/speech_server) + + ## Model List PaddleSpeech supports a series of most popular models. They are summarized in [released models](./docs/source/released_model.md) and attached with available pretrained models. + + **Speech-to-Text** contains *Acoustic Model*, *Language Model*, and *Speech Translation*, with the following details: @@ -357,6 +361,8 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
+ + **Text-to-Speech** in PaddleSpeech mainly contains three modules: *Text Frontend*, *Acoustic Model* and *Vocoder*. Acoustic Model and Vocoder models are listed as follow: @@ -457,10 +463,10 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r - + @@ -473,6 +479,8 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
GE2E + Tactron2GE2E + Tacotron2 AISHELL-3 - ge2e-tactron2-aishell3 + ge2e-tacotron2-aishell3
+ + **Audio Classification** @@ -496,6 +504,8 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
+ + **Speaker Verification** @@ -519,6 +529,8 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
+ + **Punctuation Restoration** @@ -559,10 +571,18 @@ Normally, [Speech SoTA](https://paperswithcode.com/area/speech), [Audio SoTA](ht - [Advanced Usage](./docs/source/tts/advanced_usage.md) - [Chinese Rule Based Text Frontend](./docs/source/tts/zh_text_frontend.md) - [Test Audio Samples](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html) + - Speaker Verification + - [Audio Searching](./demos/audio_searching/README.md) + - [Speaker Verification](./demos/speaker_verification/README.md) - [Audio Classification](./demos/audio_tagging/README.md) - - [Speaker Verification](./demos/speaker_verification/README.md) - [Speech Translation](./demos/speech_translation/README.md) + - [Speech Server](./demos/speech_server/README.md) - [Released Models](./docs/source/released_model.md) + - [Speech-to-Text](#SpeechToText) + - [Text-to-Speech](#TextToSpeech) + - [Audio Classification](#AudioClassification) + - [Speaker Verification](#SpeakerVerification) + - [Punctuation Restoration](#PunctuationRestoration) - [Community](#Community) - [Welcome to contribute](#contribution) - [License](#License) diff --git a/README_cn.md b/README_cn.md index ab4ce6e6b..5dab7fa0c 100644 --- a/README_cn.md +++ b/README_cn.md @@ -273,6 +273,8 @@ paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input input.wav ## 模型列表 PaddleSpeech 支持很多主流的模型，并提供了预训练模型，详情请见[模型列表](./docs/source/released_model.md)。 + + PaddleSpeech 的 **语音转文本** 包含语音识别声学模型、语音识别语言模型和语音翻译, 详情如下：
@@ -347,6 +349,7 @@ PaddleSpeech 的 **语音转文本** 包含语音识别声学模型、语音识
+ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声学模型和声码器。声学模型和声码器模型如下： @@ -447,10 +450,10 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声 - + @@ -488,6 +491,8 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
GE2E + Tactron2GE2E + Tacotron2 AISHELL-3 - ge2e-tactron2-aishell3 + ge2e-tacotron2-aishell3
+ + **声纹识别** @@ -511,6 +516,8 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
+ + **标点恢复** @@ -556,13 +563,18 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声 - [进阶用法](./docs/source/tts/advanced_usage.md) - [中文文本前端](./docs/source/tts/zh_text_frontend.md) - [测试语音样本](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html) + - 声纹识别 + - [声纹识别](./demos/speaker_verification/README_cn.md) + - [音频检索](./demos/audio_searching/README_cn.md) - [声音分类](./demos/audio_tagging/README_cn.md) - - [声纹识别](./demos/speaker_verification/README_cn.md) - [语音翻译](./demos/speech_translation/README_cn.md) + - [服务化部署](./demos/speech_server/README_cn.md) - [模型列表](#模型列表) - [语音识别](#语音识别模型) - [语音合成](#语音合成模型) - [声音分类](#声音分类模型) + - [声纹识别](#声纹识别模型) + - [标点恢复](#标点恢复模型) - [技术交流群](#技术交流群) - [欢迎贡献](#欢迎贡献) - [License](#License) diff --git a/dataset/rir_noise/rir_noise.py b/dataset/rir_noise/rir_noise.py index e7b122890..009175e5b 100644 --- a/dataset/rir_noise/rir_noise.py +++ b/dataset/rir_noise/rir_noise.py @@ -34,14 +34,14 @@ from utils.utility import unzip DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech') -URL_ROOT = 'http://www.openslr.org/resources/28' +URL_ROOT = '--no-check-certificate http://www.openslr.org/resources/28' DATA_URL = URL_ROOT + '/rirs_noises.zip' MD5_DATA = 'e6f48e257286e05de56413b4779d8ffb' parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--target_dir", - default=DATA_HOME + "/Aishell", + default=DATA_HOME + "/rirs_noise", type=str, help="Directory to save the dataset. (default: %(default)s)") parser.add_argument( @@ -81,6 +81,10 @@ def create_manifest(data_dir, manifest_path_prefix): }, ensure_ascii=False)) manifest_path = manifest_path_prefix + '.' + dtype + + if not os.path.exists(os.path.dirname(manifest_path)): + os.makedirs(os.path.dirname(manifest_path)) + with codecs.open(manifest_path, 'w', 'utf-8') as fout: for line in json_lines: fout.write(line + '\n') diff --git a/dataset/voxceleb/voxceleb1.py b/dataset/voxceleb/voxceleb1.py index 905862008..95827f708 100644 --- a/dataset/voxceleb/voxceleb1.py +++ b/dataset/voxceleb/voxceleb1.py @@ -149,7 +149,7 @@ def prepare_dataset(base_url, data_list, target_dir, manifest_path, # we will download the voxceleb1 data to ${target_dir}/vox1/dev/ or ${target_dir}/vox1/test directory if not os.path.exists(os.path.join(target_dir, "wav")): # download all dataset part - print("start to download the vox1 dev zip package") + print(f"start to download the vox1 zip package to {target_dir}") for zip_part in data_list.keys(): download_url = " --no-check-certificate " + base_url + "/" + zip_part download( diff --git a/dataset/voxceleb/voxceleb2.py b/dataset/voxceleb/voxceleb2.py index 22a2e2ffe..fe9e8b9c8 100644 --- a/dataset/voxceleb/voxceleb2.py +++ b/dataset/voxceleb/voxceleb2.py @@ -22,10 +22,12 @@ import codecs import glob import json import os +import subprocess from pathlib import Path import soundfile +from utils.utility import check_md5sum from utils.utility import download from utils.utility import unzip @@ -35,12 +37,22 @@ DATA_HOME = os.path.expanduser('.') BASE_URL = "--no-check-certificate https://www.robots.ox.ac.uk/~vgg/data/voxceleb/data/" # dev data -DEV_DATA_URL = BASE_URL + '/vox2_aac.zip' -DEV_MD5SUM = "bbc063c46078a602ca71605645c2a402" +DEV_LIST = { + "vox2_dev_aac_partaa": "da070494c573e5c0564b1d11c3b20577", + "vox2_dev_aac_partab": "17fe6dab2b32b48abaf1676429cdd06f", + "vox2_dev_aac_partac": "1de58e086c5edf63625af1cb6d831528", + "vox2_dev_aac_partad": "5a043eb03e15c5a918ee6a52aad477f9", + "vox2_dev_aac_partae": "cea401b624983e2d0b2a87fb5d59aa60", + "vox2_dev_aac_partaf": "fc886d9ba90ab88e7880ee98effd6ae9", + "vox2_dev_aac_partag": "d160ecc3f6ee3eed54d55349531cb42e", + "vox2_dev_aac_partah": "6b84a81b9af72a9d9eecbb3b1f602e65", +} + +DEV_TARGET_DATA = "vox2_dev_aac_parta* vox2_dev_aac.zip bbc063c46078a602ca71605645c2a402" # test data -TEST_DATA_URL = BASE_URL + '/vox2_test_aac.zip' -TEST_MD5SUM = "0d2b3ea430a821c33263b5ea37ede312" +TEST_LIST = {"vox2_test_aac.zip": "0d2b3ea430a821c33263b5ea37ede312"} +TEST_TARGET_DATA = "vox2_test_aac.zip vox2_test_aac.zip 0d2b3ea430a821c33263b5ea37ede312" parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( @@ -68,6 +80,14 @@ args = parser.parse_args() def create_manifest(data_dir, manifest_path_prefix): + """Generate the voxceleb2 dataset manifest file. + We will create the ${manifest_path_prefix}.vox2 as the final manifest file + The dev and test wav info will be put in one manifest file. + + Args: + data_dir (str): voxceleb2 wav directory, which include dev and test subdataset + manifest_path_prefix (str): manifest file prefix + """ print("Creating manifest %s ..." % manifest_path_prefix) json_lines = [] data_path = os.path.join(data_dir, "**", "*.wav") @@ -119,7 +139,19 @@ def create_manifest(data_dir, manifest_path_prefix): print(f"{total_sec / total_num} sec/utt", file=f) -def download_dataset(url, md5sum, target_dir, dataset): +def download_dataset(base_url, data_list, target_data, target_dir, dataset): + """Download the voxceleb2 zip package + + Args: + base_url (str): the voxceleb2 dataset download baseline url + data_list (dict): the dataset part zip package and the md5 value + target_data (str): the final dataset zip info + target_dir (str): the dataset stored directory + dataset (str): the dataset name, dev or test + + Raises: + RuntimeError: the md5sum occurs error + """ if not os.path.exists(target_dir): os.makedirs(target_dir) @@ -129,9 +161,34 @@ def download_dataset(url, md5sum, target_dir, dataset): # but the test dataset will unzip to aac # so, wo create the ${target_dir}/test and unzip the m4a to test dir if not os.path.exists(os.path.join(target_dir, dataset)): - filepath = download(url, md5sum, target_dir) + print(f"start to download the vox2 zip package to {target_dir}") + for zip_part in data_list.keys(): + download_url = " --no-check-certificate " + base_url + "/" + zip_part + download( + url=download_url, + md5sum=data_list[zip_part], + target_dir=target_dir) + + # pack the all part to target zip file + all_target_part, target_name, target_md5sum = target_data.split() + target_name = os.path.join(target_dir, target_name) + if not os.path.exists(target_name): + pack_part_cmd = "cat {}/{} > {}".format(target_dir, all_target_part, + target_name) + subprocess.call(pack_part_cmd, shell=True) + + # check the target zip file md5sum + if not check_md5sum(target_name, target_md5sum): + raise RuntimeError("{} MD5 checkssum failed".format(target_name)) + else: + print("Check {} md5sum successfully".format(target_name)) + if dataset == "test": - unzip(filepath, os.path.join(target_dir, "test")) + # we need make the test directory + unzip(target_name, os.path.join(target_dir, "test")) + else: + # upzip dev zip pacakge and will create the dev directory + unzip(target_name, target_dir) def main(): @@ -142,14 +199,16 @@ def main(): print("download: {}".format(args.download)) if args.download: download_dataset( - url=DEV_DATA_URL, - md5sum=DEV_MD5SUM, + base_url=BASE_URL, + data_list=DEV_LIST, + target_data=DEV_TARGET_DATA, target_dir=args.target_dir, dataset="dev") download_dataset( - url=TEST_DATA_URL, - md5sum=TEST_MD5SUM, + base_url=BASE_URL, + data_list=TEST_LIST, + target_data=TEST_TARGET_DATA, target_dir=args.target_dir, dataset="test") diff --git a/demos/audio_searching/README.md b/demos/audio_searching/README.md index 8a6f38639..87a1956b9 100644 --- a/demos/audio_searching/README.md +++ b/demos/audio_searching/README.md @@ -90,7 +90,7 @@ Then to start the system server, and it provides HTTP backend services. ```bash export PYTHONPATH=$PYTHONPATH:./src:../../paddleaudio - python src/main.py + python src/audio_search.py ``` Then you will see the Application is started: @@ -111,7 +111,7 @@ Then to start the system server, and it provides HTTP backend services. ```bash wget -c https://www.openslr.org/resources/82/cn-celeb_v2.tar.gz && tar -xvf cn-celeb_v2.tar.gz ``` - **Note**: If you want to build a quick demo, you can use ./src/test_main.py:download_audio_data function, it downloads 20 audio files , Subsequent results show this collection as an example + **Note**: If you want to build a quick demo, you can use ./src/test_audio_search.py:download_audio_data function, it downloads 20 audio files , Subsequent results show this collection as an example - Prepare model(Skip this step if you use the default model.) ```bash @@ -123,7 +123,7 @@ Then to start the system server, and it provides HTTP backend services. The internal process is downloading data, loading the paddlespeech model, extracting embedding, storing library, retrieving and deleting library ```bash - python ./src/test_main.py + python ./src/test_audio_search.py ``` Output： diff --git a/demos/audio_searching/README_cn.md b/demos/audio_searching/README_cn.md index 0d0f42a0f..a93dbdc1f 100644 --- a/demos/audio_searching/README_cn.md +++ b/demos/audio_searching/README_cn.md @@ -92,7 +92,7 @@ ffce340b3790 minio/minio:RELEASE.2020-12-03T00-03-10Z "/usr/bin/docker-ent…" ```bash export PYTHONPATH=$PYTHONPATH:./src:../../paddleaudio - python src/main.py + python src/audio_search.py ``` 然后你会看到应用程序启动: @@ -113,7 +113,7 @@ ffce340b3790 minio/minio:RELEASE.2020-12-03T00-03-10Z "/usr/bin/docker-ent…" ```bash wget -c https://www.openslr.org/resources/82/cn-celeb_v2.tar.gz && tar -xvf cn-celeb_v2.tar.gz ``` - **注**：如果希望快速搭建 demo，可以采用 ./src/test_main.py:download_audio_data 内部的 20 条音频，另外后续结果展示以该集合为例 + **注**：如果希望快速搭建 demo，可以采用 ./src/test_audio_search.py:download_audio_data 内部的 20 条音频，另外后续结果展示以该集合为例 - 准备模型（如果使用默认模型，可以跳过此步骤） ```bash @@ -124,7 +124,7 @@ ffce340b3790 minio/minio:RELEASE.2020-12-03T00-03-10Z "/usr/bin/docker-ent…" - 脚本测试（推荐） ```bash - python ./src/test_main.py + python ./src/test_audio_search.py ``` 注：内部将依次下载数据，加载 paddlespeech 模型，提取 embedding，存储建库，检索，删库 diff --git a/demos/audio_searching/src/main.py b/demos/audio_searching/src/audio_search.py similarity index 99% rename from demos/audio_searching/src/main.py rename to demos/audio_searching/src/audio_search.py index db091a39d..3697cee98 100644 --- a/demos/audio_searching/src/main.py +++ b/demos/audio_searching/src/audio_search.py @@ -40,7 +40,6 @@ app.add_middleware( allow_methods=["*"], allow_headers=["*"]) -MODEL = None MILVUS_CLI = MilvusHelper() MYSQL_CLI = MySQLHelper() diff --git a/demos/audio_searching/src/encode.py b/demos/audio_searching/src/encode.py index f67184c29..c89a11c1f 100644 --- a/demos/audio_searching/src/encode.py +++ b/demos/audio_searching/src/encode.py @@ -12,8 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. import numpy as np - from logs import LOGGER + from paddlespeech.cli import VectorExecutor vector_executor = VectorExecutor() diff --git a/demos/audio_searching/src/mysql_helpers.py b/demos/audio_searching/src/mysql_helpers.py index 303838399..9decf7e3b 100644 --- a/demos/audio_searching/src/mysql_helpers.py +++ b/demos/audio_searching/src/mysql_helpers.py @@ -13,6 +13,7 @@ # limitations under the License. import sys +import numpy import pymysql from config import MYSQL_DB from config import MYSQL_HOST @@ -69,7 +70,7 @@ class MySQLHelper(): sys.exit(1) def load_data_to_mysql(self, table_name, data): - # Batch insert (Milvus_ids, img_path) to mysql + # Batch insert (Milvus_ids, audio_path) to mysql self.test_connection() sql = "insert into " + table_name + " (milvus_id,audio_path) values (%s,%s);" try: @@ -82,7 +83,7 @@ class MySQLHelper(): sys.exit(1) def search_by_milvus_ids(self, ids, table_name): - # Get the img_path according to the milvus ids + # Get the audio_path according to the milvus ids self.test_connection() str_ids = str(ids).replace('[', '').replace(']', '') sql = "select audio_path from " + table_name + " where milvus_id in (" + str_ids + ") order by field (milvus_id," + str_ids + ");" @@ -120,14 +121,83 @@ class MySQLHelper(): sys.exit(1) def count_table(self, table_name): - # Get the number of mysql table + # Get the number of spk in mysql table self.test_connection() - sql = "select count(milvus_id) from " + table_name + ";" + sql = "select count(spk_id) from " + table_name + ";" try: self.cursor.execute(sql) results = self.cursor.fetchall() - LOGGER.debug(f"MYSQL count table:{table_name}") + LOGGER.debug(f"MYSQL count table:{results[0][0]}") return results[0][0] except Exception as e: LOGGER.error(f"MYSQL ERROR: {e} with sql: {sql}") sys.exit(1) + + def create_mysql_table_vpr(self, table_name): + # Create mysql table if not exists + self.test_connection() + sql = "create table if not exists " + table_name + "(spk_id TEXT, audio_path TEXT, embedding TEXT);" + try: + self.cursor.execute(sql) + LOGGER.debug(f"MYSQL create table: {table_name} with sql: {sql}") + except Exception as e: + LOGGER.error(f"MYSQL ERROR: {e} with sql: {sql}") + sys.exit(1) + + def load_data_to_mysql_vpr(self, table_name, data): + # Insert (spk, audio, embedding) to mysql + self.test_connection() + sql = "insert into " + table_name + " (spk_id,audio_path,embedding) values (%s,%s,%s);" + try: + self.cursor.execute(sql, data) + LOGGER.debug( + f"MYSQL loads data to table: {table_name} successfully") + except Exception as e: + LOGGER.error(f"MYSQL ERROR: {e} with sql: {sql}") + sys.exit(1) + + def list_vpr(self, table_name): + # Get all records in mysql + self.test_connection() + sql = "select * from " + table_name + " ;" + try: + self.cursor.execute(sql) + results = self.cursor.fetchall() + self.conn.commit() + spk_ids = [res[0] for res in results] + audio_paths = [res[1] for res in results] + embeddings = [ + numpy.array( + str(res[2]).replace('[', '').replace(']', '').split(",")) + for res in results + ] + return spk_ids, audio_paths, embeddings + except Exception as e: + LOGGER.error(f"MYSQL ERROR: {e} with sql: {sql}") + sys.exit(1) + + def search_audio_vpr(self, table_name, spk_id): + # Get the audio_path according to the spk_id + self.test_connection() + sql = "select audio_path from " + table_name + " where spk_id='" + spk_id + "' ;" + try: + self.cursor.execute(sql) + results = self.cursor.fetchall() + LOGGER.debug( + f"MYSQL search by spk id {spk_id} to get audio {results[0][0]}.") + return results[0][0] + except Exception as e: + LOGGER.error(f"MYSQL ERROR: {e} with sql: {sql}") + sys.exit(1) + + def delete_data_vpr(self, table_name, spk_id): + # Delete a record by spk_id in mysql table + self.test_connection() + sql = "delete from " + table_name + " where spk_id='" + spk_id + "';" + try: + self.cursor.execute(sql) + LOGGER.debug( + f"MYSQL delete a record {spk_id} in table {table_name}") + except Exception as e: + LOGGER.error(f"MYSQL ERROR: {e} with sql: {sql}") + sys.exit(1) diff --git a/demos/audio_searching/src/operations/count.py b/demos/audio_searching/src/operations/count.py index 9a1f42082..45614e6c1 100644 --- a/demos/audio_searching/src/operations/count.py +++ b/demos/audio_searching/src/operations/count.py @@ -31,3 +31,45 @@ def do_count(table_name, milvus_cli): except Exception as e: LOGGER.error(f"Error attempting to count table {e}") sys.exit(1) + + +def do_count_vpr(table_name, mysql_cli): + """ + Returns the total number of spk in the system + """ + if not table_name: + table_name = DEFAULT_TABLE + try: + num = mysql_cli.count_table(table_name) + return num + except Exception as e: + LOGGER.error(f"Error attempting to count table {e}") + sys.exit(1) + + +def do_list(table_name, mysql_cli): + """ + Returns the total records of vpr in the system + """ + if not table_name: + table_name = DEFAULT_TABLE + try: + spk_ids, audio_paths, _ = mysql_cli.list_vpr(table_name) + return spk_ids, audio_paths + except Exception as e: + LOGGER.error(f"Error attempting to count table {e}") + sys.exit(1) + + +def do_get(table_name, spk_id, mysql_cli): + """ + Returns the audio path by spk_id in the system + """ + if not table_name: + table_name = DEFAULT_TABLE + try: + audio_apth = mysql_cli.search_audio_vpr(table_name, spk_id) + return audio_apth + except Exception as e: + LOGGER.error(f"Error attempting to count table {e}") + sys.exit(1) diff --git a/demos/audio_searching/src/operations/drop.py b/demos/audio_searching/src/operations/drop.py index f8278ddd0..fea9a6116 100644 --- a/demos/audio_searching/src/operations/drop.py +++ b/demos/audio_searching/src/operations/drop.py @@ -32,3 +32,31 @@ def do_drop(table_name, milvus_cli, mysql_cli): except Exception as e: LOGGER.error(f"Error attempting to drop table: {e}") sys.exit(1) + + +def do_drop_vpr(table_name, mysql_cli): + """ + Delete the table of MySQL + """ + if not table_name: + table_name = DEFAULT_TABLE + try: + mysql_cli.delete_table(table_name) + return "OK" + except Exception as e: + LOGGER.error(f"Error attempting to drop table: {e}") + sys.exit(1) + + +def do_delete(table_name, spk_id, mysql_cli): + """ + Delete a record by spk_id in MySQL + """ + if not table_name: + table_name = DEFAULT_TABLE + try: + mysql_cli.delete_data_vpr(table_name, spk_id) + return "OK" + except Exception as e: + LOGGER.error(f"Error attempting to drop table: {e}") + sys.exit(1) diff --git a/demos/audio_searching/src/operations/load.py b/demos/audio_searching/src/operations/load.py index 80b6375fa..0d9edb784 100644 --- a/demos/audio_searching/src/operations/load.py +++ b/demos/audio_searching/src/operations/load.py @@ -82,3 +82,16 @@ def do_load(table_name, audio_dir, milvus_cli, mysql_cli): mysql_cli.create_mysql_table(table_name) mysql_cli.load_data_to_mysql(table_name, format_data(ids, names)) return len(ids) + + +def do_enroll(table_name, spk_id, audio_path, mysql_cli): + """ + Import spk_id,audio_path,embedding to Mysql + """ + if not table_name: + table_name = DEFAULT_TABLE + embedding = get_audio_embedding(audio_path) + mysql_cli.create_mysql_table_vpr(table_name) + data = (spk_id, audio_path, str(embedding)) + mysql_cli.load_data_to_mysql_vpr(table_name, data) + return "OK" diff --git a/demos/audio_searching/src/operations/search.py b/demos/audio_searching/src/operations/search.py index 9cf48abf9..0450da81f 100644 --- a/demos/audio_searching/src/operations/search.py +++ b/demos/audio_searching/src/operations/search.py @@ -13,6 +13,7 @@ # limitations under the License. import sys +import numpy from config import DEFAULT_TABLE from config import TOP_K from encode import get_audio_embedding @@ -39,3 +40,26 @@ def do_search(host, table_name, audio_path, milvus_cli, mysql_cli): except Exception as e: LOGGER.error(f"Error with search: {e}") sys.exit(1) + + +def do_search_vpr(host, table_name, audio_path, mysql_cli): + """ + Search the uploaded audio in MySQL + """ + try: + if not table_name: + table_name = DEFAULT_TABLE + emb = get_audio_embedding(audio_path) + emb = numpy.array(emb) + spk_ids, paths, vectors = mysql_cli.list_vpr(table_name) + scores = [numpy.dot(emb, x.astype(numpy.float64)) for x in vectors] + spk_ids = [str(x) for x in spk_ids] + paths = [str(x) for x in paths] + for i in range(len(paths)): + tmp = "http://" + str(host) + "/data?audio_path=" + str(paths[i]) + paths[i] = tmp + scores[i] = scores[i] * 100 + return spk_ids, paths, scores + except Exception as e: + LOGGER.error(f"Error with search: {e}") + sys.exit(1) diff --git a/demos/audio_searching/src/test_main.py b/demos/audio_searching/src/test_audio_search.py similarity index 96% rename from demos/audio_searching/src/test_main.py rename to demos/audio_searching/src/test_audio_search.py index 32030bae7..cb91e1562 100644 --- a/demos/audio_searching/src/test_main.py +++ b/demos/audio_searching/src/test_audio_search.py @@ -11,8 +11,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from audio_search import app from fastapi.testclient import TestClient -from main import app from utils.utility import download from utils.utility import unpack @@ -22,7 +22,7 @@ client = TestClient(app) def download_audio_data(): """ - download audio data + Download audio data """ url = "https://paddlespeech.bj.bcebos.com/vector/audio/example_audio.tar.gz" md5sum = "52ac69316c1aa1fdef84da7dd2c67b39" @@ -64,7 +64,7 @@ def test_count(): """ Returns the total number of vectors in the system """ - response = client.get("audio/count") + response = client.get("/audio/count") assert response.status_code == 200 assert response.json() == 20 diff --git a/demos/audio_searching/src/test_vpr_search.py b/demos/audio_searching/src/test_vpr_search.py new file mode 100644 index 000000000..8cc8dc841 --- /dev/null +++ b/demos/audio_searching/src/test_vpr_search.py @@ -0,0 +1,115 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from fastapi.testclient import TestClient +from vpr_search import app + +from utils.utility import download +from utils.utility import unpack + +client = TestClient(app) + + +def download_audio_data(): + """ + Download audio data + """ + url = "https://paddlespeech.bj.bcebos.com/vector/audio/example_audio.tar.gz" + md5sum = "52ac69316c1aa1fdef84da7dd2c67b39" + target_dir = "./" + filepath = download(url, md5sum, target_dir) + unpack(filepath, target_dir, True) + + +def test_drop(): + """ + Delete the table of MySQL + """ + response = client.post("/vpr/drop") + assert response.status_code == 200 + + +def test_enroll_local(spk: str, audio: str): + """ + Enroll the audio to MySQL + """ + response = client.post("/vpr/enroll/local?spk_id=" + spk + + "&audio_path=.%2Fexample_audio%2F" + audio + ".wav") + assert response.status_code == 200 + assert response.json() == { + 'status': True, + 'msg': "Successfully enroll data!" + } + + +def test_search_local(): + """ + Search the spk in MySQL by audio + """ + response = client.post( + "/vpr/recog/local?audio_path=.%2Fexample_audio%2Ftest.wav") + assert response.status_code == 200 + + +def test_list(): + """ + Get all records in MySQL + """ + response = client.get("/vpr/list") + assert response.status_code == 200 + + +def test_data(spk: str): + """ + Get the audio file by spk_id in MySQL + """ + response = client.get("/vpr/data?spk_id=" + spk) + assert response.status_code == 200 + + +def test_del(spk: str): + """ + Delete the record in MySQL by spk_id + """ + response = client.post("/vpr/del?spk_id=" + spk) + assert response.status_code == 200 + + +def test_count(): + """ + Get the number of spk in MySQL + """ + response = client.get("/vpr/count") + assert response.status_code == 200 + + +if __name__ == "__main__": + download_audio_data() + + test_enroll_local("spk1", "arms_strikes") + test_enroll_local("spk2", "sword_wielding") + test_enroll_local("spk3", "test") + test_list() + test_data("spk1") + test_count() + test_search_local() + + test_del("spk1") + test_count() + test_search_local() + + test_enroll_local("spk1", "arms_strikes") + test_count() + test_search_local() + + test_drop() diff --git a/demos/audio_searching/src/vpr_search.py b/demos/audio_searching/src/vpr_search.py new file mode 100644 index 000000000..8e702221c --- /dev/null +++ b/demos/audio_searching/src/vpr_search.py @@ -0,0 +1,206 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import uvicorn +from config import UPLOAD_PATH +from fastapi import FastAPI +from fastapi import File +from fastapi import UploadFile +from logs import LOGGER +from mysql_helpers import MySQLHelper +from operations.count import do_count_vpr +from operations.count import do_get +from operations.count import do_list +from operations.drop import do_delete +from operations.drop import do_drop_vpr +from operations.load import do_enroll +from operations.search import do_search_vpr +from starlette.middleware.cors import CORSMiddleware +from starlette.requests import Request +from starlette.responses import FileResponse + +app = FastAPI() +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"]) + +MYSQL_CLI = MySQLHelper() + +# Mkdir 'tmp/audio-data' +if not os.path.exists(UPLOAD_PATH): + os.makedirs(UPLOAD_PATH) + LOGGER.info(f"Mkdir the path: {UPLOAD_PATH}") + + +@app.post('/vpr/enroll') +async def vpr_enroll(table_name: str=None, + spk_id: str=None, + audio: UploadFile=File(...)): + # Enroll the uploaded audio with spk-id into MySQL + try: + # Save the upload data to server. + content = await audio.read() + audio_path = os.path.join(UPLOAD_PATH, audio.filename) + with open(audio_path, "wb+") as f: + f.write(content) + do_enroll(table_name, spk_id, audio_path, MYSQL_CLI) + LOGGER.info(f"Successfully enrolled {spk_id} online!") + return {'status': True, 'msg': "Successfully enroll data!"} + except Exception as e: + LOGGER.error(e) + return {'status': False, 'msg': e}, 400 + + +@app.post('/vpr/enroll/local') +async def vpr_enroll_local(table_name: str=None, + spk_id: str=None, + audio_path: str=None): + # Enroll the local audio with spk-id into MySQL + try: + do_enroll(table_name, spk_id, audio_path, MYSQL_CLI) + LOGGER.info(f"Successfully enrolled {spk_id} locally!") + return {'status': True, 'msg': "Successfully enroll data!"} + except Exception as e: + LOGGER.error(e) + return {'status': False, 'msg': e}, 400 + + +@app.post('/vpr/recog') +async def vpr_recog(request: Request, + table_name: str=None, + audio: UploadFile=File(...)): + # Voice print recognition online + try: + # Save the upload data to server. + content = await audio.read() + query_audio_path = os.path.join(UPLOAD_PATH, audio.filename) + with open(query_audio_path, "wb+") as f: + f.write(content) + host = request.headers['host'] + spk_ids, paths, scores = do_search_vpr(host, table_name, + query_audio_path, MYSQL_CLI) + for spk_id, path, score in zip(spk_ids, paths, scores): + LOGGER.info(f"spk {spk_id}, score {score}, audio path {path}, ") + res = dict(zip(spk_ids, zip(paths, scores))) + # Sort results by distance metric, closest distances first + res = sorted(res.items(), key=lambda item: item[1][1], reverse=True) + LOGGER.info("Successfully speaker recognition online!") + return res + except Exception as e: + LOGGER.error(e) + return {'status': False, 'msg': e}, 400 + + +@app.post('/vpr/recog/local') +async def vpr_recog_local(request: Request, + table_name: str=None, + audio_path: str=None): + # Voice print recognition locally + try: + host = request.headers['host'] + spk_ids, paths, scores = do_search_vpr(host, table_name, audio_path, + MYSQL_CLI) + for spk_id, path, score in zip(spk_ids, paths, scores): + LOGGER.info(f"spk {spk_id}, score {score}, audio path {path}, ") + res = dict(zip(spk_ids, zip(paths, scores))) + # Sort results by distance metric, closest distances first + res = sorted(res.items(), key=lambda item: item[1][1], reverse=True) + LOGGER.info("Successfully speaker recognition locally!") + return res + except Exception as e: + LOGGER.error(e) + return {'status': False, 'msg': e}, 400 + + +@app.post('/vpr/del') +async def vpr_del(table_name: str=None, spk_id: str=None): + # Delete a record by spk_id in MySQL + try: + do_delete(table_name, spk_id, MYSQL_CLI) + LOGGER.info("Successfully delete a record by spk_id in MySQL") + return {'status': True, 'msg': "Successfully delete data!"} + except Exception as e: + LOGGER.error(e) + return {'status': False, 'msg': e}, 400 + + +@app.get('/vpr/list') +async def vpr_list(table_name: str=None): + # Get all records in MySQL + try: + spk_ids, audio_paths = do_list(table_name, MYSQL_CLI) + for i in range(len(spk_ids)): + LOGGER.debug(f"spk {spk_ids[i]}, audio path {audio_paths[i]}") + LOGGER.info("Successfully list all records from mysql!") + return spk_ids, audio_paths + except Exception as e: + LOGGER.error(e) + return {'status': False, 'msg': e}, 400 + + +@app.get('/vpr/data') +async def vpr_data( + table_name: str=None, + spk_id: str=None, ): + # Get the audio file from path by spk_id in MySQL + try: + audio_path = do_get(table_name, spk_id, MYSQL_CLI) + LOGGER.info(f"Successfully get audio path {audio_path}!") + return FileResponse(audio_path) + except Exception as e: + LOGGER.error(e) + return {'status': False, 'msg': e}, 400 + + +@app.get('/vpr/count') +async def vpr_count(table_name: str=None): + # Get the total number of spk in MySQL + try: + num = do_count_vpr(table_name, MYSQL_CLI) + LOGGER.info("Successfully count the number of spk!") + return num + except Exception as e: + LOGGER.error(e) + return {'status': False, 'msg': e}, 400 + + +@app.post('/vpr/drop') +async def drop_tables(table_name: str=None): + # Delete the table of MySQL + try: + do_drop_vpr(table_name, MYSQL_CLI) + LOGGER.info("Successfully drop tables in MySQL!") + return {'status': True, 'msg': "Successfully drop tables!"} + except Exception as e: + LOGGER.error(e) + return {'status': False, 'msg': e}, 400 + + +@app.get('/data') +def audio_path(audio_path): + # Get the audio file from path + try: + LOGGER.info(f"Successfully get audio: {audio_path}") + return FileResponse(audio_path) + except Exception as e: + LOGGER.error(f"get audio error: {e}") + return {'status': False, 'msg': e}, 400 + + +if __name__ == '__main__': + uvicorn.run(app=app, host='0.0.0.0', port=8002) diff --git a/demos/speaker_verification/README.md b/demos/speaker_verification/README.md index 8739d402d..7d7180ae9 100644 --- a/demos/speaker_verification/README.md +++ b/demos/speaker_verification/README.md @@ -30,6 +30,11 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav paddlespeech vector --task spk --input vec.job echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk + + paddlespeech vector --task score --input "./85236145389.wav ./123456789.wav" + + echo -e "demo4 85236145389.wav 85236145389.wav \n demo5 85236145389.wav 123456789.wav" > vec.job + paddlespeech vector --task score --input vec.job ``` Usage: @@ -38,6 +43,7 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ``` Arguments: - `input`(required): Audio file to recognize. + - `task` (required): Specify `vector` task. Default `spk`。 - `model`: Model type of vector task. Default: `ecapatdnn_voxceleb12`. - `sample_rate`: Sample rate of the model. Default: `16000`. - `config`: Config of vector task. Use pretrained model when it is None. Default: `None`. @@ -47,45 +53,45 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav Output: ```bash - demo [ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268 - -3.04878 1.611095 10.127234 -10.534177 -15.821609 - 1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228 - -11.343508 2.3385992 -8.719341 14.213509 15.404744 - -0.39327756 6.338786 2.688887 8.7104025 17.469526 - -8.77959 7.0576906 4.648855 -1.3089896 -23.294737 - 8.013747 13.891729 -9.926753 5.655307 -5.9422326 - -22.842539 0.6293588 -18.46266 -10.811862 9.8192625 - 3.0070958 3.8072643 -2.3861165 3.0821571 -14.739942 - 1.7594414 -0.6485091 4.485623 2.0207152 7.264915 - -6.40137 23.63524 2.9711294 -22.708025 9.93719 - 20.354511 -10.324688 -0.700492 -8.783211 -5.27593 - 15.999649 3.3004563 12.747926 15.429879 4.7849145 - 5.6699696 -2.3826702 10.605882 3.9112158 3.1500628 - 15.859915 -2.1832209 -23.908653 -6.4799504 -4.5365124 - -9.224193 14.568347 -10.568833 4.982321 -4.342062 - 0.0914714 12.645902 -5.74285 -3.2141201 -2.7173362 - -6.680575 0.4757669 -5.035051 -6.7964664 16.865469 - -11.54324 7.681869 0.44475392 9.708182 -8.932846 - 0.4123232 -4.361452 1.3948607 9.511665 0.11667654 - 2.9079323 6.049952 9.275183 -18.078873 6.2983274 - -0.7500531 -2.725033 -7.6027865 3.3404543 2.990815 - 4.010979 11.000591 -2.8873312 7.1352735 -16.79663 - 18.495346 -14.293832 7.89578 2.2714825 22.976387 - -4.875734 -3.0836344 -2.9999814 13.751918 6.448228 - -11.924197 2.171869 2.0423572 -6.173772 10.778437 - 25.77281 -4.9495463 14.57806 0.3044315 2.6132357 - -7.591999 -2.076944 9.025118 1.7834753 -3.1799617 - -4.9401326 23.465864 5.1685796 -9.018578 9.037825 - -4.4150195 6.859591 -12.274467 -0.88911164 5.186309 - -3.9988663 -13.638606 -9.925445 -0.06329413 -3.6709652 - -12.397416 -12.719869 -1.395601 2.1150916 5.7381287 - -4.4691963 -3.82819 -0.84233856 -1.1604277 -13.490127 - 8.731719 -20.778936 -11.495662 5.8033476 -4.752041 - 10.833007 -6.717991 4.504732 13.4244375 1.1306485 - 7.3435574 1.400918 14.704036 -9.501399 7.2315617 - -6.417456 1.3333273 11.872697 -0.30664724 8.8845 - 6.5569253 4.7948146 0.03662816 -8.704245 6.224871 - -3.2701402 -11.508579 ] + demo [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055 + 1.756596 5.167894 10.80636 -3.8226728 -5.6141334 + 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897 + -9.723131 0.6619743 -6.976803 10.213478 7.494748 + 2.9105635 3.8949256 3.7999806 7.1061673 16.905321 + -7.1493764 8.733103 3.4230042 -4.831653 -11.403367 + 11.232214 7.1274667 -4.2828417 2.452362 -5.130748 + -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683 + 0.7618269 1.1253023 -2.083836 4.725744 -8.782597 + -3.539873 3.814236 5.1420674 2.162061 4.096431 + -6.4162116 12.747448 1.9429878 -15.152943 6.417416 + 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939 + 11.567354 3.69788 11.258265 7.442363 9.183411 + 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783 + 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073 + -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116 + 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578 + -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651 + -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556 + -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704 + 3.272176 2.8382776 5.134597 -9.190781 -0.5657382 + -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576 + -0.31784213 9.493548 2.1144536 4.358092 -12.089823 + 8.451689 -7.925461 4.6242585 4.4289427 18.692003 + -2.6204622 -5.149185 -0.35821092 8.488551 4.981496 + -9.32683 -2.2544234 6.6417594 1.2119585 10.977129 + 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716 + -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796 + 0.66607 15.443222 4.740594 -3.4725387 11.592567 + -2.054497 1.7361217 -8.265324 -9.30447 5.4068313 + -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733 + -8.649895 -9.998958 -2.564841 -0.53999114 2.601808 + -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085 + 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456 + 7.3629923 0.4657332 3.132599 12.438889 -1.8337058 + 4.532936 2.7264361 10.145339 -6.521951 2.897153 + -3.3925855 5.079156 7.759716 4.677565 5.8457737 + 2.402413 7.7071047 3.9711342 -6.390043 6.1268735 + -3.7760346 -11.118123 ] ``` - Python API @@ -97,56 +103,113 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav audio_emb = vector_executor( model='ecapatdnn_voxceleb12', sample_rate=16000, - config=None, + config=None, # Set `config` and `ckpt_path` to None to use pretrained model. ckpt_path=None, audio_file='./85236145389.wav', - force_yes=False, device=paddle.get_device()) print('Audio embedding Result: \n{}'.format(audio_emb)) + + test_emb = vector_executor( + model='ecapatdnn_voxceleb12', + sample_rate=16000, + config=None, # Set `config` and `ckpt_path` to None to use pretrained model. + ckpt_path=None, + audio_file='./123456789.wav', + device=paddle.get_device()) + print('Test embedding Result: \n{}'.format(test_emb)) + + # score range [0, 1] + score = vector_executor.get_embeddings_score(audio_emb, test_emb) + print(f"Eembeddings Score: {score}") ``` - Output: + Output： + ```bash # Vector Result: - [ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268 - -3.04878 1.611095 10.127234 -10.534177 -15.821609 - 1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228 - -11.343508 2.3385992 -8.719341 14.213509 15.404744 - -0.39327756 6.338786 2.688887 8.7104025 17.469526 - -8.77959 7.0576906 4.648855 -1.3089896 -23.294737 - 8.013747 13.891729 -9.926753 5.655307 -5.9422326 - -22.842539 0.6293588 -18.46266 -10.811862 9.8192625 - 3.0070958 3.8072643 -2.3861165 3.0821571 -14.739942 - 1.7594414 -0.6485091 4.485623 2.0207152 7.264915 - -6.40137 23.63524 2.9711294 -22.708025 9.93719 - 20.354511 -10.324688 -0.700492 -8.783211 -5.27593 - 15.999649 3.3004563 12.747926 15.429879 4.7849145 - 5.6699696 -2.3826702 10.605882 3.9112158 3.1500628 - 15.859915 -2.1832209 -23.908653 -6.4799504 -4.5365124 - -9.224193 14.568347 -10.568833 4.982321 -4.342062 - 0.0914714 12.645902 -5.74285 -3.2141201 -2.7173362 - -6.680575 0.4757669 -5.035051 -6.7964664 16.865469 - -11.54324 7.681869 0.44475392 9.708182 -8.932846 - 0.4123232 -4.361452 1.3948607 9.511665 0.11667654 - 2.9079323 6.049952 9.275183 -18.078873 6.2983274 - -0.7500531 -2.725033 -7.6027865 3.3404543 2.990815 - 4.010979 11.000591 -2.8873312 7.1352735 -16.79663 - 18.495346 -14.293832 7.89578 2.2714825 22.976387 - -4.875734 -3.0836344 -2.9999814 13.751918 6.448228 - -11.924197 2.171869 2.0423572 -6.173772 10.778437 - 25.77281 -4.9495463 14.57806 0.3044315 2.6132357 - -7.591999 -2.076944 9.025118 1.7834753 -3.1799617 - -4.9401326 23.465864 5.1685796 -9.018578 9.037825 - -4.4150195 6.859591 -12.274467 -0.88911164 5.186309 - -3.9988663 -13.638606 -9.925445 -0.06329413 -3.6709652 - -12.397416 -12.719869 -1.395601 2.1150916 5.7381287 - -4.4691963 -3.82819 -0.84233856 -1.1604277 -13.490127 - 8.731719 -20.778936 -11.495662 5.8033476 -4.752041 - 10.833007 -6.717991 4.504732 13.4244375 1.1306485 - 7.3435574 1.400918 14.704036 -9.501399 7.2315617 - -6.417456 1.3333273 11.872697 -0.30664724 8.8845 - 6.5569253 4.7948146 0.03662816 -8.704245 6.224871 - -3.2701402 -11.508579 ] + Audio embedding Result: + [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055 + 1.756596 5.167894 10.80636 -3.8226728 -5.6141334 + 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897 + -9.723131 0.6619743 -6.976803 10.213478 7.494748 + 2.9105635 3.8949256 3.7999806 7.1061673 16.905321 + -7.1493764 8.733103 3.4230042 -4.831653 -11.403367 + 11.232214 7.1274667 -4.2828417 2.452362 -5.130748 + -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683 + 0.7618269 1.1253023 -2.083836 4.725744 -8.782597 + -3.539873 3.814236 5.1420674 2.162061 4.096431 + -6.4162116 12.747448 1.9429878 -15.152943 6.417416 + 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939 + 11.567354 3.69788 11.258265 7.442363 9.183411 + 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783 + 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073 + -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116 + 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578 + -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651 + -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556 + -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704 + 3.272176 2.8382776 5.134597 -9.190781 -0.5657382 + -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576 + -0.31784213 9.493548 2.1144536 4.358092 -12.089823 + 8.451689 -7.925461 4.6242585 4.4289427 18.692003 + -2.6204622 -5.149185 -0.35821092 8.488551 4.981496 + -9.32683 -2.2544234 6.6417594 1.2119585 10.977129 + 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716 + -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796 + 0.66607 15.443222 4.740594 -3.4725387 11.592567 + -2.054497 1.7361217 -8.265324 -9.30447 5.4068313 + -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733 + -8.649895 -9.998958 -2.564841 -0.53999114 2.601808 + -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085 + 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456 + 7.3629923 0.4657332 3.132599 12.438889 -1.8337058 + 4.532936 2.7264361 10.145339 -6.521951 2.897153 + -3.3925855 5.079156 7.759716 4.677565 5.8457737 + 2.402413 7.7071047 3.9711342 -6.390043 6.1268735 + -3.7760346 -11.118123 ] + # get the test embedding + Test embedding Result: + [ -1.902964 2.0690894 -8.034194 3.5472693 0.18089125 + 6.9085927 1.4097427 -1.9487704 -10.021278 -0.20755845 + -8.04332 4.344489 2.3200977 -14.306299 5.184692 + -11.55602 -3.8497238 0.6444722 1.2833948 2.6766639 + 0.5878921 0.7946299 1.7207596 2.5791872 14.998469 + -1.3385371 15.031221 -0.8006958 1.99287 -9.52007 + 2.435466 4.003221 -4.33817 -4.898601 -5.304714 + -18.033886 10.790787 -12.784645 -5.641755 2.9761686 + -10.566622 1.4839455 6.152458 -5.7195854 2.8603241 + 6.112133 8.489869 5.5958056 1.2836679 -1.2293907 + 0.89927405 7.0288725 -2.854029 -0.9782962 5.8255906 + 14.905906 -5.025907 0.7866458 -4.2444224 -16.354029 + 10.521315 0.9604709 -3.3257897 7.144871 -13.592733 + -8.568869 -1.7953678 0.26313916 10.916714 -6.9374123 + 1.857403 -6.2746415 2.8154466 -7.2338667 -2.293357 + -0.05452765 5.4287076 5.0849075 -6.690375 -1.6183422 + 3.654291 0.94352573 -9.200294 -5.4749465 -3.5235846 + 1.3420814 4.240421 -2.772944 -2.8451524 16.311104 + 4.2969875 -1.762936 -12.5758915 8.595198 -0.8835239 + -1.5708797 1.568961 1.1413603 3.5032008 -0.45251232 + -6.786333 16.89443 5.3366146 -8.789056 0.6355629 + 3.2579517 -3.328322 7.5969577 0.66025066 -6.550468 + -9.148656 2.020372 -0.4615173 1.1965656 -3.8764873 + 11.6562195 -6.0750933 12.182899 3.2218833 0.81969476 + 5.570001 -3.8459578 -7.205299 7.9262037 -7.6611166 + -5.249467 -2.2671914 7.2658715 -13.298164 4.821147 + -2.7263982 11.691089 -3.8918593 -2.838112 -1.0336838 + -3.8034165 2.8536487 -5.60398 -1.1972581 1.3455094 + -3.4903061 2.2408795 5.5010734 -3.970756 11.99696 + -7.8858757 0.43160373 -5.5059714 4.3426995 16.322706 + 11.635366 0.72157705 -9.245714 -3.91465 -4.449838 + -1.5716927 7.713747 -2.2430465 -6.198303 -13.481864 + 2.8156567 -5.7812386 5.1456156 2.7289324 -14.505571 + 13.270688 3.448231 -7.0659585 4.5886116 -4.466099 + -0.296428 -11.463529 -2.6076477 14.110243 -6.9725137 + -1.9962958 2.7119343 19.391657 0.01961198 14.607133 + -1.6695905 -4.391516 1.3131028 -6.670972 -5.888604 + 12.0612335 5.9285784 3.3715196 1.492534 10.723728 + -0.95514804 -12.085431 ] + # get the score between enroll and test + Eembeddings Score: 0.4292638301849365 ``` ### 4.Pretrained Models diff --git a/demos/speaker_verification/README_cn.md b/demos/speaker_verification/README_cn.md index fe8949b3c..db382f298 100644 --- a/demos/speaker_verification/README_cn.md +++ b/demos/speaker_verification/README_cn.md @@ -29,6 +29,11 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav paddlespeech vector --task spk --input vec.job echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk + + paddlespeech vector --task score --input "./85236145389.wav ./123456789.wav" + + echo -e "demo4 85236145389.wav 85236145389.wav \n demo5 85236145389.wav 123456789.wav" > vec.job + paddlespeech vector --task score --input vec.job ``` 使用方法： @@ -37,6 +42,7 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ``` 参数： - `input`(必须输入)：用于识别的音频文件。 + - `task` (必须输入): 用于指定 `vector` 处理的具体任务，默认是 `spk`。 - `model`：声纹任务的模型，默认值：`ecapatdnn_voxceleb12`。 - `sample_rate`：音频采样率，默认值：`16000`。 - `config`：声纹任务的参数文件，若不设置则使用预训练模型中的默认配置，默认值：`None`。 @@ -45,45 +51,45 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav 输出： ```bash - demo [ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268 - -3.04878 1.611095 10.127234 -10.534177 -15.821609 - 1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228 - -11.343508 2.3385992 -8.719341 14.213509 15.404744 - -0.39327756 6.338786 2.688887 8.7104025 17.469526 - -8.77959 7.0576906 4.648855 -1.3089896 -23.294737 - 8.013747 13.891729 -9.926753 5.655307 -5.9422326 - -22.842539 0.6293588 -18.46266 -10.811862 9.8192625 - 3.0070958 3.8072643 -2.3861165 3.0821571 -14.739942 - 1.7594414 -0.6485091 4.485623 2.0207152 7.264915 - -6.40137 23.63524 2.9711294 -22.708025 9.93719 - 20.354511 -10.324688 -0.700492 -8.783211 -5.27593 - 15.999649 3.3004563 12.747926 15.429879 4.7849145 - 5.6699696 -2.3826702 10.605882 3.9112158 3.1500628 - 15.859915 -2.1832209 -23.908653 -6.4799504 -4.5365124 - -9.224193 14.568347 -10.568833 4.982321 -4.342062 - 0.0914714 12.645902 -5.74285 -3.2141201 -2.7173362 - -6.680575 0.4757669 -5.035051 -6.7964664 16.865469 - -11.54324 7.681869 0.44475392 9.708182 -8.932846 - 0.4123232 -4.361452 1.3948607 9.511665 0.11667654 - 2.9079323 6.049952 9.275183 -18.078873 6.2983274 - -0.7500531 -2.725033 -7.6027865 3.3404543 2.990815 - 4.010979 11.000591 -2.8873312 7.1352735 -16.79663 - 18.495346 -14.293832 7.89578 2.2714825 22.976387 - -4.875734 -3.0836344 -2.9999814 13.751918 6.448228 - -11.924197 2.171869 2.0423572 -6.173772 10.778437 - 25.77281 -4.9495463 14.57806 0.3044315 2.6132357 - -7.591999 -2.076944 9.025118 1.7834753 -3.1799617 - -4.9401326 23.465864 5.1685796 -9.018578 9.037825 - -4.4150195 6.859591 -12.274467 -0.88911164 5.186309 - -3.9988663 -13.638606 -9.925445 -0.06329413 -3.6709652 - -12.397416 -12.719869 -1.395601 2.1150916 5.7381287 - -4.4691963 -3.82819 -0.84233856 -1.1604277 -13.490127 - 8.731719 -20.778936 -11.495662 5.8033476 -4.752041 - 10.833007 -6.717991 4.504732 13.4244375 1.1306485 - 7.3435574 1.400918 14.704036 -9.501399 7.2315617 - -6.417456 1.3333273 11.872697 -0.30664724 8.8845 - 6.5569253 4.7948146 0.03662816 -8.704245 6.224871 - -3.2701402 -11.508579 ] + demo [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055 + 1.756596 5.167894 10.80636 -3.8226728 -5.6141334 + 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897 + -9.723131 0.6619743 -6.976803 10.213478 7.494748 + 2.9105635 3.8949256 3.7999806 7.1061673 16.905321 + -7.1493764 8.733103 3.4230042 -4.831653 -11.403367 + 11.232214 7.1274667 -4.2828417 2.452362 -5.130748 + -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683 + 0.7618269 1.1253023 -2.083836 4.725744 -8.782597 + -3.539873 3.814236 5.1420674 2.162061 4.096431 + -6.4162116 12.747448 1.9429878 -15.152943 6.417416 + 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939 + 11.567354 3.69788 11.258265 7.442363 9.183411 + 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783 + 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073 + -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116 + 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578 + -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651 + -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556 + -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704 + 3.272176 2.8382776 5.134597 -9.190781 -0.5657382 + -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576 + -0.31784213 9.493548 2.1144536 4.358092 -12.089823 + 8.451689 -7.925461 4.6242585 4.4289427 18.692003 + -2.6204622 -5.149185 -0.35821092 8.488551 4.981496 + -9.32683 -2.2544234 6.6417594 1.2119585 10.977129 + 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716 + -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796 + 0.66607 15.443222 4.740594 -3.4725387 11.592567 + -2.054497 1.7361217 -8.265324 -9.30447 5.4068313 + -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733 + -8.649895 -9.998958 -2.564841 -0.53999114 2.601808 + -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085 + 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456 + 7.3629923 0.4657332 3.132599 12.438889 -1.8337058 + 4.532936 2.7264361 10.145339 -6.521951 2.897153 + -3.3925855 5.079156 7.759716 4.677565 5.8457737 + 2.402413 7.7071047 3.9711342 -6.390043 6.1268735 + -3.7760346 -11.118123 ] ``` - Python API @@ -98,53 +104,109 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav config=None, # Set `config` and `ckpt_path` to None to use pretrained model. ckpt_path=None, audio_file='./85236145389.wav', - force_yes=False, device=paddle.get_device()) print('Audio embedding Result: \n{}'.format(audio_emb)) + + test_emb = vector_executor( + model='ecapatdnn_voxceleb12', + sample_rate=16000, + config=None, # Set `config` and `ckpt_path` to None to use pretrained model. + ckpt_path=None, + audio_file='./123456789.wav', + device=paddle.get_device()) + print('Test embedding Result: \n{}'.format(test_emb)) + + # score range [0, 1] + score = vector_executor.get_embeddings_score(audio_emb, test_emb) + print(f"Eembeddings Score: {score}") ``` 输出： ```bash # Vector Result: - [ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268 - -3.04878 1.611095 10.127234 -10.534177 -15.821609 - 1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228 - -11.343508 2.3385992 -8.719341 14.213509 15.404744 - -0.39327756 6.338786 2.688887 8.7104025 17.469526 - -8.77959 7.0576906 4.648855 -1.3089896 -23.294737 - 8.013747 13.891729 -9.926753 5.655307 -5.9422326 - -22.842539 0.6293588 -18.46266 -10.811862 9.8192625 - 3.0070958 3.8072643 -2.3861165 3.0821571 -14.739942 - 1.7594414 -0.6485091 4.485623 2.0207152 7.264915 - -6.40137 23.63524 2.9711294 -22.708025 9.93719 - 20.354511 -10.324688 -0.700492 -8.783211 -5.27593 - 15.999649 3.3004563 12.747926 15.429879 4.7849145 - 5.6699696 -2.3826702 10.605882 3.9112158 3.1500628 - 15.859915 -2.1832209 -23.908653 -6.4799504 -4.5365124 - -9.224193 14.568347 -10.568833 4.982321 -4.342062 - 0.0914714 12.645902 -5.74285 -3.2141201 -2.7173362 - -6.680575 0.4757669 -5.035051 -6.7964664 16.865469 - -11.54324 7.681869 0.44475392 9.708182 -8.932846 - 0.4123232 -4.361452 1.3948607 9.511665 0.11667654 - 2.9079323 6.049952 9.275183 -18.078873 6.2983274 - -0.7500531 -2.725033 -7.6027865 3.3404543 2.990815 - 4.010979 11.000591 -2.8873312 7.1352735 -16.79663 - 18.495346 -14.293832 7.89578 2.2714825 22.976387 - -4.875734 -3.0836344 -2.9999814 13.751918 6.448228 - -11.924197 2.171869 2.0423572 -6.173772 10.778437 - 25.77281 -4.9495463 14.57806 0.3044315 2.6132357 - -7.591999 -2.076944 9.025118 1.7834753 -3.1799617 - -4.9401326 23.465864 5.1685796 -9.018578 9.037825 - -4.4150195 6.859591 -12.274467 -0.88911164 5.186309 - -3.9988663 -13.638606 -9.925445 -0.06329413 -3.6709652 - -12.397416 -12.719869 -1.395601 2.1150916 5.7381287 - -4.4691963 -3.82819 -0.84233856 -1.1604277 -13.490127 - 8.731719 -20.778936 -11.495662 5.8033476 -4.752041 - 10.833007 -6.717991 4.504732 13.4244375 1.1306485 - 7.3435574 1.400918 14.704036 -9.501399 7.2315617 - -6.417456 1.3333273 11.872697 -0.30664724 8.8845 - 6.5569253 4.7948146 0.03662816 -8.704245 6.224871 - -3.2701402 -11.508579 ] + Audio embedding Result: + [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055 + 1.756596 5.167894 10.80636 -3.8226728 -5.6141334 + 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897 + -9.723131 0.6619743 -6.976803 10.213478 7.494748 + 2.9105635 3.8949256 3.7999806 7.1061673 16.905321 + -7.1493764 8.733103 3.4230042 -4.831653 -11.403367 + 11.232214 7.1274667 -4.2828417 2.452362 -5.130748 + -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683 + 0.7618269 1.1253023 -2.083836 4.725744 -8.782597 + -3.539873 3.814236 5.1420674 2.162061 4.096431 + -6.4162116 12.747448 1.9429878 -15.152943 6.417416 + 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939 + 11.567354 3.69788 11.258265 7.442363 9.183411 + 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783 + 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073 + -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116 + 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578 + -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651 + -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556 + -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704 + 3.272176 2.8382776 5.134597 -9.190781 -0.5657382 + -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576 + -0.31784213 9.493548 2.1144536 4.358092 -12.089823 + 8.451689 -7.925461 4.6242585 4.4289427 18.692003 + -2.6204622 -5.149185 -0.35821092 8.488551 4.981496 + -9.32683 -2.2544234 6.6417594 1.2119585 10.977129 + 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716 + -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796 + 0.66607 15.443222 4.740594 -3.4725387 11.592567 + -2.054497 1.7361217 -8.265324 -9.30447 5.4068313 + -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733 + -8.649895 -9.998958 -2.564841 -0.53999114 2.601808 + -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085 + 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456 + 7.3629923 0.4657332 3.132599 12.438889 -1.8337058 + 4.532936 2.7264361 10.145339 -6.521951 2.897153 + -3.3925855 5.079156 7.759716 4.677565 5.8457737 + 2.402413 7.7071047 3.9711342 -6.390043 6.1268735 + -3.7760346 -11.118123 ] + # get the test embedding + Test embedding Result: + [ -1.902964 2.0690894 -8.034194 3.5472693 0.18089125 + 6.9085927 1.4097427 -1.9487704 -10.021278 -0.20755845 + -8.04332 4.344489 2.3200977 -14.306299 5.184692 + -11.55602 -3.8497238 0.6444722 1.2833948 2.6766639 + 0.5878921 0.7946299 1.7207596 2.5791872 14.998469 + -1.3385371 15.031221 -0.8006958 1.99287 -9.52007 + 2.435466 4.003221 -4.33817 -4.898601 -5.304714 + -18.033886 10.790787 -12.784645 -5.641755 2.9761686 + -10.566622 1.4839455 6.152458 -5.7195854 2.8603241 + 6.112133 8.489869 5.5958056 1.2836679 -1.2293907 + 0.89927405 7.0288725 -2.854029 -0.9782962 5.8255906 + 14.905906 -5.025907 0.7866458 -4.2444224 -16.354029 + 10.521315 0.9604709 -3.3257897 7.144871 -13.592733 + -8.568869 -1.7953678 0.26313916 10.916714 -6.9374123 + 1.857403 -6.2746415 2.8154466 -7.2338667 -2.293357 + -0.05452765 5.4287076 5.0849075 -6.690375 -1.6183422 + 3.654291 0.94352573 -9.200294 -5.4749465 -3.5235846 + 1.3420814 4.240421 -2.772944 -2.8451524 16.311104 + 4.2969875 -1.762936 -12.5758915 8.595198 -0.8835239 + -1.5708797 1.568961 1.1413603 3.5032008 -0.45251232 + -6.786333 16.89443 5.3366146 -8.789056 0.6355629 + 3.2579517 -3.328322 7.5969577 0.66025066 -6.550468 + -9.148656 2.020372 -0.4615173 1.1965656 -3.8764873 + 11.6562195 -6.0750933 12.182899 3.2218833 0.81969476 + 5.570001 -3.8459578 -7.205299 7.9262037 -7.6611166 + -5.249467 -2.2671914 7.2658715 -13.298164 4.821147 + -2.7263982 11.691089 -3.8918593 -2.838112 -1.0336838 + -3.8034165 2.8536487 -5.60398 -1.1972581 1.3455094 + -3.4903061 2.2408795 5.5010734 -3.970756 11.99696 + -7.8858757 0.43160373 -5.5059714 4.3426995 16.322706 + 11.635366 0.72157705 -9.245714 -3.91465 -4.449838 + -1.5716927 7.713747 -2.2430465 -6.198303 -13.481864 + 2.8156567 -5.7812386 5.1456156 2.7289324 -14.505571 + 13.270688 3.448231 -7.0659585 4.5886116 -4.466099 + -0.296428 -11.463529 -2.6076477 14.110243 -6.9725137 + -1.9962958 2.7119343 19.391657 0.01961198 14.607133 + -1.6695905 -4.391516 1.3131028 -6.670972 -5.888604 + 12.0612335 5.9285784 3.3715196 1.492534 10.723728 + -0.95514804 -12.085431 ] + # get the score between enroll and test + Eembeddings Score: 0.4292638301849365 ``` ### 4.预训练模型 diff --git a/demos/speaker_verification/run.sh b/demos/speaker_verification/run.sh index 856886d33..6140f7f38 100644 --- a/demos/speaker_verification/run.sh +++ b/demos/speaker_verification/run.sh @@ -1,6 +1,9 @@ #!/bin/bash wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav +wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.wav -# asr -paddlespeech vector --task spk --input ./85236145389.wav \ No newline at end of file +# vector +paddlespeech vector --task spk --input ./85236145389.wav + +paddlespeech vector --task score --input "./85236145389.wav ./123456789.wav" diff --git a/demos/speech_recognition/run.sh b/demos/speech_recognition/run.sh old mode 100755 new mode 100644 index 064669286..19ce0ebb3 --- a/demos/speech_recognition/run.sh +++ b/demos/speech_recognition/run.sh @@ -7,4 +7,4 @@ paddlespeech asr --input ./zh.wav # asr + punc -paddlespeech asr --input ./zh.wav | paddlespeech text --task punc \ No newline at end of file +paddlespeech asr --input ./zh.wav | paddlespeech text --task punc diff --git a/demos/speech_server/README_cn.md b/demos/speech_server/README_cn.md index 687b51f10..17a01f0bb 100644 --- a/demos/speech_server/README_cn.md +++ b/demos/speech_server/README_cn.md @@ -85,6 +85,10 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee - 命令行 (推荐使用) ``` paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav + + # 流式ASR + paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8091 --input ./zh.wav + ``` 使用帮助: @@ -191,7 +195,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee ``` - ### 5. CLS 客户端使用方法 + ### 6. CLS 客户端使用方法 **注意：** 初次使用客户端时响应时间会略长 - 命令行 (推荐使用) ``` diff --git a/docs/source/released_model.md b/docs/source/released_model.md index 9a423e03e..a7d00f241 100644 --- a/docs/source/released_model.md +++ b/docs/source/released_model.md @@ -6,7 +6,7 @@ ### Speech Recognition Model Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech | Example Link :-------------:| :------------:| :-----: | -----: | :-----: |:-----:| :-----: | :-----: | :-----: -[Ds2 Online Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.1.1.model.tar.gz) | Aishell Dataset | Char-based | 345 MB | 2 Conv + 5 LSTM layers with only forward direction | 0.080 |-| 151 h | [D2 Online Aishell ASR0](../../examples/aishell/asr0) +[Ds2 Online Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz) | Aishell Dataset | Char-based | 345 MB | 2 Conv + 5 LSTM layers with only forward direction | 0.078 |-| 151 h | [D2 Online Aishell ASR0](../../examples/aishell/asr0) [Ds2 Offline Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz)| Aishell Dataset | Char-based | 306 MB | 2 Conv + 3 bidirectional GRU layers| 0.064 |-| 151 h | [Ds2 Offline Aishell ASR0](../../examples/aishell/asr0) [Conformer Online Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_chunk_conformer_aishell_ckpt_0.1.2.model.tar.gz) | Aishell Dataset | Char-based | 189 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0565 |-| 151 h | [Conformer Online Aishell ASR1](../../examples/aishell/asr1) [Conformer Offline Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_0.1.2.model.tar.gz) | Aishell Dataset | Char-based | 189 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0483 |-| 151 h | [Conformer Offline Aishell ASR1](../../examples/aishell/asr1) @@ -37,8 +37,8 @@ Model Type | Dataset| Example Link | Pretrained Models|Static Models|Size (stati Tacotron2|LJSpeech|[tacotron2-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts0)|[tacotron2_ljspeech_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.2.0.zip)||| Tacotron2|CSMSC|[tacotron2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts0)|[tacotron2_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip)|[tacotron2_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_static_0.2.0.zip)|103MB| TransformerTTS| LJSpeech| [transformer-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts1)|[transformer_tts_ljspeech_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/transformer_tts/transformer_tts_ljspeech_ckpt_0.4.zip)||| -SpeedySpeech| CSMSC | [speedyspeech-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts2) |[speedyspeech_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_ckpt_0.5.zip)|[speedyspeech_nosil_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip)|12MB| -FastSpeech2| CSMSC |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts3)|[fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip)|[fastspeech2_nosil_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip)|157MB| +SpeedySpeech| CSMSC | [speedyspeech-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts2)|[speedyspeech_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip)|[speedyspeech_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_static_0.2.0.zip)|12MB| +FastSpeech2| CSMSC |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts3)|[fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip)|[fastspeech2_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_static_0.2.0.zip)|157MB| FastSpeech2-Conformer| CSMSC |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts3)|[fastspeech2_conformer_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_conformer_baker_ckpt_0.5.zip)||| FastSpeech2| AISHELL-3 |[fastspeech2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3)|[fastspeech2_nosil_aishell3_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip)||| FastSpeech2| LJSpeech |[fastspeech2-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts3)|[fastspeech2_nosil_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip)||| @@ -80,7 +80,7 @@ PANN | ESC-50 |[pann-esc50](../../examples/esc50/cls0)|[esc50_cnn6.tar.gz](https Model Type | Dataset| Example Link | Pretrained Models | Static Models :-------------:| :------------:| :-----: | :-----: | :-----: -PANN | VoxCeleb| [voxceleb_ecapatdnn](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/voxceleb/sv0) | [ecapatdnn.tar.gz](https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz) | - +PANN | VoxCeleb| [voxceleb_ecapatdnn](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/voxceleb/sv0) | [ecapatdnn.tar.gz](https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_0.tar.gz) | - ## Punctuation Restoration Models Model Type | Dataset| Example Link | Pretrained Models diff --git a/examples/aishell/asr0/README.md b/examples/aishell/asr0/README.md index bb45d8df0..4459b1382 100644 --- a/examples/aishell/asr0/README.md +++ b/examples/aishell/asr0/README.md @@ -151,21 +151,14 @@ avg.sh best exp/deepspeech2/checkpoints 1 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 ``` ## Pretrained Model -You can get the pretrained transformer or conformer using the scripts below: -```bash -Deepspeech2 offline: -wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz - -Deepspeech2 online: -wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/aishell_ds2_online_cer8.00_release.tar.gz +You can get the pretrained models from [this](../../../docs/source/released_model.md). -``` using the `tar` scripts to unpack the model and then you can use the script to test the model. For example: ``` -wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz -tar xzvf ds2.model.tar.gz +wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz +tar xzvf asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz source path.sh # If you have process the data and get the manifest file， you can skip the following 2 steps bash local/data.sh --stage -1 --stop_stage -1 @@ -173,12 +166,7 @@ bash local/data.sh --stage 2 --stop_stage 2 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 ``` -The performance of the released models are shown below: - -| Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech | -| :----------------------------: | :-------------: | :---------: | -----: | :------------------------------------------------- | :---- | :--- | :-------------- | -| Ds2 Online Aishell ASR0 Model | Aishell Dataset | Char-based | 345 MB | 2 Conv + 5 LSTM layers with only forward direction | 0.080 | - | 151 h | -| Ds2 Offline Aishell ASR0 Model | Aishell Dataset | Char-based | 306 MB | 2 Conv + 3 bidirectional GRU layers | 0.064 | - | 151 h | +The performance of the released models are shown in [this](./RESULTS.md) ## Stage 4: Static graph model Export This stage is to transform dygraph to static graph. ```bash @@ -214,8 +202,8 @@ if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then ``` you can train the model by yourself, or you can download the pretrained model by the script below: ```bash -wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz -tar xzvf ds2.model.tar.gz +wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz +tar xzvf asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz ``` You can download the audio demo: ```bash diff --git a/examples/aishell/asr0/RESULTS.md b/examples/aishell/asr0/RESULTS.md index 5841a8522..8af3d66d1 100644 --- a/examples/aishell/asr0/RESULTS.md +++ b/examples/aishell/asr0/RESULTS.md @@ -4,15 +4,16 @@ | Model | Number of Params | Release | Config | Test set | Valid Loss | CER | | --- | --- | --- | --- | --- | --- | --- | -| DeepSpeech2 | 45.18M | 2.2.0 | conf/deepspeech2_online.yaml + spec aug | test | 7.994938373565674 | 0.080 | +| DeepSpeech2 | 45.18M | r0.2.0 | conf/deepspeech2_online.yaml + spec aug | test | 7.708217620849609| 0.078 | +| DeepSpeech2 | 45.18M | v2.2.0 | conf/deepspeech2_online.yaml + spec aug | test | 7.994938373565674 | 0.080 | ## Deepspeech2 Non-Streaming | Model | Number of Params | Release | Config | Test set | Valid Loss | CER | | --- | --- | --- | --- | --- | --- | --- | -| DeepSpeech2 | 58.4M | 2.2.0 | conf/deepspeech2.yaml + spec aug | test | 5.738585948944092 | 0.064000 | -| DeepSpeech2 | 58.4M | 2.1.0 | conf/deepspeech2.yaml + spec aug | test | 7.483316898345947 | 0.077860 | -| DeepSpeech2 | 58.4M | 2.1.0 | conf/deepspeech2.yaml | test | 7.299022197723389 | 0.078671 | -| DeepSpeech2 | 58.4M | 2.0.0 | conf/deepspeech2.yaml | test | - | 0.078977 | +| DeepSpeech2 | 58.4M | v2.2.0 | conf/deepspeech2.yaml + spec aug | test | 5.738585948944092 | 0.064000 | +| DeepSpeech2 | 58.4M | v2.1.0 | conf/deepspeech2.yaml + spec aug | test | 7.483316898345947 | 0.077860 | +| DeepSpeech2 | 58.4M | v2.1.0 | conf/deepspeech2.yaml | test | 7.299022197723389 | 0.078671 | +| DeepSpeech2 | 58.4M | v2.0.0 | conf/deepspeech2.yaml | test | - | 0.078977 | | --- | --- | --- | --- | --- | --- | --- | -| DeepSpeech2 | 58.4M | 1.8.5 | - | test | - | 0.080447 | +| DeepSpeech2 | 58.4M | v1.8.5 | - | test | - | 0.080447 | diff --git a/examples/aishell/asr0/run.sh b/examples/aishell/asr0/run.sh index 15685f21f..114af5a97 100755 --- a/examples/aishell/asr0/run.sh +++ b/examples/aishell/asr0/run.sh @@ -5,7 +5,7 @@ source path.sh gpus=0,1,2,3 stage=0 stop_stage=100 -conf_path=conf/deepspeech2.yaml #conf/deepspeech2.yaml or conf/deepspeeech2_online.yaml +conf_path=conf/deepspeech2.yaml #conf/deepspeech2.yaml or conf/deepspeech2_online.yaml decode_conf_path=conf/tuning/decode.yaml avg_num=1 model_type=offline # offline or online diff --git a/examples/aishell/asr1/README.md b/examples/aishell/asr1/README.md index 5277a31eb..25b28ede8 100644 --- a/examples/aishell/asr1/README.md +++ b/examples/aishell/asr1/README.md @@ -143,25 +143,14 @@ avg.sh best exp/conformer/checkpoints 20 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 ``` ## Pretrained Model -You can get the pretrained transformer or conformer using the scripts below: +You can get the pretrained transformer or conformer from [this](../../../docs/source/released_model.md) -```bash -# Conformer: -wget https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz - -# Chunk Conformer: -wget https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.chunk.release.tar.gz - -# Transformer: -wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz - -``` using the `tar` scripts to unpack the model and then you can use the script to test the model. For example: ``` -wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz -tar xzvf transformer.model.tar.gz +wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz +tar xzvf asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz source path.sh # If you have process the data and get the manifest file， you can skip the following 2 steps bash local/data.sh --stage -1 --stop_stage -1 @@ -206,7 +195,7 @@ In some situations, you want to use the trained model to do the inference for th ``` you can train the model by yourself using ```bash run.sh --stage 0 --stop_stage 3```, or you can download the pretrained model through the script below: ```bash -wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz +wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz tar xzvf transformer.model.tar.gz ``` You can download the audio demo: diff --git a/examples/aishell3/vc0/README.md b/examples/aishell3/vc0/README.md index 664ec1ac3..925663ab1 100644 --- a/examples/aishell3/vc0/README.md +++ b/examples/aishell3/vc0/README.md @@ -118,7 +118,7 @@ CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_outpu ``` ## Pretrained Model -[tacotron2_aishell3_ckpt_vc0_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_aishell3_ckpt_vc0_0.2.0.zip) +- [tacotron2_aishell3_ckpt_vc0_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_aishell3_ckpt_vc0_0.2.0.zip) Model | Step | eval/loss | eval/l1_loss | eval/mse_loss | eval/bce_loss| eval/attn_loss diff --git a/examples/aishell3/vc1/README.md b/examples/aishell3/vc1/README.md index 04b83a5ff..8ab0f9c8c 100644 --- a/examples/aishell3/vc1/README.md +++ b/examples/aishell3/vc1/README.md @@ -119,7 +119,7 @@ ref_audio CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ge2e_params_path} ${ref_audio_dir} ``` ## Pretrained Model -[fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip) +- [fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip) Model | Step | eval/loss | eval/l1_loss | eval/duration_loss | eval/pitch_loss| eval/energy_loss :-------------:| :------------:| :-----: | :-----: | :--------: |:--------:|:---------: diff --git a/examples/aishell3/voc1/README.md b/examples/aishell3/voc1/README.md index dad464092..eb30e7c40 100644 --- a/examples/aishell3/voc1/README.md +++ b/examples/aishell3/voc1/README.md @@ -137,7 +137,8 @@ optional arguments: 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Models -Pretrained models can be downloaded here [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip). +Pretrained models can be downloaded here: +- [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip) Model | Step | eval/generator_loss | eval/log_stft_magnitude_loss:| eval/spectral_convergence_loss :-------------:| :------------:| :-----: | :-----: | :--------: diff --git a/examples/aishell3/voc5/README.md b/examples/aishell3/voc5/README.md index ebe2530be..c957c4a3a 100644 --- a/examples/aishell3/voc5/README.md +++ b/examples/aishell3/voc5/README.md @@ -136,7 +136,8 @@ optional arguments: 4. `--output-dir` is the directory to save the synthesized audio files. 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Models -The pretrained model can be downloaded here [hifigan_aishell3_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip). +The pretrained model can be downloaded here: +- [hifigan_aishell3_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip) Model | Step | eval/generator_loss | eval/mel_loss| eval/feature_matching_loss diff --git a/examples/ami/sd0/conf/ecapa_tdnn.yaml b/examples/ami/sd0/conf/ecapa_tdnn.yaml new file mode 100755 index 000000000..319e44976 --- /dev/null +++ b/examples/ami/sd0/conf/ecapa_tdnn.yaml @@ -0,0 +1,62 @@ +########################################################### +# AMI DATA PREPARE SETTING # +########################################################### +split_type: 'full_corpus_asr' +skip_TNO: True +# Options for mic_type: 'Mix-Lapel', 'Mix-Headset', 'Array1', 'Array1-01', 'BeamformIt' +mic_type: 'Mix-Headset' +vad_type: 'oracle' +max_subseg_dur: 3.0 +overlap: 1.5 +# Some more exp folders (for cleaner structure). +embedding_dir: emb #!ref /emb +meta_data_dir: metadata #!ref /metadata +ref_rttm_dir: ref_rttms #!ref /ref_rttms +sys_rttm_dir: sys_rttms #!ref /sys_rttms +der_dir: DER #!ref /DER + + +########################################################### +# FEATURE EXTRACTION SETTING # +########################################################### +# currently, we only support fbank +sr: 16000 # sample rate +n_mels: 80 +window_size: 400 #25ms, sample rate 16000, 25 * 16000 / 1000 = 400 +hop_size: 160 #10ms, sample rate 16000, 10 * 16000 / 1000 = 160 +#left_frames: 0 +#right_frames: 0 +#deltas: False + + +########################################################### +# MODEL SETTING # +########################################################### +# currently, we only support ecapa-tdnn in the ecapa_tdnn.yaml +# if we want use another model, please choose another configuration yaml file +seed: 1234 +emb_dim: 192 +batch_size: 16 +model: + input_size: 80 + channels: [1024, 1024, 1024, 1024, 3072] + kernel_sizes: [5, 3, 3, 3, 1] + dilations: [1, 2, 3, 4, 1] + attention_channels: 128 + lin_neurons: 192 +# Will automatically download ECAPA-TDNN model (best). + +########################################################### +# SPECTRAL CLUSTERING SETTING # +########################################################### +backend: 'SC' # options: 'kmeans' # Note: kmeans goes only with cos affinity +affinity: 'cos' # options: cos, nn +max_num_spkrs: 10 +oracle_n_spkrs: True + + +########################################################### +# DER EVALUATION SETTING # +########################################################### +ignore_overlap: True +forgiveness_collar: 0.25 diff --git a/examples/ami/sd0/local/compute_embdding.py b/examples/ami/sd0/local/compute_embdding.py new file mode 100644 index 000000000..dc824d7ca --- /dev/null +++ b/examples/ami/sd0/local/compute_embdding.py @@ -0,0 +1,231 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import json +import os +import pickle +import sys + +import numpy as np +import paddle +from paddle.io import BatchSampler +from paddle.io import DataLoader +from tqdm.contrib import tqdm +from yacs.config import CfgNode + +from paddlespeech.s2t.utils.log import Log +from paddlespeech.vector.cluster.diarization import EmbeddingMeta +from paddlespeech.vector.io.batch import batch_feature_normalize +from paddlespeech.vector.io.dataset_from_json import JSONDataset +from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn +from paddlespeech.vector.modules.sid_model import SpeakerIdetification +from paddlespeech.vector.training.seeding import seed_everything + +# Logger setup +logger = Log(__name__).getlog() + + +def prepare_subset_json(full_meta_data, rec_id, out_meta_file): + """Prepares metadata for a given recording ID. + + Arguments + --------- + full_meta_data : json + Full meta (json) containing all the recordings + rec_id : str + The recording ID for which meta (json) has to be prepared + out_meta_file : str + Path of the output meta (json) file. + """ + + subset = {} + for key in full_meta_data: + k = str(key) + if k.startswith(rec_id): + subset[key] = full_meta_data[key] + + with open(out_meta_file, mode="w") as json_f: + json.dump(subset, json_f, indent=2) + + +def create_dataloader(json_file, batch_size): + """Creates the datasets and their data processing pipelines. + This is used for multi-mic processing. + """ + + # create datasets + dataset = JSONDataset( + json_file=json_file, + feat_type='melspectrogram', + n_mels=config.n_mels, + window_size=config.window_size, + hop_length=config.hop_size) + + # create dataloader + batch_sampler = BatchSampler(dataset, batch_size=batch_size, shuffle=True) + dataloader = DataLoader(dataset, + batch_sampler=batch_sampler, + collate_fn=lambda x: batch_feature_normalize( + x, mean_norm=True, std_norm=False), + return_list=True) + + return dataloader + + +def main(args, config): + # set the training device, cpu or gpu + paddle.set_device(args.device) + # set the random seed + seed_everything(config.seed) + + # stage1: build the dnn backbone model network + ecapa_tdnn = EcapaTdnn(**config.model) + + # stage2: build the speaker verification eval instance with backbone model + model = SpeakerIdetification(backbone=ecapa_tdnn, num_class=1) + + # stage3: load the pre-trained model + # we get the last model from the epoch and save_interval + args.load_checkpoint = os.path.abspath( + os.path.expanduser(args.load_checkpoint)) + + # load model checkpoint to sid model + state_dict = paddle.load( + os.path.join(args.load_checkpoint, 'model.pdparams')) + model.set_state_dict(state_dict) + logger.info(f'Checkpoint loaded from {args.load_checkpoint}') + + # set the model to eval mode + model.eval() + + # load meta data + meta_file = os.path.join( + args.data_dir, + config.meta_data_dir, + "ami_" + args.dataset + "." + config.mic_type + ".subsegs.json", ) + with open(meta_file, "r") as f: + full_meta = json.load(f) + + # get all the recording IDs in this dataset. + all_keys = full_meta.keys() + A = [word.rstrip().split("_")[0] for word in all_keys] + all_rec_ids = list(set(A[1:])) + all_rec_ids.sort() + split = "AMI_" + args.dataset + i = 1 + + msg = "Extra embdding for " + args.dataset + " set" + logger.info(msg) + + if len(all_rec_ids) <= 0: + msg = "No recording IDs found! Please check if meta_data json file is properly generated." + logger.error(msg) + sys.exit() + + # extra different recordings embdding in a dataset. + for rec_id in tqdm(all_rec_ids): + # This tag will be displayed in the log. + tag = ("[" + str(args.dataset) + ": " + str(i) + "/" + + str(len(all_rec_ids)) + "]") + i = i + 1 + + # log message. + msg = "Embdding %s : %s " % (tag, rec_id) + logger.debug(msg) + + # embedding directory. + if not os.path.exists( + os.path.join(args.data_dir, config.embedding_dir, split)): + os.makedirs( + os.path.join(args.data_dir, config.embedding_dir, split)) + + # file to store embeddings. + emb_file_name = rec_id + "." + config.mic_type + ".emb_stat.pkl" + diary_stat_emb_file = os.path.join(args.data_dir, config.embedding_dir, + split, emb_file_name) + + # prepare a metadata (json) for one recording. This is basically a subset of full_meta. + # lets keep this meta-info in embedding directory itself. + json_file_name = rec_id + "." + config.mic_type + ".json" + meta_per_rec_file = os.path.join(args.data_dir, config.embedding_dir, + split, json_file_name) + + # write subset (meta for one recording) json metadata. + prepare_subset_json(full_meta, rec_id, meta_per_rec_file) + + # prepare data loader. + diary_set_loader = create_dataloader(meta_per_rec_file, + config.batch_size) + + # extract embeddings (skip if already done). + if not os.path.isfile(diary_stat_emb_file): + logger.debug("Extracting deep embeddings") + embeddings = np.empty(shape=[0, config.emb_dim], dtype=np.float64) + segset = [] + + for batch_idx, batch in enumerate(tqdm(diary_set_loader)): + # extrac the audio embedding + ids, feats, lengths = batch['ids'], batch['feats'], batch[ + 'lengths'] + seg = [x for x in ids] + segset = segset + seg + emb = model.backbone(feats, lengths).squeeze( + -1).numpy() # (N, emb_size, 1) -> (N, emb_size) + embeddings = np.concatenate((embeddings, emb), axis=0) + + segset = np.array(segset, dtype="|O") + stat_obj = EmbeddingMeta( + segset=segset, + stats=embeddings, ) + logger.debug("Saving Embeddings...") + with open(diary_stat_emb_file, "wb") as output: + pickle.dump(stat_obj, output) + + else: + logger.debug("Skipping embedding extraction (as already present).") + + +# Begin experiment! +if __name__ == "__main__": + parser = argparse.ArgumentParser(__doc__) + parser.add_argument( + '--device', + default="gpu", + help="Select which device to perform diarization, defaults to gpu.") + parser.add_argument( + "--config", default=None, type=str, help="configuration file") + parser.add_argument( + "--data-dir", + default="../save/", + type=str, + help="processsed data directory") + parser.add_argument( + "--dataset", + choices=['dev', 'eval'], + default="dev", + type=str, + help="Select which dataset to extra embdding, defaults to dev") + parser.add_argument( + "--load-checkpoint", + type=str, + default='', + help="Directory to load model checkpoint to compute embeddings.") + args = parser.parse_args() + config = CfgNode(new_allowed=True) + if args.config: + config.merge_from_file(args.config) + + config.freeze() + + main(args, config) diff --git a/examples/ami/sd0/local/data.sh b/examples/ami/sd0/local/data.sh deleted file mode 100755 index 478ec432d..000000000 --- a/examples/ami/sd0/local/data.sh +++ /dev/null @@ -1,49 +0,0 @@ -#!/bin/bash - -stage=1 - -TARGET_DIR=${MAIN_ROOT}/dataset/ami -data_folder=${TARGET_DIR}/amicorpus #e.g., /path/to/amicorpus/ -manual_annot_folder=${TARGET_DIR}/ami_public_manual_1.6.2 #e.g., /path/to/ami_public_manual_1.6.2/ - -save_folder=${MAIN_ROOT}/examples/ami/sd0/data -ref_rttm_dir=${save_folder}/ref_rttms -meta_data_dir=${save_folder}/metadata - -set=L - -. ${MAIN_ROOT}/utils/parse_options.sh || exit 1; -set -u -set -o pipefail - -mkdir -p ${save_folder} - -if [ ${stage} -le 0 ]; then - # Download AMI corpus, You need around 10GB of free space to get whole data - # The signals are too large to package in this way, - # so you need to use the chooser to indicate which ones you wish to download - echo "Please follow https://groups.inf.ed.ac.uk/ami/download/ to download the data." - echo "Annotations: AMI manual annotations v1.6.2 " - echo "Signals: " - echo "1) Select one or more AMI meetings: the IDs please follow ./ami_split.py" - echo "2) Select media streams: Just select Headset mix" - exit 0; -fi - -if [ ${stage} -le 1 ]; then - echo "AMI Data preparation" - - python local/ami_prepare.py --data_folder ${data_folder} \ - --manual_annot_folder ${manual_annot_folder} \ - --save_folder ${save_folder} --ref_rttm_dir ${ref_rttm_dir} \ - --meta_data_dir ${meta_data_dir} - - if [ $? -ne 0 ]; then - echo "Prepare AMI failed. Please check log message." - exit 1 - fi - -fi - -echo "AMI data preparation done." -exit 0 diff --git a/examples/ami/sd0/local/experiment.py b/examples/ami/sd0/local/experiment.py new file mode 100755 index 000000000..298228376 --- /dev/null +++ b/examples/ami/sd0/local/experiment.py @@ -0,0 +1,428 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import glob +import json +import os +import pickle +import shutil +import sys + +import numpy as np +from tqdm.contrib import tqdm +from yacs.config import CfgNode + +from paddlespeech.s2t.utils.log import Log +from paddlespeech.vector.cluster import diarization as diar +from utils.DER import DER + +# Logger setup +logger = Log(__name__).getlog() + + +def diarize_dataset( + full_meta, + split_type, + n_lambdas, + pval, + save_dir, + config, + n_neighbors=10, ): + """This function diarizes all the recordings in a given dataset. It performs + computation of embedding and clusters them using spectral clustering (or other backends). + The output speaker boundary file is stored in the RTTM format. + """ + + # prepare `spkr_info` only once when Oracle num of speakers is selected. + # spkr_info is essential to obtain number of speakers from groundtruth. + if config.oracle_n_spkrs is True: + full_ref_rttm_file = os.path.join(save_dir, config.ref_rttm_dir, + "fullref_ami_" + split_type + ".rttm") + rttm = diar.read_rttm(full_ref_rttm_file) + + spkr_info = list( # noqa F841 + filter(lambda x: x.startswith("SPKR-INFO"), rttm)) + + # get all the recording IDs in this dataset. + all_keys = full_meta.keys() + A = [word.rstrip().split("_")[0] for word in all_keys] + all_rec_ids = list(set(A[1:])) + all_rec_ids.sort() + split = "AMI_" + split_type + i = 1 + + # adding tag for directory path. + type_of_num_spkr = "oracle" if config.oracle_n_spkrs else "est" + tag = (type_of_num_spkr + "_" + str(config.affinity) + "_" + config.backend) + + # make out rttm dir + out_rttm_dir = os.path.join(save_dir, config.sys_rttm_dir, config.mic_type, + split, tag) + if not os.path.exists(out_rttm_dir): + os.makedirs(out_rttm_dir) + + # diarizing different recordings in a dataset. + for rec_id in tqdm(all_rec_ids): + # this tag will be displayed in the log. + tag = ("[" + str(split_type) + ": " + str(i) + "/" + + str(len(all_rec_ids)) + "]") + i = i + 1 + + # log message. + msg = "Diarizing %s : %s " % (tag, rec_id) + logger.debug(msg) + + # load embeddings. + emb_file_name = rec_id + "." + config.mic_type + ".emb_stat.pkl" + diary_stat_emb_file = os.path.join(save_dir, config.embedding_dir, + split, emb_file_name) + if not os.path.isfile(diary_stat_emb_file): + msg = "Embdding file %s not found! Please check if embdding file is properly generated." % ( + diary_stat_emb_file) + logger.error(msg) + sys.exit() + with open(diary_stat_emb_file, "rb") as in_file: + diary_obj = pickle.load(in_file) + + out_rttm_file = out_rttm_dir + "/" + rec_id + ".rttm" + + # processing starts from here. + if config.oracle_n_spkrs is True: + # oracle num of speakers. + num_spkrs = diar.get_oracle_num_spkrs(rec_id, spkr_info) + else: + if config.affinity == "nn": + # num of speakers tunned on dev set (only for nn affinity). + num_spkrs = n_lambdas + else: + # num of speakers will be estimated using max eigen gap for cos based affinity. + # so adding None here. Will use this None later-on. + num_spkrs = None + + if config.backend == "kmeans": + diar.do_kmeans_clustering( + diary_obj, + out_rttm_file, + rec_id, + num_spkrs, + pval, ) + + if config.backend == "SC": + # go for Spectral Clustering (SC). + diar.do_spec_clustering( + diary_obj, + out_rttm_file, + rec_id, + num_spkrs, + pval, + config.affinity, + n_neighbors, ) + + # can used for AHC later. Likewise one can add different backends here. + if config.backend == "AHC": + # call AHC + threshold = pval # pval for AHC is nothing but threshold. + diar.do_AHC(diary_obj, out_rttm_file, rec_id, num_spkrs, threshold) + + # once all RTTM outputs are generated, concatenate individual RTTM files to obtain single RTTM file. + # this is not needed but just staying with the standards. + concate_rttm_file = out_rttm_dir + "/sys_output.rttm" + logger.debug("Concatenating individual RTTM files...") + with open(concate_rttm_file, "w") as cat_file: + for f in glob.glob(out_rttm_dir + "/*.rttm"): + if f == concate_rttm_file: + continue + with open(f, "r") as indi_rttm_file: + shutil.copyfileobj(indi_rttm_file, cat_file) + + msg = "The system generated RTTM file for %s set : %s" % ( + split_type, concate_rttm_file, ) + logger.debug(msg) + + return concate_rttm_file + + +def dev_pval_tuner(full_meta, save_dir, config): + """Tuning p_value for affinity matrix. + The p_value used so that only p% of the values in each row is retained. + """ + + DER_list = [] + prange = np.arange(0.002, 0.015, 0.001) + + n_lambdas = None # using it as flag later. + for p_v in prange: + # Process whole dataset for value of p_v. + concate_rttm_file = diarize_dataset(full_meta, "dev", n_lambdas, p_v, + save_dir, config) + + ref_rttm_file = os.path.join(save_dir, config.ref_rttm_dir, + "fullref_ami_dev.rttm") + sys_rttm_file = concate_rttm_file + [MS, FA, SER, DER_] = DER( + ref_rttm_file, + sys_rttm_file, + config.ignore_overlap, + config.forgiveness_collar, ) + + DER_list.append(DER_) + + if config.oracle_n_spkrs is True and config.backend == "kmeans": + # no need of p_val search. Note p_val is needed for SC for both oracle and est num of speakers. + # p_val is needed in oracle_n_spkr=False when using kmeans backend. + break + + # Take p_val that gave minmum DER on Dev dataset. + tuned_p_val = prange[DER_list.index(min(DER_list))] + + return tuned_p_val + + +def dev_ahc_threshold_tuner(full_meta, save_dir, config): + """Tuning threshold for affinity matrix. This function is called when AHC is used as backend. + """ + + DER_list = [] + prange = np.arange(0.0, 1.0, 0.1) + + n_lambdas = None # using it as flag later. + + # Note: p_val is threshold in case of AHC. + for p_v in prange: + # Process whole dataset for value of p_v. + concate_rttm_file = diarize_dataset(full_meta, "dev", n_lambdas, p_v, + save_dir, config) + + ref_rttm = os.path.join(save_dir, config.ref_rttm_dir, + "fullref_ami_dev.rttm") + sys_rttm = concate_rttm_file + [MS, FA, SER, DER_] = DER( + ref_rttm, + sys_rttm, + config.ignore_overlap, + config.forgiveness_collar, ) + + DER_list.append(DER_) + + if config.oracle_n_spkrs is True: + break # no need of threshold search. + + # Take p_val that gave minmum DER on Dev dataset. + tuned_p_val = prange[DER_list.index(min(DER_list))] + + return tuned_p_val + + +def dev_nn_tuner(full_meta, split_type, save_dir, config): + """Tuning n_neighbors on dev set. Assuming oracle num of speakers. + This is used when nn based affinity is selected. + """ + + DER_list = [] + pval = None + + # Now assumming oracle num of speakers. + n_lambdas = 4 + + for nn in range(5, 15): + + # Process whole dataset for value of n_lambdas. + concate_rttm_file = diarize_dataset(full_meta, "dev", n_lambdas, p_v, + save_dir, config, nn) + + ref_rttm = os.path.join(save_dir, config.ref_rttm_dir, + "fullref_ami_dev.rttm") + sys_rttm = concate_rttm_file + [MS, FA, SER, DER_] = DER( + ref_rttm, + sys_rttm, + config.ignore_overlap, + config.forgiveness_collar, ) + + DER_list.append([nn, DER_]) + + if config.oracle_n_spkrs is True and config.backend == "kmeans": + break + + DER_list.sort(key=lambda x: x[1]) + tunned_nn = DER_list[0] + + return tunned_nn[0] + + +def dev_tuner(full_meta, split_type, save_dir, config): + """Tuning n_components on dev set. Used for nn based affinity matrix. + Note: This is a very basic tunning for nn based affinity. + This is work in progress till we find a better way. + """ + + DER_list = [] + pval = None + for n_lambdas in range(1, config.max_num_spkrs + 1): + + # Process whole dataset for value of n_lambdas. + concate_rttm_file = diarize_dataset(full_meta, "dev", n_lambdas, p_v, + save_dir, config) + + ref_rttm = os.path.join(save_dir, config.ref_rttm_dir, + "fullref_ami_dev.rttm") + sys_rttm = concate_rttm_file + [MS, FA, SER, DER_] = DER( + ref_rttm, + sys_rttm, + config.ignore_overlap, + config.forgiveness_collar, ) + + DER_list.append(DER_) + + # Take n_lambdas with minmum DER. + tuned_n_lambdas = DER_list.index(min(DER_list)) + 1 + + return tuned_n_lambdas + + +def main(args, config): + # AMI Dev Set: Tune hyperparams on dev set. + # Read the embdding file for dev set generated during embdding compute + dev_meta_file = os.path.join( + args.data_dir, + config.meta_data_dir, + "ami_dev." + config.mic_type + ".subsegs.json", ) + with open(dev_meta_file, "r") as f: + meta_dev = json.load(f) + + full_meta = meta_dev + + # Processing starts from here + # Following few lines selects option for different backend and affinity matrices. Finds best values for hyperameters using dev set. + ref_rttm_file = os.path.join(args.data_dir, config.ref_rttm_dir, + "fullref_ami_dev.rttm") + best_nn = None + if config.affinity == "nn": + logger.info("Tuning for nn (Multiple iterations over AMI Dev set)") + best_nn = dev_nn_tuner(full_meta, args.data_dir, config) + + n_lambdas = None + best_pval = None + + if config.affinity == "cos" and (config.backend == "SC" or + config.backend == "kmeans"): + # oracle num_spkrs or not, doesn't matter for kmeans and SC backends + # cos: Tune for the best pval for SC /kmeans (for unknown num of spkrs) + logger.info( + "Tuning for p-value for SC (Multiple iterations over AMI Dev set)") + best_pval = dev_pval_tuner(full_meta, args.data_dir, config) + + elif config.backend == "AHC": + logger.info("Tuning for threshold-value for AHC") + best_threshold = dev_ahc_threshold_tuner(full_meta, args.data_dir, + config) + best_pval = best_threshold + else: + # NN for unknown num of speakers (can be used in future) + if config.oracle_n_spkrs is False: + # nn: Tune num of number of components (to be updated later) + logger.info( + "Tuning for number of eigen components for NN (Multiple iterations over AMI Dev set)" + ) + # dev_tuner used for tuning num of components in NN. Can be used in future. + n_lambdas = dev_tuner(full_meta, args.data_dir, config) + + # load 'dev' and 'eval' metadata files. + full_meta_dev = full_meta # current full_meta is for 'dev' + eval_meta_file = os.path.join( + args.data_dir, + config.meta_data_dir, + "ami_eval." + config.mic_type + ".subsegs.json", ) + with open(eval_meta_file, "r") as f: + full_meta_eval = json.load(f) + + # tag to be appended to final output DER files. Writing DER for individual files. + type_of_num_spkr = "oracle" if config.oracle_n_spkrs else "est" + tag = ( + type_of_num_spkr + "_" + str(config.affinity) + "." + config.mic_type) + + # perform final diarization on 'dev' and 'eval' with best hyperparams. + final_DERs = {} + out_der_dir = os.path.join(args.data_dir, config.der_dir) + if not os.path.exists(out_der_dir): + os.makedirs(out_der_dir) + + for split_type in ["dev", "eval"]: + if split_type == "dev": + full_meta = full_meta_dev + else: + full_meta = full_meta_eval + + # performing diarization. + msg = "Diarizing using best hyperparams: " + split_type + " set" + logger.info(msg) + out_boundaries = diarize_dataset( + full_meta, + split_type, + n_lambdas=n_lambdas, + pval=best_pval, + n_neighbors=best_nn, + save_dir=args.data_dir, + config=config) + + # computing DER. + msg = "Computing DERs for " + split_type + " set" + logger.info(msg) + ref_rttm = os.path.join(args.data_dir, config.ref_rttm_dir, + "fullref_ami_" + split_type + ".rttm") + sys_rttm = out_boundaries + [MS, FA, SER, DER_vals] = DER( + ref_rttm, + sys_rttm, + config.ignore_overlap, + config.forgiveness_collar, + individual_file_scores=True, ) + + # writing DER values to a file. Append tag. + der_file_name = split_type + "_DER_" + tag + out_der_file = os.path.join(out_der_dir, der_file_name) + msg = "Writing DER file to: " + out_der_file + logger.info(msg) + diar.write_ders_file(ref_rttm, DER_vals, out_der_file) + + msg = ("AMI " + split_type + " set DER = %s %%\n" % + (str(round(DER_vals[-1], 2)))) + logger.info(msg) + final_DERs[split_type] = round(DER_vals[-1], 2) + + # final print DERs + msg = ( + "Final Diarization Error Rate (%%) on AMI corpus: Dev = %s %% | Eval = %s %%\n" + % (str(final_DERs["dev"]), str(final_DERs["eval"]))) + logger.info(msg) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(__doc__) + parser.add_argument( + "--config", default=None, type=str, help="configuration file") + parser.add_argument( + "--data-dir", + default="../data/", + type=str, + help="processsed data directory") + args = parser.parse_args() + config = CfgNode(new_allowed=True) + if args.config: + config.merge_from_file(args.config) + + config.freeze() + + main(args, config) diff --git a/examples/ami/sd0/local/process.sh b/examples/ami/sd0/local/process.sh new file mode 100755 index 000000000..1dfd11b86 --- /dev/null +++ b/examples/ami/sd0/local/process.sh @@ -0,0 +1,49 @@ +#!/bin/bash + +stage=0 +set=L + +. ${MAIN_ROOT}/utils/parse_options.sh || exit 1; +set -o pipefail + +data_folder=$1 +manual_annot_folder=$2 +save_folder=$3 +pretrained_model_dir=$4 +conf_path=$5 +device=$6 + +ref_rttm_dir=${save_folder}/ref_rttms +meta_data_dir=${save_folder}/metadata + +if [ ${stage} -le 0 ]; then + echo "AMI Data preparation" + python local/ami_prepare.py --data_folder ${data_folder} \ + --manual_annot_folder ${manual_annot_folder} \ + --save_folder ${save_folder} --ref_rttm_dir ${ref_rttm_dir} \ + --meta_data_dir ${meta_data_dir} + + if [ $? -ne 0 ]; then + echo "Prepare AMI failed. Please check log message." + exit 1 + fi + echo "AMI data preparation done." +fi + +if [ ${stage} -le 1 ]; then + # extra embddings for dev and eval dataset + for name in dev eval; do + python local/compute_embdding.py --config ${conf_path} \ + --data-dir ${save_folder} \ + --device ${device} \ + --dataset ${name} \ + --load-checkpoint ${pretrained_model_dir} + done +fi + +if [ ${stage} -le 2 ]; then + # tune hyperparams on dev set + # perform final diarization on 'dev' and 'eval' with best hyperparams + python local/experiment.py --config ${conf_path} \ + --data-dir ${save_folder} +fi diff --git a/examples/ami/sd0/run.sh b/examples/ami/sd0/run.sh index 91d4b706a..9035f5955 100644 --- a/examples/ami/sd0/run.sh +++ b/examples/ami/sd0/run.sh @@ -1,14 +1,46 @@ #!/bin/bash -. path.sh || exit 1; +. ./path.sh || exit 1; set -e -stage=1 +stage=0 +#TARGET_DIR=${MAIN_ROOT}/dataset/ami +TARGET_DIR=/home/dataset/AMI +data_folder=${TARGET_DIR}/amicorpus #e.g., /path/to/amicorpus/ +manual_annot_folder=${TARGET_DIR}/ami_public_manual_1.6.2 #e.g., /path/to/ami_public_manual_1.6.2/ + +save_folder=./save +pretraind_model_dir=${save_folder}/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1/model +conf_path=conf/ecapa_tdnn.yaml +device=gpu . ${MAIN_ROOT}/utils/parse_options.sh || exit 1; -if [ ${stage} -le 1 ]; then - # prepare data - bash ./local/data.sh || exit -1 -fi \ No newline at end of file +if [ $stage -le 0 ]; then + # Prepare data + # Download AMI corpus, You need around 10GB of free space to get whole data + # The signals are too large to package in this way, + # so you need to use the chooser to indicate which ones you wish to download + echo "Please follow https://groups.inf.ed.ac.uk/ami/download/ to download the data." + echo "Annotations: AMI manual annotations v1.6.2 " + echo "Signals: " + echo "1) Select one or more AMI meetings: the IDs please follow ./ami_split.py" + echo "2) Select media streams: Just select Headset mix" +fi + +if [ $stage -le 1 ]; then + # Download the pretrained model + wget https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz + mkdir -p ${save_folder} && tar -xvf sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz -C ${save_folder} + rm -rf sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz + echo "download the pretrained ECAPA-TDNN Model to path: "${pretraind_model_dir} +fi + +if [ $stage -le 2 ]; then + # Tune hyperparams on dev set and perform final diarization on dev and eval with best hyperparams. + echo ${data_folder} ${manual_annot_folder} ${save_folder} ${pretraind_model_dir} ${conf_path} + bash ./local/process.sh ${data_folder} ${manual_annot_folder} \ + ${save_folder} ${pretraind_model_dir} ${conf_path} ${device} || exit 1 +fi + diff --git a/examples/csmsc/tts0/README.md b/examples/csmsc/tts0/README.md index 0129329ae..01376bd61 100644 --- a/examples/csmsc/tts0/README.md +++ b/examples/csmsc/tts0/README.md @@ -212,7 +212,8 @@ optional arguments: Pretrained Tacotron2 model with no silence in the edge of audios: - [tacotron2_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip) -The static model can be downloaded here [tacotron2_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_static_0.2.0.zip). +The static model can be downloaded here: +- [tacotron2_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_static_0.2.0.zip) Model | Step | eval/loss | eval/l1_loss | eval/mse_loss | eval/bce_loss| eval/attn_loss diff --git a/examples/csmsc/tts0/local/inference.sh b/examples/csmsc/tts0/local/inference.sh index e417d748e..d29604410 100755 --- a/examples/csmsc/tts0/local/inference.sh +++ b/examples/csmsc/tts0/local/inference.sh @@ -27,20 +27,8 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --phones_dict=dump/phone_id_map.txt fi -# style melgan -# style melgan's Dygraph to Static Graph is not ready now -if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then - python3 ${BIN_DIR}/../inference.py \ - --inference_dir=${train_output_path}/inference \ - --am=tacotron2_csmsc \ - --voc=style_melgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ - --output_dir=${train_output_path}/pd_infer_out \ - --phones_dict=dump/phone_id_map.txt -fi - # hifigan -if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then python3 ${BIN_DIR}/../inference.py \ --inference_dir=${train_output_path}/inference \ --am=tacotron2_csmsc \ diff --git a/examples/csmsc/tts2/README.md b/examples/csmsc/tts2/README.md index 5f31f7b36..081d85848 100644 --- a/examples/csmsc/tts2/README.md +++ b/examples/csmsc/tts2/README.md @@ -221,21 +221,30 @@ CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path} ``` ## Pretrained Model -Pretrained SpeedySpeech model with no silence in the edge of audios[speedyspeech_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_ckpt_0.5.zip). +Pretrained SpeedySpeech model with no silence in the edge of audios: +- [speedyspeech_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_ckpt_0.5.zip) +- [speedyspeech_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip) + +The static model can be downloaded here: +- [speedyspeech_nosil_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip) +- [speedyspeech_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_static_0.2.0.zip) + +The ONNX model can be downloaded here: +- [speedyspeech_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_onnx_0.2.0.zip) -The static model can be downloaded here [speedyspeech_nosil_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip). Model | Step | eval/loss | eval/l1_loss | eval/duration_loss | eval/ssim_loss :-------------:| :------------:| :-----: | :-----: | :--------:|:--------: -default| 1(gpu) x 11400|0.83655|0.42324|0.03211| 0.38119 +default| 1(gpu) x 11400|0.79532|0.400246|0.030259| 0.36482 SpeedySpeech checkpoint contains files listed below. + ```text -speedyspeech_nosil_baker_ckpt_0.5 +speedyspeech_csmsc_ckpt_0.2.0 ├── default.yaml # default config used to train speedyspeech ├── feats_stats.npy # statistics used to normalize spectrogram when training speedyspeech ├── phone_id_map.txt # phone vocabulary file when training speedyspeech -├── snapshot_iter_11400.pdz # model parameters and optimizer states +├── snapshot_iter_30600.pdz # model parameters and optimizer states └── tone_id_map.txt # tone vocabulary file when training speedyspeech ``` You can use the following scripts to synthesize for `${BIN_DIR}/../sentences.txt` using pretrained speedyspeech and parallel wavegan models. @@ -246,9 +255,9 @@ FLAGS_allocator_strategy=naive_best_fit \ FLAGS_fraction_of_gpu_memory_to_use=0.01 \ python3 ${BIN_DIR}/../synthesize_e2e.py \ --am=speedyspeech_csmsc \ - --am_config=speedyspeech_nosil_baker_ckpt_0.5/default.yaml \ - --am_ckpt=speedyspeech_nosil_baker_ckpt_0.5/snapshot_iter_11400.pdz \ - --am_stat=speedyspeech_nosil_baker_ckpt_0.5/feats_stats.npy \ + --am_config=speedyspeech_csmsc_ckpt_0.2.0/default.yaml \ + --am_ckpt=speedyspeech_csmsc_ckpt_0.2.0/snapshot_iter_30600.pdz \ + --am_stat=speedyspeech_csmsc_ckpt_0.2.0/feats_stats.npy \ --voc=pwgan_csmsc \ --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \ --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ @@ -257,6 +266,6 @@ python3 ${BIN_DIR}/../synthesize_e2e.py \ --text=${BIN_DIR}/../sentences.txt \ --output_dir=exp/default/test_e2e \ --inference_dir=exp/default/inference \ - --phones_dict=speedyspeech_nosil_baker_ckpt_0.5/phone_id_map.txt \ - --tones_dict=speedyspeech_nosil_baker_ckpt_0.5/tone_id_map.txt + --phones_dict=speedyspeech_csmsc_ckpt_0.2.0/phone_id_map.txt \ + --tones_dict=speedyspeech_csmsc_ckpt_0.2.0/tone_id_map.txt ``` diff --git a/examples/csmsc/tts2/local/inference.sh b/examples/csmsc/tts2/local/inference.sh index d78c3eb32..ed92136cd 100755 --- a/examples/csmsc/tts2/local/inference.sh +++ b/examples/csmsc/tts2/local/inference.sh @@ -30,21 +30,8 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --tones_dict=dump/tone_id_map.txt fi -# style melgan -# style melgan's Dygraph to Static Graph is not ready now -if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then - python3 ${BIN_DIR}/../inference.py \ - --inference_dir=${train_output_path}/inference \ - --am=speedyspeech_csmsc \ - --voc=style_melgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ - --output_dir=${train_output_path}/pd_infer_out \ - --phones_dict=dump/phone_id_map.txt \ - --tones_dict=dump/tone_id_map.txt -fi - # hifigan -if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then python3 ${BIN_DIR}/../inference.py \ --inference_dir=${train_output_path}/inference \ --am=speedyspeech_csmsc \ diff --git a/examples/csmsc/tts2/local/ort_predict.sh b/examples/csmsc/tts2/local/ort_predict.sh new file mode 100755 index 000000000..46b0409b8 --- /dev/null +++ b/examples/csmsc/tts2/local/ort_predict.sh @@ -0,0 +1,32 @@ +train_output_path=$1 + +stage=0 +stop_stage=0 + +# only support default_fastspeech2/speedyspeech + hifigan/mb_melgan now! + +# synthesize from metadata +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + python3 ${BIN_DIR}/../ort_predict.py \ + --inference_dir=${train_output_path}/inference_onnx \ + --am=speedyspeech_csmsc \ + --voc=hifigan_csmsc \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/onnx_infer_out \ + --device=cpu \ + --cpu_threads=2 +fi + +# e2e, synthesize from text +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + python3 ${BIN_DIR}/../ort_predict_e2e.py \ + --inference_dir=${train_output_path}/inference_onnx \ + --am=speedyspeech_csmsc \ + --voc=hifigan_csmsc \ + --output_dir=${train_output_path}/onnx_infer_out_e2e \ + --text=${BIN_DIR}/../csmsc_test.txt \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --device=cpu \ + --cpu_threads=2 +fi diff --git a/examples/csmsc/tts2/local/paddle2onnx.sh b/examples/csmsc/tts2/local/paddle2onnx.sh new file mode 120000 index 000000000..87c46634d --- /dev/null +++ b/examples/csmsc/tts2/local/paddle2onnx.sh @@ -0,0 +1 @@ +../../tts3/local/paddle2onnx.sh \ No newline at end of file diff --git a/examples/csmsc/tts2/run.sh b/examples/csmsc/tts2/run.sh index 8b8f53bd0..766aa882b 100755 --- a/examples/csmsc/tts2/run.sh +++ b/examples/csmsc/tts2/run.sh @@ -40,3 +40,25 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then # inference with static model CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path} || exit -1 fi + +# paddle2onnx, please make sure the static models are in ${train_output_path}/inference first +# we have only tested the following models so far +if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then + # install paddle2onnx + version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') + if [[ -z "$version" || ${version} != '0.9.4' ]]; then + pip install paddle2onnx==0.9.4 + fi + ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx speedyspeech_csmsc + ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx hifigan_csmsc +fi + +# inference with onnxruntime, use fastspeech2 + hifigan by default +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + # install onnxruntime + version=$(echo `pip list |grep "onnxruntime"` |awk -F" " '{print $2}') + if [[ -z "$version" || ${version} != '1.10.0' ]]; then + pip install onnxruntime==1.10.0 + fi + ./local/ort_predict.sh ${train_output_path} +fi diff --git a/examples/csmsc/tts3/README.md b/examples/csmsc/tts3/README.md index ae8f7af60..c734199b4 100644 --- a/examples/csmsc/tts3/README.md +++ b/examples/csmsc/tts3/README.md @@ -231,11 +231,19 @@ Pretrained FastSpeech2 model with no silence in the edge of audios: The static model can be downloaded here: - [fastspeech2_nosil_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip) - [fastspeech2_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_static_0.2.0.zip) +- [fastspeech2_cnndecoder_csmsc_static_1.0.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_static_1.0.0.zip) +- [fastspeech2_cnndecoder_csmsc_streaming_static_1.0.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_static_1.0.0.zip) + +The ONNX model can be downloaded here: +- [fastspeech2_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_onnx_0.2.0.zip) +- [fastspeech2_cnndecoder_csmsc_onnx_1.0.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_onnx_1.0.0.zip) +- [fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip) Model | Step | eval/loss | eval/l1_loss | eval/duration_loss | eval/pitch_loss| eval/energy_loss :-------------:| :------------:| :-----: | :-----: | :--------: |:--------:|:---------: default| 2(gpu) x 76000|1.0991|0.59132|0.035815|0.31915|0.15287| conformer| 2(gpu) x 76000|1.0675|0.56103|0.035869|0.31553|0.15509| +cnndecoder| 1(gpu) x 153000|1.1153|0.61475|0.03380|0.30414|0.14707| FastSpeech2 checkpoint contains files listed below. ```text diff --git a/examples/csmsc/tts3/local/inference.sh b/examples/csmsc/tts3/local/inference.sh index 9322cfd69..b43fd2861 100755 --- a/examples/csmsc/tts3/local/inference.sh +++ b/examples/csmsc/tts3/local/inference.sh @@ -5,6 +5,7 @@ train_output_path=$1 stage=0 stop_stage=0 +# pwgan if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then python3 ${BIN_DIR}/../inference.py \ --inference_dir=${train_output_path}/inference \ @@ -27,20 +28,8 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --phones_dict=dump/phone_id_map.txt fi -# style melgan -# style melgan's Dygraph to Static Graph is not ready now -if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then - python3 ${BIN_DIR}/../inference.py \ - --inference_dir=${train_output_path}/inference \ - --am=fastspeech2_csmsc \ - --voc=style_melgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ - --output_dir=${train_output_path}/pd_infer_out \ - --phones_dict=dump/phone_id_map.txt -fi - # hifigan -if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then python3 ${BIN_DIR}/../inference.py \ --inference_dir=${train_output_path}/inference \ --am=fastspeech2_csmsc \ @@ -51,7 +40,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then fi # wavernn -if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then python3 ${BIN_DIR}/../inference.py \ --inference_dir=${train_output_path}/inference \ --am=fastspeech2_csmsc \ diff --git a/examples/csmsc/tts3/local/inference_streaming.sh b/examples/csmsc/tts3/local/inference_streaming.sh new file mode 100755 index 000000000..719f46c62 --- /dev/null +++ b/examples/csmsc/tts3/local/inference_streaming.sh @@ -0,0 +1,47 @@ +#!/bin/bash + +train_output_path=$1 + +stage=0 +stop_stage=0 + +# pwgan +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + python3 ${BIN_DIR}/../inference_streaming.py \ + --inference_dir=${train_output_path}/inference_streaming \ + --am=fastspeech2_csmsc \ + --am_stat=dump/train/speech_stats.npy \ + --voc=pwgan_csmsc \ + --text=${BIN_DIR}/../sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out_streaming \ + --phones_dict=dump/phone_id_map.txt \ + --am_streaming=True +fi + +# for more GAN Vocoders +# multi band melgan +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + python3 ${BIN_DIR}/../inference_streaming.py \ + --inference_dir=${train_output_path}/inference_streaming \ + --am=fastspeech2_csmsc \ + --am_stat=dump/train/speech_stats.npy \ + --voc=mb_melgan_csmsc \ + --text=${BIN_DIR}/../sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out_streaming \ + --phones_dict=dump/phone_id_map.txt \ + --am_streaming=True +fi + +# hifigan +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + python3 ${BIN_DIR}/../inference_streaming.py \ + --inference_dir=${train_output_path}/inference_streaming \ + --am=fastspeech2_csmsc \ + --am_stat=dump/train/speech_stats.npy \ + --voc=hifigan_csmsc \ + --text=${BIN_DIR}/../sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out_streaming \ + --phones_dict=dump/phone_id_map.txt \ + --am_streaming=True +fi + diff --git a/examples/csmsc/tts3/local/ort_predict.sh b/examples/csmsc/tts3/local/ort_predict.sh new file mode 100755 index 000000000..96350c06c --- /dev/null +++ b/examples/csmsc/tts3/local/ort_predict.sh @@ -0,0 +1,31 @@ +train_output_path=$1 + +stage=0 +stop_stage=0 + +# only support default_fastspeech2/speedyspeech + hifigan/mb_melgan now! + +# synthesize from metadata +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + python3 ${BIN_DIR}/../ort_predict.py \ + --inference_dir=${train_output_path}/inference_onnx \ + --am=fastspeech2_csmsc \ + --voc=hifigan_csmsc \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/onnx_infer_out \ + --device=cpu \ + --cpu_threads=2 +fi + +# e2e, synthesize from text +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + python3 ${BIN_DIR}/../ort_predict_e2e.py \ + --inference_dir=${train_output_path}/inference_onnx \ + --am=fastspeech2_csmsc \ + --voc=hifigan_csmsc \ + --output_dir=${train_output_path}/onnx_infer_out_e2e \ + --text=${BIN_DIR}/../csmsc_test.txt \ + --phones_dict=dump/phone_id_map.txt \ + --device=cpu \ + --cpu_threads=2 +fi diff --git a/examples/csmsc/tts3/local/ort_predict_streaming.sh b/examples/csmsc/tts3/local/ort_predict_streaming.sh new file mode 100755 index 000000000..502ec912a --- /dev/null +++ b/examples/csmsc/tts3/local/ort_predict_streaming.sh @@ -0,0 +1,19 @@ +train_output_path=$1 + +stage=0 +stop_stage=0 + +# e2e, synthesize from text +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + python3 ${BIN_DIR}/../ort_predict_streaming.py \ + --inference_dir=${train_output_path}/inference_onnx_streaming \ + --am=fastspeech2_csmsc \ + --am_stat=dump/train/speech_stats.npy \ + --voc=hifigan_csmsc \ + --output_dir=${train_output_path}/onnx_infer_out_streaming \ + --text=${BIN_DIR}/../csmsc_test.txt \ + --phones_dict=dump/phone_id_map.txt \ + --device=cpu \ + --cpu_threads=2 \ + --am_streaming=True +fi diff --git a/examples/csmsc/tts3/local/paddle2onnx.sh b/examples/csmsc/tts3/local/paddle2onnx.sh new file mode 100755 index 000000000..0b05a6d6f --- /dev/null +++ b/examples/csmsc/tts3/local/paddle2onnx.sh @@ -0,0 +1,23 @@ +train_output_path=$1 +model_dir=$2 +output_dir=$3 +model=$4 + +enable_dev_version=True + +model_name=${model%_*} +echo model_name: ${model_name} + +if [ ${model_name} = 'mb_melgan' ] ;then + enable_dev_version=False +fi + +mkdir -p ${train_output_path}/${output_dir} + +paddle2onnx \ + --model_dir ${train_output_path}/${model_dir} \ + --model_filename ${model}.pdmodel \ + --params_filename ${model}.pdiparams \ + --save_file ${train_output_path}/${output_dir}/${model}.onnx \ + --opset_version 11 \ + --enable_dev_version ${enable_dev_version} \ No newline at end of file diff --git a/examples/csmsc/tts3/local/synthesize_e2e.sh b/examples/csmsc/tts3/local/synthesize_e2e.sh index 512e062b7..8130eff14 100755 --- a/examples/csmsc/tts3/local/synthesize_e2e.sh +++ b/examples/csmsc/tts3/local/synthesize_e2e.sh @@ -109,6 +109,6 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then --lang=zh \ --text=${BIN_DIR}/../sentences.txt \ --output_dir=${train_output_path}/test_e2e \ - --phones_dict=dump/phone_id_map.txt \ - --inference_dir=${train_output_path}/inference + --phones_dict=dump/phone_id_map.txt #\ + # --inference_dir=${train_output_path}/inference fi diff --git a/examples/csmsc/tts3/local/synthesize_streaming.sh b/examples/csmsc/tts3/local/synthesize_streaming.sh index 7606c2385..b135db76d 100755 --- a/examples/csmsc/tts3/local/synthesize_streaming.sh +++ b/examples/csmsc/tts3/local/synthesize_streaming.sh @@ -88,5 +88,6 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then --text=${BIN_DIR}/../sentences.txt \ --output_dir=${train_output_path}/test_e2e_streaming \ --phones_dict=dump/phone_id_map.txt \ - --am_streaming=True + --am_streaming=True \ + --inference_dir=${train_output_path}/inference_streaming fi diff --git a/examples/csmsc/tts3/run.sh b/examples/csmsc/tts3/run.sh index e1a149b65..b617d5352 100755 --- a/examples/csmsc/tts3/run.sh +++ b/examples/csmsc/tts3/run.sh @@ -41,3 +41,25 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path} || exit -1 fi +# paddle2onnx, please make sure the static models are in ${train_output_path}/inference first +# we have only tested the following models so far +if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then + # install paddle2onnx + version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') + if [[ -z "$version" || ${version} != '0.9.4' ]]; then + pip install paddle2onnx==0.9.4 + fi + ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc + ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx hifigan_csmsc + ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx mb_melgan_csmsc +fi + +# inference with onnxruntime, use fastspeech2 + hifigan by default +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + # install onnxruntime + version=$(echo `pip list |grep "onnxruntime"` |awk -F" " '{print $2}') + if [[ -z "$version" || ${version} != '1.10.0' ]]; then + pip install onnxruntime==1.10.0 + fi + ./local/ort_predict.sh ${train_output_path} +fi diff --git a/examples/csmsc/tts3/run_cnndecoder.sh b/examples/csmsc/tts3/run_cnndecoder.sh index 5cccef016..61cd02a93 100755 --- a/examples/csmsc/tts3/run_cnndecoder.sh +++ b/examples/csmsc/tts3/run_cnndecoder.sh @@ -31,18 +31,75 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1 fi +# synthesize_e2e non-streaming if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then # synthesize_e2e, vocoder is pwgan CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1 fi +# inference non-streaming if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then # inference with static model CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path} || exit -1 fi +# synthesize_e2e streaming if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then # synthesize_e2e, vocoder is pwgan CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_streaming.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1 fi +# inference streaming +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + # inference with static model + CUDA_VISIBLE_DEVICES=${gpus} ./local/inference_streaming.sh ${train_output_path} || exit -1 +fi + +# paddle2onnx non streaming +if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then + # install paddle2onnx + version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') + if [[ -z "$version" || ${version} != '0.9.4' ]]; then + pip install paddle2onnx==0.9.4 + fi + ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc + ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx hifigan_csmsc +fi + + +# onnxruntime non streaming +# inference with onnxruntime, use fastspeech2 + hifigan by default +if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then + # install onnxruntime + version=$(echo `pip list |grep "onnxruntime"` |awk -F" " '{print $2}') + if [[ -z "$version" || ${version} != '1.10.0' ]]; then + pip install onnxruntime==1.10.0 + fi + ./local/ort_predict.sh ${train_output_path} +fi + +# paddle2onnx streaming +if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then + # install paddle2onnx + version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') + if [[ -z "$version" || ${version} != '0.9.4' ]]; then + pip install paddle2onnx==0.9.4 + fi + # streaming acoustic model + ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_encoder_infer + ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_decoder + ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_postnet + # vocoder + ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming hifigan_csmsc +fi + +# onnxruntime streaming +if [ ${stage} -le 10 ] && [ ${stop_stage} -ge 10 ]; then + # install onnxruntime + version=$(echo `pip list |grep "onnxruntime"` |awk -F" " '{print $2}') + if [[ -z "$version" || ${version} != '1.10.0' ]]; then + pip install onnxruntime==1.10.0 + fi + ./local/ort_predict_streaming.sh ${train_output_path} +fi + diff --git a/examples/csmsc/voc1/README.md b/examples/csmsc/voc1/README.md index 5527e8088..77da5b185 100644 --- a/examples/csmsc/voc1/README.md +++ b/examples/csmsc/voc1/README.md @@ -127,9 +127,14 @@ optional arguments: 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Models -The pretrained model can be downloaded here [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip). +The pretrained model can be downloaded here: +- [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip) -The static model can be downloaded here [pwg_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip). +The static model can be downloaded here: +- [pwg_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip) + +The ONNX model can be downloaded here: +- [pwgan_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_csmsc_onnx_0.2.0.zip) Model | Step | eval/generator_loss | eval/log_stft_magnitude_loss| eval/spectral_convergence_loss :-------------:| :------------:| :-----: | :-----: | :--------: diff --git a/examples/csmsc/voc3/README.md b/examples/csmsc/voc3/README.md index 22104a8f2..12adaf7f4 100644 --- a/examples/csmsc/voc3/README.md +++ b/examples/csmsc/voc3/README.md @@ -152,11 +152,17 @@ TODO: The hyperparameter of `finetune.yaml` is not good enough, a smaller `learning_rate` should be used (more `milestones` should be set). ## Pretrained Models -The pretrained model can be downloaded here [mb_melgan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip). +The pretrained model can be downloaded here: +- [mb_melgan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip) -The finetuned model can be downloaded here [mb_melgan_baker_finetune_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_finetune_ckpt_0.5.zip). +The finetuned model can be downloaded here: +- [mb_melgan_baker_finetune_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_finetune_ckpt_0.5.zip) -The static model can be downloaded here [mb_melgan_csmsc_static_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_static_0.1.1.zip) +The static model can be downloaded here: +- [mb_melgan_csmsc_static_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_static_0.1.1.zip) + +The ONNX model can be downloaded here: +- [mb_melgan_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip) Model | Step | eval/generator_loss | eval/log_stft_magnitude_loss|eval/spectral_convergence_loss |eval/sub_log_stft_magnitude_loss|eval/sub_spectral_convergence_loss :-------------:| :------------:| :-----: | :-----: | :--------:| :--------:| :--------: diff --git a/examples/csmsc/voc4/README.md b/examples/csmsc/voc4/README.md index b5c687391..b7add3e57 100644 --- a/examples/csmsc/voc4/README.md +++ b/examples/csmsc/voc4/README.md @@ -112,7 +112,8 @@ optional arguments: 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Models -The pretrained model can be downloaded here [style_melgan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip). +The pretrained model can be downloaded here: +- [style_melgan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip) The static model of Style MelGAN is not available now. diff --git a/examples/csmsc/voc5/README.md b/examples/csmsc/voc5/README.md index 21afe6eef..33e676165 100644 --- a/examples/csmsc/voc5/README.md +++ b/examples/csmsc/voc5/README.md @@ -112,9 +112,14 @@ optional arguments: 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Models -The pretrained model can be downloaded here [hifigan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip). +The pretrained model can be downloaded here: +- [hifigan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip) -The static model can be downloaded here [hifigan_csmsc_static_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_static_0.1.1.zip). +The static model can be downloaded here: +- [hifigan_csmsc_static_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_static_0.1.1.zip) + +The ONNX model can be downloaded here: +- [hifigan_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_onnx_0.2.0.zip) Model | Step | eval/generator_loss | eval/mel_loss| eval/feature_matching_loss :-------------:| :------------:| :-----: | :-----: | :--------: diff --git a/examples/csmsc/voc6/README.md b/examples/csmsc/voc6/README.md index 7763b3551..26d4523d9 100644 --- a/examples/csmsc/voc6/README.md +++ b/examples/csmsc/voc6/README.md @@ -109,9 +109,11 @@ optional arguments: 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Models -The pretrained model can be downloaded here [wavernn_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip). +The pretrained model can be downloaded here: +- [wavernn_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip) -The static model can be downloaded here [wavernn_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_static_0.2.0.zip). +The static model can be downloaded here: +- [wavernn_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_static_0.2.0.zip) Model | Step | eval/loss :-------------:|:------------:| :------------: diff --git a/examples/esc50/README.md b/examples/esc50/README.md index 911a72ad7..9eab95d26 100644 --- a/examples/esc50/README.md +++ b/examples/esc50/README.md @@ -4,7 +4,7 @@ 对于声音分类任务，传统机器学习的一个常用做法是首先人工提取音频的时域和频域的多种特征并做特征选择、组合、变换等，然后基于SVM或决策树进行分类。而端到端的深度学习则通常利用深度网络如RNN，CNN等直接对声间波形(waveform)或时频特征(time-frequency)进行特征学习(representation learning)和分类预测。 -在IEEE ICASSP 2017 大会上，谷歌开放了一个大规模的音频数据集[Audioset](https://research.google.com/audioset/)。该数据集包含了 632 类的音频类别以及 2,084,320 条人工标记的每段 10 秒长度的声音剪辑片段（来源于YouTube视频）。目前该数据集已经有210万个已标注的视频数据，5800小时的音频数据，经过标记的声音样本的标签类别为527。 +在IEEE ICASSP 2017 大会上，谷歌开放了一个大规模的音频数据集[Audioset](https://research.google.com/audioset/)。该数据集包含了 632 类的音频类别以及 2,084,320 条人工标记的每段 **10 秒**长度的声音剪辑片段（来源于YouTube视频）。目前该数据集已经有 210万 个已标注的视频数据，5800 小时的音频数据，经过标记的声音样本的标签类别为 527。 `PANNs`([PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://arxiv.org/pdf/1912.10211.pdf))是基于Audioset数据集训练的声音分类/识别的模型。经过预训练后，模型可以用于提取音频的embbedding。本示例将使用`PANNs`的预训练模型Finetune完成声音分类的任务。 @@ -12,14 +12,14 @@ ## 模型简介 PaddleAudio提供了PANNs的CNN14、CNN10和CNN6的预训练模型，可供用户选择使用： -- CNN14: 该模型主要包含12个卷积层和2个全连接层，模型参数的数量为79.6M，embbedding维度是2048。 -- CNN10: 该模型主要包含8个卷积层和2个全连接层，模型参数的数量为4.9M，embbedding维度是512。 -- CNN6: 该模型主要包含4个卷积层和2个全连接层，模型参数的数量为4.5M，embbedding维度是512。 +- CNN14: 该模型主要包含12个卷积层和2个全连接层，模型参数的数量为 79.6M，embbedding维度是 2048。 +- CNN10: 该模型主要包含8个卷积层和2个全连接层，模型参数的数量为 4.9M，embbedding维度是 512。 +- CNN6: 该模型主要包含4个卷积层和2个全连接层，模型参数的数量为 4.5M，embbedding维度是 512。 ## 数据集 -[ESC-50: Dataset for Environmental Sound Classification](https://github.com/karolpiczak/ESC-50) 是一个包含有 2000 个带标签的环境声音样本，音频样本采样率为 44,100Hz 的单通道音频文件，所有样本根据标签被划分为 50 个类别，每个类别有 40 个样本。 +[ESC-50: Dataset for Environmental Sound Classification](https://github.com/karolpiczak/ESC-50) 是一个包含有 2000 个带标签的时长为 **5 秒**的环境声音样本，音频样本采样率为 44,100Hz 的单通道音频文件，所有样本根据标签被划分为 50 个类别，每个类别有 40 个样本。 ## 模型指标 @@ -43,13 +43,13 @@ $ CUDA_VISIBLE_DEVICES=0 ./run.sh 1 conf/panns.yaml ``` 训练的参数可在 `conf/panns.yaml` 的 `training` 中配置，其中： -- `epochs`: 训练轮次，默认为50。 +- `epochs`: 训练轮次，默认为 50。 - `learning_rate`: Fine-tune的学习率；默认为5e-5。 -- `batch_size`: 批处理大小，请结合显存情况进行调整，若出现显存不足，请适当调低这一参数；默认为16。 +- `batch_size`: 批处理大小，请结合显存情况进行调整，若出现显存不足，请适当调低这一参数；默认为 16。 - `num_workers`: Dataloader获取数据的子进程数。默认为0，加载数据的流程在主进程执行。 - `checkpoint_dir`: 模型参数文件和optimizer参数文件的保存目录，默认为`./checkpoint`。 -- `save_freq`: 训练过程中的模型保存频率，默认为10。 -- `log_freq`: 训练过程中的信息打印频率，默认为10。 +- `save_freq`: 训练过程中的模型保存频率，默认为 10。 +- `log_freq`: 训练过程中的信息打印频率，默认为 10。 示例代码中使用的预训练模型为`CNN14`，如果想更换为其他预训练模型，可通过修改 `conf/panns.yaml` 的 `model` 中配置： ```yaml @@ -76,7 +76,7 @@ $ CUDA_VISIBLE_DEVICES=0 ./run.sh 2 conf/panns.yaml 训练的参数可在 `conf/panns.yaml` 的 `predicting` 中配置，其中： - `audio_file`: 指定预测的音频文件。 -- `top_k`: 预测显示的top k标签的得分，默认为1。 +- `top_k`: 预测显示的top k标签的得分，默认为 1。 - `checkpoint`: 模型参数checkpoint文件。 输出的预测结果如下： diff --git a/examples/iwslt2012/punc0/README.md b/examples/iwslt2012/punc0/README.md index 74d599a21..6caa9710b 100644 --- a/examples/iwslt2012/punc0/README.md +++ b/examples/iwslt2012/punc0/README.md @@ -21,7 +21,7 @@ The pretrained model can be downloaded here [ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip). ### Test Result -- Ernie Linear +- Ernie | |COMMA | PERIOD | QUESTION | OVERALL| |:-----:|:-----:|:-----:|:-----:|:-----:| |Precision |0.510955 |0.526462 |0.820755 |0.619391| diff --git a/examples/iwslt2012/punc0/RESULTS.md b/examples/iwslt2012/punc0/RESULTS.md new file mode 100644 index 000000000..2e22713d8 --- /dev/null +++ b/examples/iwslt2012/punc0/RESULTS.md @@ -0,0 +1,9 @@ +# iwslt2012 + +## Ernie + +| |COMMA | PERIOD | QUESTION | OVERALL| +|:-----:|:-----:|:-----:|:-----:|:-----:| +|Precision |0.510955 |0.526462 |0.820755 |0.619391| +|Recall |0.517433 |0.564179 |0.861386 |0.647666| +|F1 |0.514173 |0.544669 |0.840580 |0.633141| diff --git a/examples/librispeech/asr1/README.md b/examples/librispeech/asr1/README.md index eb1a44001..ae252a58b 100644 --- a/examples/librispeech/asr1/README.md +++ b/examples/librispeech/asr1/README.md @@ -151,44 +151,22 @@ avg.sh best exp/conformer/checkpoints 20 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 ``` ## Pretrained Model -You can get the pretrained transformer or conformer using the scripts below: -```bash -# Conformer: -wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/conformer.model.tar.gz -# Transformer: -wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/transformer.model.tar.gz -``` +You can get the pretrained transformer or conformer from [this](../../../docs/source/released_model.md). + using the `tar` scripts to unpack the model and then you can use the script to test the model. For example: ```bash -wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/conformer.model.tar.gz -tar xzvf transformer.model.tar.gz +wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz +tar xzvf asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz source path.sh # If you have process the data and get the manifest file， you can skip the following 2 steps bash local/data.sh --stage -1 --stop_stage -1 bash local/data.sh --stage 2 --stop_stage 2 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 ``` -The performance of the released models are shown below: -## Conformer -train: Epoch 70, 4 V100-32G, best avg: 20 - -| Model | Params | Config | Augmentation | Test set | Decode method | Loss | WER | -| --------- | ------- | ------------------- | ------------ | ---------- | ---------------------- | ----------------- | -------- | -| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-clean | attention | 6.433612394332886 | 0.039771 | -| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-clean | ctc_greedy_search | 6.433612394332886 | 0.040342 | -| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-clean | ctc_prefix_beam_search | 6.433612394332886 | 0.040342 | -| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-clean | attention_rescoring | 6.433612394332886 | 0.033761 | -## Transformer -train: Epoch 120, 4 V100-32G, 27 Day, best avg: 10 +The performance of the released models are shown in [here](./RESULTS.md). -| Model | Params | Config | Augmentation | Test set | Decode method | Loss | WER | -| ----------- | ------- | --------------------- | ------------ | ---------- | ---------------------- | ----------------- | -------- | -| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | attention | 6.382194232940674 | 0.049661 | -| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | ctc_greedy_search | 6.382194232940674 | 0.049566 | -| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | ctc_prefix_beam_search | 6.382194232940674 | 0.049585 | -| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | attention_rescoring | 6.382194232940674 | 0.038135 | ## Stage 4: CTC Alignment If you want to get the alignment between the audio and the text, you can use the ctc alignment. The code of this stage is shown below: ```bash @@ -227,8 +205,8 @@ In some situations, you want to use the trained model to do the inference for th ``` you can train the model by yourself using ```bash run.sh --stage 0 --stop_stage 3```, or you can download the pretrained model through the script below: ```bash -wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/conformer.model.tar.gz -tar xzvf conformer.model.tar.gz +wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz +tar xzvf asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz ``` You can download the audio demo: ```bash diff --git a/examples/librispeech/asr2/README.md b/examples/librispeech/asr2/README.md index 7d6fe11df..5bc7185a9 100644 --- a/examples/librispeech/asr2/README.md +++ b/examples/librispeech/asr2/README.md @@ -1,4 +1,4 @@ -# Transformer/Conformer ASR with Librispeech Asr2 +# Transformer/Conformer ASR with Librispeech ASR2 This example contains code used to train a Transformer or [Conformer](http://arxiv.org/abs/2008.03802) model with [Librispeech dataset](http://www.openslr.org/resources/12) and use some functions in kaldi. @@ -213,17 +213,14 @@ avg.sh latest exp/transformer/checkpoints 10 ./local/recog.sh --ckpt_prefix exp/transformer/checkpoints/avg_10 ``` ## Pretrained Model -You can get the pretrained transformer using the scripts below: -```bash -# Transformer: -wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/transformer.model.tar.gz -``` +You can get the pretrained models from [this](../../../docs/source/released_model.md). + using the `tar` scripts to unpack the model and then you can use the script to test the model. For example: ```bash -wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/transformer.model.tar.gz -tar xzvf transformer.model.tar.gz +wget https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz +tar xzvf asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz source path.sh # If you have process the data and get the manifest file， you can skip the following 2 steps bash local/data.sh --stage -1 --stop_stage -1 @@ -231,26 +228,7 @@ bash local/data.sh --stage 2 --stop_stage 2 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/transformer.yaml exp/ctc/checkpoints/avg_10 ``` -The performance of the released models are shown below: -### Transformer -| Model | Params | GPUS | Averaged Model | Config | Augmentation | Loss | -| :---------: | :----: | :--------------------: | :--------------: | :-------------------: | :----------: | :-------------: | -| transformer | 32.52M | 8 Tesla V100-SXM2-32GB | 10-best val_loss | conf/transformer.yaml | spec_aug | 6.3197922706604 | - -#### Attention Rescore -| Test Set | Decode Method | #Snt | #Wrd | Corr | Sub | Del | Ins | Err | S.Err | -| ---------- | --------------------- | ---- | ----- | ---- | ---- | ---- | ---- | ---- | ----- | -| test-clean | attention | 2620 | 52576 | 96.4 | 2.5 | 1.1 | 0.4 | 4.0 | 34.7 | -| test-clean | ctc_greedy_search | 2620 | 52576 | 95.9 | 3.7 | 0.4 | 0.5 | 4.6 | 48.0 | -| test-clean | ctc_prefix_beamsearch | 2620 | 52576 | 95.9 | 3.7 | 0.4 | 0.5 | 4.6 | 47.6 | -| test-clean | attention_rescore | 2620 | 52576 | 96.8 | 2.9 | 0.3 | 0.4 | 3.7 | 38.0 | - -#### JoinCTC -| Test Set | Decode Method | #Snt | #Wrd | Corr | Sub | Del | Ins | Err | S.Err | -| ---------- | ----------------- | ---- | ----- | ---- | ---- | ---- | ---- | ---- | ----- | -| test-clean | join_ctc_only_att | 2620 | 52576 | 96.1 | 2.5 | 1.4 | 0.4 | 4.4 | 34.7 | -| test-clean | join_ctc_w/o_lm | 2620 | 52576 | 97.2 | 2.6 | 0.3 | 0.4 | 3.2 | 34.9 | -| test-clean | join_ctc_w_lm | 2620 | 52576 | 97.9 | 1.8 | 0.2 | 0.3 | 2.4 | 27.8 | +The performance of the released models are shown [here](./RESULTS.md). Compare with [ESPNET](https://github.com/espnet/espnet/blob/master/egs/librispeech/asr1/RESULTS.md#pytorch-large-transformer-with-specaug-4-gpus--transformer-lm-4-gpus) we using 8gpu, but the model size (aheads4-adim256) small than it. ## Stage 5: CTC Alignment diff --git a/examples/ljspeech/tts1/README.md b/examples/ljspeech/tts1/README.md index 4f7680e84..7f32522ac 100644 --- a/examples/ljspeech/tts1/README.md +++ b/examples/ljspeech/tts1/README.md @@ -171,7 +171,8 @@ optional arguments: 6. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Model -Pretrained Model can be downloaded here. [transformer_tts_ljspeech_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/transformer_tts/transformer_tts_ljspeech_ckpt_0.4.zip) +Pretrained Model can be downloaded here: +- [transformer_tts_ljspeech_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/transformer_tts/transformer_tts_ljspeech_ckpt_0.4.zip) TransformerTTS checkpoint contains files listed below. ```text diff --git a/examples/ljspeech/tts3/README.md b/examples/ljspeech/tts3/README.md index f5e919c0f..e028fa05d 100644 --- a/examples/ljspeech/tts3/README.md +++ b/examples/ljspeech/tts3/README.md @@ -214,7 +214,8 @@ optional arguments: 9. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Model -Pretrained FastSpeech2 model with no silence in the edge of audios. [fastspeech2_nosil_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip) +Pretrained FastSpeech2 model with no silence in the edge of audios: +- [fastspeech2_nosil_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip) Model | Step | eval/loss | eval/l1_loss | eval/duration_loss | eval/pitch_loss| eval/energy_loss :-------------:| :------------:| :-----: | :-----: | :--------: |:--------:|:---------: diff --git a/examples/ljspeech/tts3/local/synthesize.sh b/examples/ljspeech/tts3/local/synthesize.sh index 6dc34274c..0733e96fa 100755 --- a/examples/ljspeech/tts3/local/synthesize.sh +++ b/examples/ljspeech/tts3/local/synthesize.sh @@ -26,7 +26,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then fi # hifigan -if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then FLAGS_allocator_strategy=naive_best_fit \ FLAGS_fraction_of_gpu_memory_to_use=0.01 \ python3 ${BIN_DIR}/../synthesize.py \ diff --git a/examples/ljspeech/voc0/README.md b/examples/ljspeech/voc0/README.md index 13a50efb5..41b08d57f 100644 --- a/examples/ljspeech/voc0/README.md +++ b/examples/ljspeech/voc0/README.md @@ -50,4 +50,5 @@ Synthesize waveform. 6. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Model -Pretrained Model with residual channel equals 128 can be downloaded here. [waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/waveflow/waveflow_ljspeech_ckpt_0.3.zip). +Pretrained Model with residual channel equals 128 can be downloaded here: +- [waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/waveflow/waveflow_ljspeech_ckpt_0.3.zip) diff --git a/examples/ljspeech/voc1/README.md b/examples/ljspeech/voc1/README.md index 6fcb2a520..4513b2a05 100644 --- a/examples/ljspeech/voc1/README.md +++ b/examples/ljspeech/voc1/README.md @@ -127,7 +127,8 @@ optional arguments: 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Model -Pretrained models can be downloaded here. [pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip) +Pretrained models can be downloaded here: +- [pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip) Parallel WaveGAN checkpoint contains files listed below. diff --git a/examples/ljspeech/voc5/README.md b/examples/ljspeech/voc5/README.md index 9fbb9f746..9b31e2650 100644 --- a/examples/ljspeech/voc5/README.md +++ b/examples/ljspeech/voc5/README.md @@ -127,7 +127,8 @@ optional arguments: 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Model -The pretrained model can be downloaded here [hifigan_ljspeech_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_ckpt_0.2.0.zip). +The pretrained model can be downloaded here: +- [hifigan_ljspeech_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_ckpt_0.2.0.zip) Model | Step | eval/generator_loss | eval/mel_loss| eval/feature_matching_loss @@ -143,6 +144,5 @@ hifigan_ljspeech_ckpt_0.2.0 └── snapshot_iter_2500000.pdz # generator parameters of hifigan ``` - ## Acknowledgement We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN. diff --git a/examples/other/1xt2x/src_deepspeech2x/__init__.py b/examples/other/1xt2x/src_deepspeech2x/__init__.py index 62f3eee3e..74be4a254 100644 --- a/examples/other/1xt2x/src_deepspeech2x/__init__.py +++ b/examples/other/1xt2x/src_deepspeech2x/__init__.py @@ -26,10 +26,10 @@ from paddlespeech.s2t.utils.log import Log #TODO(Hui Zhang): remove fluid import logger = Log(__name__).getlog() -########### hcak logging ############# +########### hack logging ############# logger.warn = logger.warning -########### hcak paddle ############# +########### hack paddle ############# paddle.half = 'float16' paddle.float = 'float32' paddle.double = 'float64' @@ -110,7 +110,7 @@ if not hasattr(paddle, 'cat'): paddle.cat = cat -########### hcak paddle.Tensor ############# +########### hack paddle.Tensor ############# def item(x: paddle.Tensor): return x.numpy().item() @@ -353,7 +353,7 @@ if not hasattr(paddle.Tensor, 'tolist'): setattr(paddle.Tensor, 'tolist', tolist) -########### hcak paddle.nn ############# +########### hack paddle.nn ############# class GLU(nn.Layer): """Gated Linear Units (GLU) Layer""" diff --git a/examples/other/ngram_lm/s0/local/build_zh_lm.sh b/examples/other/ngram_lm/s0/local/build_zh_lm.sh index 73eb165ec..b031371f5 100644 --- a/examples/other/ngram_lm/s0/local/build_zh_lm.sh +++ b/examples/other/ngram_lm/s0/local/build_zh_lm.sh @@ -27,7 +27,7 @@ arpa=$3 if [ $stage -le 0 ] && [ $stop_stage -ge 0 ];then # text tn & wordseg preprocess echo "process text." - python3 ${MAIN_ROOT}/utils/zh_tn.py ${type} ${text} ${text}.${type}.tn + python3 ${MAIN_ROOT}/utils/zh_tn.py --token_type ${type} ${text} ${text}.${type}.tn fi if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then diff --git a/examples/other/ngram_lm/s0/local/download_lm_zh.sh b/examples/other/ngram_lm/s0/local/download_lm_zh.sh index f9e2261fd..050749ce1 100755 --- a/examples/other/ngram_lm/s0/local/download_lm_zh.sh +++ b/examples/other/ngram_lm/s0/local/download_lm_zh.sh @@ -10,6 +10,11 @@ MD5="29e02312deb2e59b3c8686c7966d4fe3" TARGET=${DIR}/zh_giga.no_cna_cmn.prune01244.klm +if [ -e $TARGET ];then + echo "already have lm" + exit 0; +fi + echo "Download language model ..." download $URL $MD5 $TARGET if [ $? -ne 0 ]; then diff --git a/examples/vctk/tts3/README.md b/examples/vctk/tts3/README.md index 157949d1f..f373ca6a3 100644 --- a/examples/vctk/tts3/README.md +++ b/examples/vctk/tts3/README.md @@ -217,7 +217,8 @@ optional arguments: 9. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Model -Pretrained FastSpeech2 model with no silence in the edge of audios. [fastspeech2_nosil_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip) +Pretrained FastSpeech2 model with no silence in the edge of audios: +- [fastspeech2_nosil_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip) FastSpeech2 checkpoint contains files listed below. ```text diff --git a/examples/vctk/voc1/README.md b/examples/vctk/voc1/README.md index 4714f28dc..1c3016f88 100644 --- a/examples/vctk/voc1/README.md +++ b/examples/vctk/voc1/README.md @@ -132,7 +132,8 @@ optional arguments: 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Model -Pretrained models can be downloaded here [pwg_vctk_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip). +Pretrained models can be downloaded here: +- [pwg_vctk_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip) Parallel WaveGAN checkpoint contains files listed below. diff --git a/examples/vctk/voc5/README.md b/examples/vctk/voc5/README.md index b4be341c0..4eb25c02d 100644 --- a/examples/vctk/voc5/README.md +++ b/examples/vctk/voc5/README.md @@ -133,7 +133,8 @@ optional arguments: 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. ## Pretrained Model -The pretrained model can be downloaded here [hifigan_vctk_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip). +The pretrained model can be downloaded here: +- [hifigan_vctk_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip) Model | Step | eval/generator_loss | eval/mel_loss| eval/feature_matching_loss diff --git a/examples/voxceleb/sv0/RESULT.md b/examples/voxceleb/sv0/RESULT.md index c37bcecef..3a3f67d09 100644 --- a/examples/voxceleb/sv0/RESULT.md +++ b/examples/voxceleb/sv0/RESULT.md @@ -4,4 +4,4 @@ | Model | Number of Params | Release | Config | dim | Test set | Cosine | Cosine + S-Norm | | --- | --- | --- | --- | --- | --- | --- | ---- | -| ECAPA-TDNN | 85M | 0.1.1 | conf/ecapa_tdnn.yaml |192 | test | 1.15 | 1.06 | +| ECAPA-TDNN | 85M | 0.2.0 | conf/ecapa_tdnn.yaml |192 | test | 1.02 | 0.95 | diff --git a/examples/voxceleb/sv0/conf/ecapa_tdnn.yaml b/examples/voxceleb/sv0/conf/ecapa_tdnn.yaml index e58dca82d..3e3a13072 100644 --- a/examples/voxceleb/sv0/conf/ecapa_tdnn.yaml +++ b/examples/voxceleb/sv0/conf/ecapa_tdnn.yaml @@ -1,14 +1,16 @@ ########################################### # Data # ########################################### -# we should explicitly specify the wav path of vox2 audio data converted from m4a -vox2_base_path: augment: True -batch_size: 16 +batch_size: 32 num_workers: 2 num_speakers: 7205 # 1211 vox1, 5994 vox2, 7205 vox1+2, test speakers: 41 shuffle: True +skip_prep: False +split_ratio: 0.9 +chunk_duration: 3.0 # seconds random_chunk: True +verification_file: data/vox1/veri_test2.txt ########################################################### # FEATURE EXTRACTION SETTING # @@ -26,7 +28,6 @@ hop_size: 160 #10ms, sample rate 16000, 10 * 16000 / 1000 = 160 # if we want use another model, please choose another configuration yaml file model: input_size: 80 - # "channels": [512, 512, 512, 512, 1536], channels: [1024, 1024, 1024, 1024, 3072] kernel_sizes: [5, 3, 3, 3, 1] dilations: [1, 2, 3, 4, 1] @@ -38,11 +39,19 @@ model: ########################################### seed: 1986 # according from speechbrain configuration epochs: 10 -save_interval: 1 -log_interval: 1 +save_interval: 10 +log_interval: 10 learning_rate: 1e-8 +max_lr: 1e-3 +step_size: 140000 +########################################### +# loss # +########################################### +margin: 0.2 +scale: 30 + ########################################### # Testing # ########################################### diff --git a/examples/voxceleb/sv0/conf/ecapa_tdnn_small.yaml b/examples/voxceleb/sv0/conf/ecapa_tdnn_small.yaml new file mode 100644 index 000000000..5925e5730 --- /dev/null +++ b/examples/voxceleb/sv0/conf/ecapa_tdnn_small.yaml @@ -0,0 +1,60 @@ +########################################### +# Data # +########################################### +augment: True +batch_size: 32 +num_workers: 2 +num_speakers: 1211 # 1211 vox1, 5994 vox2, 7205 vox1+2, test speakers: 41 +shuffle: True +skip_prep: False +split_ratio: 0.9 +chunk_duration: 3.0 # seconds +random_chunk: True +verification_file: data/vox1/veri_test2.txt + +########################################################### +# FEATURE EXTRACTION SETTING # +########################################################### +# currently, we only support fbank +sr: 16000 # sample rate +n_mels: 80 +window_size: 400 #25ms, sample rate 16000, 25 * 16000 / 1000 = 400 +hop_size: 160 #10ms, sample rate 16000, 10 * 16000 / 1000 = 160 + +########################################################### +# MODEL SETTING # +########################################################### +# currently, we only support ecapa-tdnn in the ecapa_tdnn.yaml +# if we want use another model, please choose another configuration yaml file +model: + input_size: 80 + channels: [512, 512, 512, 512, 1536] + kernel_sizes: [5, 3, 3, 3, 1] + dilations: [1, 2, 3, 4, 1] + attention_channels: 128 + lin_neurons: 192 + +########################################### +# Training # +########################################### +seed: 1986 # according from speechbrain configuration +epochs: 100 +save_interval: 10 +log_interval: 10 +learning_rate: 1e-8 +max_lr: 1e-3 +step_size: 140000 + +########################################### +# loss # +########################################### +margin: 0.2 +scale: 30 + +########################################### +# Testing # +########################################### +global_embedding_norm: True +embedding_mean_norm: True +embedding_std_norm: False + diff --git a/examples/voxceleb/sv0/local/data.sh b/examples/voxceleb/sv0/local/data.sh index a3ff1c486..d6010ec66 100755 --- a/examples/voxceleb/sv0/local/data.sh +++ b/examples/voxceleb/sv0/local/data.sh @@ -12,7 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -stage=1 +stage=0 stop_stage=100 . ${MAIN_ROOT}/utils/parse_options.sh || exit -1; @@ -30,29 +30,114 @@ dir=$1 conf_path=$2 mkdir -p ${dir} -if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then - # data prepare for vox1 and vox2, vox2 must be converted from m4a to wav - # we should use the local/convert.sh convert m4a to wav - python3 local/data_prepare.py \ - --data-dir ${dir} \ - --config ${conf_path} -fi - +# Generally the `MAIN_ROOT` refers to the root of PaddleSpeech, +# which is defined in the path.sh +# And we will download the voxceleb data and rirs noise to ${MAIN_ROOT}/dataset TARGET_DIR=${MAIN_ROOT}/dataset mkdir -p ${TARGET_DIR} if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then - # download data, generate manifests - python3 ${TARGET_DIR}/voxceleb/voxceleb1.py \ - --manifest_prefix="data/vox1/manifest" \ + # download data, generate manifests + # we will generate the manifest.{dev,test} file from ${TARGET_DIR}/voxceleb/vox1/{dev,test} directory + # and generate the meta info and download the trial file + # manifest.dev: 148642 + # manifest.test: 4847 + echo "Start to download vox1 dataset and generate the manifest files " + python3 ${TARGET_DIR}/voxceleb/voxceleb1.py \ + --manifest_prefix="${dir}/vox1/manifest" \ --target_dir="${TARGET_DIR}/voxceleb/vox1/" - if [ $? -ne 0 ]; then - echo "Prepare voxceleb failed. Terminated." - exit 1 - fi + if [ $? -ne 0 ]; then + echo "Prepare voxceleb1 failed. Terminated." + exit 1 + fi + +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + # download voxceleb2 data + # we will download the data and unzip the package + # and we will store the m4a file in ${TARGET_DIR}/voxceleb/vox2/{dev,test} + echo "start to download vox2 dataset" + python3 ${TARGET_DIR}/voxceleb/voxceleb2.py \ + --download \ + --target_dir="${TARGET_DIR}/voxceleb/vox2/" + + if [ $? -ne 0 ]; then + echo "Download voxceleb2 dataset failed. Terminated." + exit 1 + fi + +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + # convert the m4a to wav + # and we will not delete the original m4a file + echo "start to convert the m4a to wav" + bash local/convert.sh ${TARGET_DIR}/voxceleb/vox2/test/ || exit 1; + + if [ $? -ne 0 ]; then + echo "Convert voxceleb2 dataset from m4a to wav failed. Terminated." + exit 1 + fi + echo "m4a convert to wav operation finished" +fi + +if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then + # generate the vox2 manifest file from wav file + # we will generate the ${dir}/vox2/manifest.vox2 + # because we use all the vox2 dataset to train, so collect all the vox2 data in one file + echo "start generate the vox2 manifest files" + python3 ${TARGET_DIR}/voxceleb/voxceleb2.py \ + --generate \ + --manifest_prefix="${dir}/vox2/manifest" \ + --target_dir="${TARGET_DIR}/voxceleb/vox2/" - # for dataset in train dev test; do - # mv data/manifest.${dataset} data/manifest.${dataset}.raw - # done -fi \ No newline at end of file + if [ $? -ne 0 ]; then + echo "Prepare voxceleb2 dataset failed. Terminated." + exit 1 + fi +fi + +if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then + # generate the vox csv file + # Currently, our training system use csv file for dataset + echo "convert the json format to csv format to be compatible with training process" + python3 local/make_vox_csv_dataset_from_json.py\ + --train "${dir}/vox1/manifest.dev" "${dir}/vox2/manifest.vox2"\ + --test "${dir}/vox1/manifest.test" \ + --target_dir "${dir}/vox/" \ + --config ${conf_path} + + if [ $? -ne 0 ]; then + echo "Prepare voxceleb failed. Terminated." + exit 1 + fi +fi + +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + # generate the open rir noise manifest file + echo "generate the open rir noise manifest file" + python3 ${TARGET_DIR}/rir_noise/rir_noise.py\ + --manifest_prefix="${dir}/rir_noise/manifest" \ + --target_dir="${TARGET_DIR}/rir_noise/" + + if [ $? -ne 0 ]; then + echo "Prepare rir_noise failed. Terminated." + exit 1 + fi +fi + +if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then + # generate the open rir noise manifest file + echo "generate the open rir noise csv file" + python3 local/make_rirs_noise_csv_dataset_from_json.py \ + --noise_dir="${TARGET_DIR}/rir_noise/" \ + --data_dir="${dir}/rir_noise/" \ + --config ${conf_path} + + if [ $? -ne 0 ]; then + echo "Prepare rir_noise failed. Terminated." + exit 1 + fi +fi diff --git a/examples/voxceleb/sv0/local/make_rirs_noise_csv_dataset_from_json.py b/examples/voxceleb/sv0/local/make_rirs_noise_csv_dataset_from_json.py new file mode 100644 index 000000000..b25a9d49a --- /dev/null +++ b/examples/voxceleb/sv0/local/make_rirs_noise_csv_dataset_from_json.py @@ -0,0 +1,167 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Convert the PaddleSpeech jsonline format data to csv format data in voxceleb experiment. +Currently, Speaker Identificaton Training process use csv format. +""" +import argparse +import csv +import os +from typing import List + +import tqdm +from yacs.config import CfgNode + +from paddleaudio import load as load_audio +from paddlespeech.s2t.utils.log import Log +from paddlespeech.vector.utils.vector_utils import get_chunks + +logger = Log(__name__).getlog() + + +def get_chunks_list(wav_file: str, + split_chunks: bool, + base_path: str, + chunk_duration: float=3.0) -> List[List[str]]: + """Get the single audio file info + + Args: + wav_file (list): the wav audio file and get this audio segment info list + split_chunks (bool): audio split flag + base_path (str): the audio base path + chunk_duration (float): the chunk duration. + if set the split_chunks, we split the audio into multi-chunks segment. + """ + waveform, sr = load_audio(wav_file) + audio_id = wav_file.split("/rir_noise/")[-1].split(".")[0] + audio_duration = waveform.shape[0] / sr + + ret = [] + if split_chunks and audio_duration > chunk_duration: # Split into pieces of self.chunk_duration seconds. + uniq_chunks_list = get_chunks(chunk_duration, audio_id, audio_duration) + + for idx, chunk in enumerate(uniq_chunks_list): + s, e = chunk.split("_")[-2:] # Timestamps of start and end + start_sample = int(float(s) * sr) + end_sample = int(float(e) * sr) + + # currently, all vector csv data format use one representation + # id, duration, wav, start, stop, label + # in rirs noise, all the label name is 'noise' + # the label is string type and we will convert it to integer type in training + ret.append([ + chunk, audio_duration, wav_file, start_sample, end_sample, + "noise" + ]) + else: # Keep whole audio. + ret.append( + [audio_id, audio_duration, wav_file, 0, waveform.shape[0], "noise"]) + return ret + + +def generate_csv(wav_files, + output_file: str, + base_path: str, + split_chunks: bool=True): + """Prepare the csv file according the wav files + + Args: + wav_files (list): all the audio list to prepare the csv file + output_file (str): the output csv file + config (CfgNode): yaml configuration content + split_chunks (bool): audio split flag + """ + logger.info(f'Generating csv: {output_file}') + header = ["utt_id", "duration", "wav", "start", "stop", "label"] + csv_lines = [] + for item in tqdm.tqdm(wav_files): + csv_lines.extend( + get_chunks_list( + item, base_path=base_path, split_chunks=split_chunks)) + + if not os.path.exists(os.path.dirname(output_file)): + os.makedirs(os.path.dirname(output_file)) + + with open(output_file, mode="w") as csv_f: + csv_writer = csv.writer( + csv_f, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL) + csv_writer.writerow(header) + for line in csv_lines: + csv_writer.writerow(line) + + +def prepare_data(args, config): + """Convert the jsonline format to csv format + + Args: + args (argparse.Namespace): scripts args + config (CfgNode): yaml configuration content + """ + # if external config set the skip_prep flat, we will do nothing + if config.skip_prep: + return + + base_path = args.noise_dir + wav_path = os.path.join(base_path, "RIRS_NOISES") + logger.info(f"base path: {base_path}") + logger.info(f"wav path: {wav_path}") + rir_list = os.path.join(wav_path, "real_rirs_isotropic_noises", "rir_list") + rir_files = [] + with open(rir_list, 'r') as f: + for line in f.readlines(): + rir_file = line.strip().split(' ')[-1] + rir_files.append(os.path.join(base_path, rir_file)) + + noise_list = os.path.join(wav_path, "pointsource_noises", "noise_list") + noise_files = [] + with open(noise_list, 'r') as f: + for line in f.readlines(): + noise_file = line.strip().split(' ')[-1] + noise_files.append(os.path.join(base_path, noise_file)) + + csv_path = os.path.join(args.data_dir, 'csv') + logger.info(f"csv path: {csv_path}") + generate_csv( + rir_files, os.path.join(csv_path, 'rir.csv'), base_path=base_path) + generate_csv( + noise_files, os.path.join(csv_path, 'noise.csv'), base_path=base_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--noise_dir", + default=None, + required=True, + help="The noise dataset dataset directory.") + parser.add_argument( + "--data_dir", + default=None, + required=True, + help="The target directory stores the csv files") + parser.add_argument( + "--config", + default=None, + required=True, + type=str, + help="configuration file") + args = parser.parse_args() + + # parse the yaml config file + config = CfgNode(new_allowed=True) + if args.config: + config.merge_from_file(args.config) + + # prepare the csv file from jsonlines files + prepare_data(args, config) diff --git a/examples/voxceleb/sv0/local/make_vox_csv_dataset_from_json.py b/examples/voxceleb/sv0/local/make_vox_csv_dataset_from_json.py new file mode 100644 index 000000000..4e64c3067 --- /dev/null +++ b/examples/voxceleb/sv0/local/make_vox_csv_dataset_from_json.py @@ -0,0 +1,251 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Convert the PaddleSpeech jsonline format data to csv format data in voxceleb experiment. +Currently, Speaker Identificaton Training process use csv format. +""" +import argparse +import csv +import json +import os +import random + +import tqdm +from yacs.config import CfgNode + +from paddleaudio import load as load_audio +from paddlespeech.s2t.utils.log import Log +from paddlespeech.vector.utils.vector_utils import get_chunks + +logger = Log(__name__).getlog() + + +def prepare_csv(wav_files, output_file, config, split_chunks=True): + """Prepare the csv file according the wav files + + Args: + wav_files (list): all the audio list to prepare the csv file + output_file (str): the output csv file + config (CfgNode): yaml configuration content + split_chunks (bool, optional): audio split flag. Defaults to True. + """ + if not os.path.exists(os.path.dirname(output_file)): + os.makedirs(os.path.dirname(output_file)) + csv_lines = [] + header = ["utt_id", "duration", "wav", "start", "stop", "label"] + # voxceleb meta info for each training utterance segment + # we extract a segment from a utterance to train + # and the segment' period is between start and stop time point in the original wav file + # each field in the meta info means as follows: + # utt_id: the utterance segment name, which is uniq in training dataset + # duration: the total utterance time + # wav: utterance file path, which should be absoulute path + # start: start point in the original wav file sample point range + # stop: stop point in the original wav file sample point range + # label: the utterance segment's label name, + # which is speaker name in speaker verification domain + for item in tqdm.tqdm(wav_files, total=len(wav_files)): + item = json.loads(item.strip()) + audio_id = item['utt'].replace(".wav", + "") # we remove the wav suffix name + audio_duration = item['feat_shape'][0] + wav_file = item['feat'] + label = audio_id.split('-')[ + 0] # speaker name in speaker verification domain + waveform, sr = load_audio(wav_file) + if split_chunks: + uniq_chunks_list = get_chunks(config.chunk_duration, audio_id, + audio_duration) + for chunk in uniq_chunks_list: + s, e = chunk.split("_")[-2:] # Timestamps of start and end + start_sample = int(float(s) * sr) + end_sample = int(float(e) * sr) + # id, duration, wav, start, stop, label + # in vector, the label in speaker id + csv_lines.append([ + chunk, audio_duration, wav_file, start_sample, end_sample, + label + ]) + else: + csv_lines.append([ + audio_id, audio_duration, wav_file, 0, waveform.shape[0], label + ]) + + with open(output_file, mode="w") as csv_f: + csv_writer = csv.writer( + csv_f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) + csv_writer.writerow(header) + for line in csv_lines: + csv_writer.writerow(line) + + +def get_enroll_test_list(dataset_list, verification_file): + """Get the enroll and test utterance list from all the voxceleb1 test utterance dataset. + Generally, we get the enroll and test utterances from the verfification file. + The verification file format as follows: + target/nontarget enroll-utt test-utt, + we set 0 as nontarget and 1 as target, eg: + 0 a.wav b.wav + 1 a.wav a.wav + + Args: + dataset_list (list): all the dataset to get the test utterances + verification_file (str): voxceleb1 trial file + """ + logger.info(f"verification file: {verification_file}") + enroll_audios = set() + test_audios = set() + with open(verification_file, 'r') as f: + for line in f: + _, enroll_file, test_file = line.strip().split(' ') + enroll_audios.add('-'.join(enroll_file.split('/'))) + test_audios.add('-'.join(test_file.split('/'))) + + enroll_files = [] + test_files = [] + for dataset in dataset_list: + with open(dataset, 'r') as f: + for line in f: + # audio_id may be in enroll and test at the same time + # eg: 1 a.wav a.wav + # the audio a.wav is enroll and test file at the same time + audio_id = json.loads(line.strip())['utt'] + if audio_id in enroll_audios: + enroll_files.append(line) + if audio_id in test_audios: + test_files.append(line) + + enroll_files = sorted(enroll_files) + test_files = sorted(test_files) + + return enroll_files, test_files + + +def get_train_dev_list(dataset_list, target_dir, split_ratio): + """Get the train and dev utterance list from all the training utterance dataset. + Generally, we use the split_ratio as the train dataset ratio, + and the remaining utterance (ratio is 1 - split_ratio) is the dev dataset + + Args: + dataset_list (list): all the dataset to get the all utterances + target_dir (str): the target train and dev directory, + we will create the csv directory to store the {train,dev}.csv file + split_ratio (float): train dataset ratio in all utterance list + """ + logger.info("start to get train and dev utt list") + if not os.path.exists(os.path.join(target_dir, "meta")): + os.makedirs(os.path.join(target_dir, "meta")) + + audio_files = [] + speakers = set() + for dataset in dataset_list: + with open(dataset, 'r') as f: + for line in f: + # the label is speaker name + label_name = json.loads(line.strip())['utt2spk'] + speakers.add(label_name) + audio_files.append(line.strip()) + speakers = sorted(speakers) + logger.info(f"we get {len(speakers)} speakers from all the train dataset") + + with open(os.path.join(target_dir, "meta", "label2id.txt"), 'w') as f: + for label_id, label_name in enumerate(speakers): + f.write(f'{label_name} {label_id}\n') + logger.info( + f'we store the speakers to {os.path.join(target_dir, "meta", "label2id.txt")}' + ) + + # the split_ratio is for train dataset + # the remaining is for dev dataset + split_idx = int(split_ratio * len(audio_files)) + audio_files = sorted(audio_files) + random.shuffle(audio_files) + train_files, dev_files = audio_files[:split_idx], audio_files[split_idx:] + logger.info( + f"we get train utterances: {len(train_files)}, dev utterance: {len(dev_files)}" + ) + return train_files, dev_files + + +def prepare_data(args, config): + """Convert the jsonline format to csv format + + Args: + args (argparse.Namespace): scripts args + config (CfgNode): yaml configuration content + """ + # stage0: set the random seed + random.seed(config.seed) + + # if external config set the skip_prep flat, we will do nothing + if config.skip_prep: + return + + # stage 1: prepare the enroll and test csv file + # And we generate the speaker to label file label2id.txt + logger.info("start to prepare the data csv file") + enroll_files, test_files = get_enroll_test_list( + [args.test], verification_file=config.verification_file) + prepare_csv( + enroll_files, + os.path.join(args.target_dir, "csv", "enroll.csv"), + config, + split_chunks=False) + prepare_csv( + test_files, + os.path.join(args.target_dir, "csv", "test.csv"), + config, + split_chunks=False) + + # stage 2: prepare the train and dev csv file + # we get the train dataset ratio as config.split_ratio + # and the remaining is dev dataset + logger.info("start to prepare the data csv file") + train_files, dev_files = get_train_dev_list( + args.train, target_dir=args.target_dir, split_ratio=config.split_ratio) + prepare_csv(train_files, + os.path.join(args.target_dir, "csv", "train.csv"), config) + prepare_csv(dev_files, + os.path.join(args.target_dir, "csv", "dev.csv"), config) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--train", + required=True, + nargs='+', + help="The jsonline files list for train.") + parser.add_argument( + "--test", required=True, help="The jsonline file for test") + parser.add_argument( + "--target_dir", + default=None, + required=True, + help="The target directory stores the csv files and meta file.") + parser.add_argument( + "--config", + default=None, + required=True, + type=str, + help="configuration file") + args = parser.parse_args() + + # parse the yaml config file + config = CfgNode(new_allowed=True) + if args.config: + config.merge_from_file(args.config) + + # prepare the csv file from jsonlines files + prepare_data(args, config) diff --git a/examples/voxceleb/sv0/run.sh b/examples/voxceleb/sv0/run.sh index bbc9e3dbb..e1dccf2ae 100755 --- a/examples/voxceleb/sv0/run.sh +++ b/examples/voxceleb/sv0/run.sh @@ -18,24 +18,22 @@ set -e ####################################################################### # stage 0: data prepare, including voxceleb1 download and generate {train,dev,enroll,test}.csv -# voxceleb2 data is m4a format, so we need user to convert the m4a to wav yourselves as described in Readme.md with the script local/convert.sh +# voxceleb2 data is m4a format, so we need convert the m4a to wav yourselves with the script local/convert.sh # stage 1: train the speaker identification model # stage 2: test speaker identification -# stage 3: extract the training embeding to train the LDA and PLDA +# stage 3: (todo)extract the training embeding to train the LDA and PLDA ###################################################################### -# we can set the variable PPAUDIO_HOME to specifiy the root directory of the downloaded vox1 and vox2 dataset -# default the dataset will be stored in the ~/.paddleaudio/ # the vox2 dataset is stored in m4a format, we need to convert the audio from m4a to wav yourself -# and put all of them to ${PPAUDIO_HOME}/datasets/vox2 -# we will find the wav from ${PPAUDIO_HOME}/datasets/vox1/wav and ${PPAUDIO_HOME}/datasets/vox2/wav -# export PPAUDIO_HOME= +# and put all of them to ${MAIN_ROOT}/datasets/vox2 +# we will find the wav from ${MAIN_ROOT}/datasets/vox1/{dev,test}/wav and ${MAIN_ROOT}/datasets/vox2/wav + stage=0 stop_stage=50 # data directory # if we set the variable ${dir}, we will store the wav info to this directory -# otherwise, we will store the wav info to vox1 and vox2 directory respectively +# otherwise, we will store the wav info to data/vox1 and data/vox2 directory respectively # vox2 wav path, we must convert the m4a format to wav format dir=data/ # data info directory @@ -64,6 +62,6 @@ if [ $stage -le 2 ] && [ ${stop_stage} -ge 2 ]; then fi # if [ $stage -le 3 ]; then -# # stage 2: extract the training embeding to train the LDA and PLDA +# # stage 3: extract the training embeding to train the LDA and PLDA # # todo: extract the training embedding # fi diff --git a/paddleaudio/paddleaudio/compliance/librosa.py b/paddleaudio/paddleaudio/compliance/librosa.py index 740584ca5..168632d7c 100644 --- a/paddleaudio/paddleaudio/compliance/librosa.py +++ b/paddleaudio/paddleaudio/compliance/librosa.py @@ -341,7 +341,7 @@ def stft(x: np.ndarray, hop_length (Optional[int], optional): Number of steps to advance between adjacent windows. Defaults to None. win_length (Optional[int], optional): The size of window. Defaults to None. window (str, optional): A string of window specification. Defaults to "hann". - center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\_length` at the center of `t`-th frame. Defaults to True. + center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\\_length` at the center of `t`-th frame. Defaults to True. dtype (type, optional): Data type of STFT results. Defaults to np.complex64. pad_mode (str, optional): Choose padding pattern when `center` is `True`. Defaults to "reflect". @@ -509,7 +509,7 @@ def melspectrogram(x: np.ndarray, fmin (float, optional): Minimum frequency in Hz. Defaults to 50.0. fmax (Optional[float], optional): Maximum frequency in Hz. Defaults to None. window (str, optional): A string of window specification. Defaults to "hann". - center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\_length` at the center of `t`-th frame. Defaults to True. + center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\\_length` at the center of `t`-th frame. Defaults to True. pad_mode (str, optional): Choose padding pattern when `center` is `True`. Defaults to "reflect". power (float, optional): Exponent for the magnitude melspectrogram. Defaults to 2.0. to_db (bool, optional): Enable db scale. Defaults to True. @@ -564,7 +564,7 @@ def spectrogram(x: np.ndarray, window_size (int, optional): Size of FFT and window length. Defaults to 512. hop_length (int, optional): Number of steps to advance between adjacent windows. Defaults to 320. window (str, optional): A string of window specification. Defaults to "hann". - center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\_length` at the center of `t`-th frame. Defaults to True. + center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\\_length` at the center of `t`-th frame. Defaults to True. pad_mode (str, optional): Choose padding pattern when `center` is `True`. Defaults to "reflect". power (float, optional): Exponent for the magnitude melspectrogram. Defaults to 2.0. diff --git a/paddleaudio/paddleaudio/datasets/voxceleb.py b/paddleaudio/paddleaudio/datasets/voxceleb.py index 3f72b5f2e..07f44e0c1 100644 --- a/paddleaudio/paddleaudio/datasets/voxceleb.py +++ b/paddleaudio/paddleaudio/datasets/voxceleb.py @@ -261,7 +261,7 @@ class VoxCeleb(Dataset): output_file: str, split_chunks: bool=True): print(f'Generating csv: {output_file}') - header = ["ID", "duration", "wav", "start", "stop", "spk_id"] + header = ["id", "duration", "wav", "start", "stop", "spk_id"] # Note: this may occurs c++ execption, but the program will execute fine # so we can ignore the execption with Pool(cpu_count()) as p: diff --git a/paddleaudio/paddleaudio/features/layers.py b/paddleaudio/paddleaudio/features/layers.py index 09037255d..292363e64 100644 --- a/paddleaudio/paddleaudio/features/layers.py +++ b/paddleaudio/paddleaudio/features/layers.py @@ -42,7 +42,7 @@ class Spectrogram(nn.Layer): win_length (Optional[int], optional): The window length of the short time FFT. If `None`, it is set to same as `n_fft`. Defaults to None. window (str, optional): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'kaiser', 'gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor'. Defaults to 'hann'. power (float, optional): Exponent for the magnitude spectrogram. Defaults to 2.0. - center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\_length` at the center of `t`-th frame. Defaults to True. + center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\\_length` at the center of `t`-th frame. Defaults to True. pad_mode (str, optional): Choose padding pattern when `center` is `True`. Defaults to 'reflect'. dtype (str, optional): Data type of input and window. Defaults to 'float32'. """ @@ -99,7 +99,7 @@ class MelSpectrogram(nn.Layer): win_length (Optional[int], optional): The window length of the short time FFT. If `None`, it is set to same as `n_fft`. Defaults to None. window (str, optional): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'kaiser', 'gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor'. Defaults to 'hann'. power (float, optional): Exponent for the magnitude spectrogram. Defaults to 2.0. - center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\_length` at the center of `t`-th frame. Defaults to True. + center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\\_length` at the center of `t`-th frame. Defaults to True. pad_mode (str, optional): Choose padding pattern when `center` is `True`. Defaults to 'reflect'. n_mels (int, optional): Number of mel bins. Defaults to 64. f_min (float, optional): Minimum frequency in Hz. Defaults to 50.0. @@ -176,7 +176,7 @@ class LogMelSpectrogram(nn.Layer): win_length (Optional[int], optional): The window length of the short time FFT. If `None`, it is set to same as `n_fft`. Defaults to None. window (str, optional): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'kaiser', 'gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor'. Defaults to 'hann'. power (float, optional): Exponent for the magnitude spectrogram. Defaults to 2.0. - center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\_length` at the center of `t`-th frame. Defaults to True. + center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\\_length` at the center of `t`-th frame. Defaults to True. pad_mode (str, optional): Choose padding pattern when `center` is `True`. Defaults to 'reflect'. n_mels (int, optional): Number of mel bins. Defaults to 64. f_min (float, optional): Minimum frequency in Hz. Defaults to 50.0. @@ -257,7 +257,7 @@ class MFCC(nn.Layer): win_length (Optional[int], optional): The window length of the short time FFT. If `None`, it is set to same as `n_fft`. Defaults to None. window (str, optional): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'kaiser', 'gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor'. Defaults to 'hann'. power (float, optional): Exponent for the magnitude spectrogram. Defaults to 2.0. - center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\_length` at the center of `t`-th frame. Defaults to True. + center (bool, optional): Whether to pad `x` to make that the :math:`t \times hop\\_length` at the center of `t`-th frame. Defaults to True. pad_mode (str, optional): Choose padding pattern when `center` is `True`. Defaults to 'reflect'. n_mels (int, optional): Number of mel bins. Defaults to 64. f_min (float, optional): Minimum frequency in Hz. Defaults to 50.0. diff --git a/paddleaudio/paddleaudio/metric/__init__.py b/paddleaudio/paddleaudio/metric/__init__.py index 8e5ca9f75..d2b3a1360 100644 --- a/paddleaudio/paddleaudio/metric/__init__.py +++ b/paddleaudio/paddleaudio/metric/__init__.py @@ -14,4 +14,3 @@ from .dtw import dtw_distance from .eer import compute_eer from .eer import compute_minDCF -from .mcd import mcd_distance diff --git a/paddleaudio/paddleaudio/metric/mcd.py b/paddleaudio/paddleaudio/metric/mcd.py deleted file mode 100644 index 63a25fc23..000000000 --- a/paddleaudio/paddleaudio/metric/mcd.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Callable - -import mcd.metrics_fast as mt -import numpy as np -from mcd import dtw - -__all__ = [ - 'mcd_distance', -] - - -def mcd_distance(xs: np.ndarray, - ys: np.ndarray, - cost_fn: Callable=mt.logSpecDbDist) -> float: - """Mel cepstral distortion (MCD), dtw distance. - - Dynamic Time Warping. - Uses dynamic programming to compute: - - Examples: - .. code-block:: python - - wps[i, j] = cost_fn(xs[i], ys[j]) + min( - wps[i-1, j ], // vertical / insertion / expansion - wps[i , j-1], // horizontal / deletion / compression - wps[i-1, j-1]) // diagonal / match - - dtw = sqrt(wps[-1, -1]) - - Cost Function: - Examples: - .. code-block:: python - - logSpecDbConst = 10.0 / math.log(10.0) * math.sqrt(2.0) - - def logSpecDbDist(x, y): - diff = x - y - return logSpecDbConst * math.sqrt(np.inner(diff, diff)) - - Args: - xs (np.ndarray): ref sequence, [T,D] - ys (np.ndarray): hyp sequence, [T,D] - cost_fn (Callable, optional): Cost function. Defaults to mt.logSpecDbDist. - - Returns: - float: dtw distance - """ - - min_cost, path = dtw.dtw(xs, ys, cost_fn) - return min_cost diff --git a/paddleaudio/paddleaudio/utils/numeric.py b/paddleaudio/paddleaudio/utils/numeric.py new file mode 100644 index 000000000..126cada50 --- /dev/null +++ b/paddleaudio/paddleaudio/utils/numeric.py @@ -0,0 +1,30 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np + + +def pcm16to32(audio: np.ndarray) -> np.ndarray: + """pcm int16 to float32 + + Args: + audio (np.ndarray): Waveform with dtype of int16. + + Returns: + np.ndarray: Waveform with dtype of float32. + """ + if audio.dtype == np.int16: + audio = audio.astype("float32") + bits = np.iinfo(np.int16).bits + audio = audio / (2**(bits - 1)) + return audio diff --git a/paddleaudio/setup.py b/paddleaudio/setup.py index e08b88a3b..aac389302 100644 --- a/paddleaudio/setup.py +++ b/paddleaudio/setup.py @@ -19,7 +19,7 @@ from setuptools.command.install import install from setuptools.command.test import test # set the version here -VERSION = '0.2.0' +VERSION = '0.2.1' # Inspired by the example at https://pytest.org/latest/goodpractises.html @@ -83,8 +83,7 @@ setuptools.setup( python_requires='>=3.6', install_requires=[ 'numpy >= 1.15.0', 'scipy >= 1.0.0', 'resampy >= 0.2.2', - 'soundfile >= 0.9.0', 'colorlog', 'dtaidistance == 2.3.1', 'mcd >= 0.4', - 'pathos' + 'soundfile >= 0.9.0', 'colorlog', 'dtaidistance == 2.3.1', 'pathos' ], extras_require={ 'test': [ diff --git a/paddlespeech/cli/asr/infer.py b/paddlespeech/cli/asr/infer.py index 1fb4be434..49dd7b356 100644 --- a/paddlespeech/cli/asr/infer.py +++ b/paddlespeech/cli/asr/infer.py @@ -29,9 +29,10 @@ from ..download import get_path_from_url from ..executor import BaseExecutor from ..log import logger from ..utils import cli_register -from ..utils import download_and_decompress from ..utils import MODEL_HOME from ..utils import stats_wrapper +from .pretrained_models import model_alias +from .pretrained_models import pretrained_models from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer from paddlespeech.s2t.transform.transformation import Transformation from paddlespeech.s2t.utils.dynamic_import import dynamic_import @@ -39,94 +40,13 @@ from paddlespeech.s2t.utils.utility import UpdateConfig __all__ = ['ASRExecutor'] -pretrained_models = { - # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]". - # e.g. "conformer_wenetspeech-zh-16k" and "panns_cnn6-32k". - # Command line and python api use "{model_name}[_{dataset}]" as --model, usage: - # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav" - "conformer_wenetspeech-zh-16k": { - 'url': - 'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar.gz', - 'md5': - '76cb19ed857e6623856b7cd7ebbfeda4', - 'cfg_path': - 'model.yaml', - 'ckpt_path': - 'exp/conformer/checkpoints/wenetspeech', - }, - "transformer_librispeech-en-16k": { - 'url': - 'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz', - 'md5': - '2c667da24922aad391eacafe37bc1660', - 'cfg_path': - 'model.yaml', - 'ckpt_path': - 'exp/transformer/checkpoints/avg_10', - }, - "deepspeech2offline_aishell-zh-16k": { - 'url': - 'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz', - 'md5': - '932c3593d62fe5c741b59b31318aa314', - 'cfg_path': - 'model.yaml', - 'ckpt_path': - 'exp/deepspeech2/checkpoints/avg_1', - 'lm_url': - 'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm', - 'lm_md5': - '29e02312deb2e59b3c8686c7966d4fe3' - }, - "deepspeech2online_aishell-zh-16k": { - 'url': - 'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.1.1.model.tar.gz', - 'md5': - 'd5e076217cf60486519f72c217d21b9b', - 'cfg_path': - 'model.yaml', - 'ckpt_path': - 'exp/deepspeech2_online/checkpoints/avg_1', - 'lm_url': - 'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm', - 'lm_md5': - '29e02312deb2e59b3c8686c7966d4fe3' - }, - "deepspeech2offline_librispeech-en-16k": { - 'url': - 'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz', - 'md5': - 'f5666c81ad015c8de03aac2bc92e5762', - 'cfg_path': - 'model.yaml', - 'ckpt_path': - 'exp/deepspeech2/checkpoints/avg_1', - 'lm_url': - 'https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm', - 'lm_md5': - '099a601759d467cd0a8523ff939819c5' - }, -} - -model_alias = { - "deepspeech2offline": - "paddlespeech.s2t.models.ds2:DeepSpeech2Model", - "deepspeech2online": - "paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline", - "conformer": - "paddlespeech.s2t.models.u2:U2Model", - "transformer": - "paddlespeech.s2t.models.u2:U2Model", - "wenetspeech": - "paddlespeech.s2t.models.u2:U2Model", -} - - @cli_register( name='paddlespeech.asr', description='Speech to text infer command.') class ASRExecutor(BaseExecutor): def __init__(self): - super(ASRExecutor, self).__init__() + super().__init__() + self.model_alias = model_alias + self.pretrained_models = pretrained_models self.parser = argparse.ArgumentParser( prog='paddlespeech.asr', add_help=True) @@ -136,7 +56,9 @@ class ASRExecutor(BaseExecutor): '--model', type=str, default='conformer_wenetspeech', - choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()], + choices=[ + tag[:tag.index('-')] for tag in self.pretrained_models.keys() + ], help='Choose model type of asr task.') self.parser.add_argument( '--lang', @@ -192,23 +114,6 @@ class ASRExecutor(BaseExecutor): action='store_true', help='Increase logger verbosity of current task.') - def _get_pretrained_path(self, tag: str) -> os.PathLike: - """ - Download and returns pretrained resources path of current task. - """ - support_models = list(pretrained_models.keys()) - assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format( - tag, '\n\t\t'.join(support_models)) - - res_path = os.path.join(MODEL_HOME, tag) - decompressed_path = download_and_decompress(pretrained_models[tag], - res_path) - decompressed_path = os.path.abspath(decompressed_path) - logger.info( - 'Use pretrained model stored in: {}'.format(decompressed_path)) - - return decompressed_path - def _init_from_path(self, model_type: str='wenetspeech', lang: str='zh', @@ -219,6 +124,7 @@ class ASRExecutor(BaseExecutor): """ Init model and other resources from a specific path. """ + logger.info("start to init the model") if hasattr(self, 'model'): logger.info('Model had been initialized.') return @@ -228,19 +134,21 @@ class ASRExecutor(BaseExecutor): tag = model_type + '-' + lang + '-' + sample_rate_str res_path = self._get_pretrained_path(tag) # wenetspeech_zh self.res_path = res_path - self.cfg_path = os.path.join(res_path, - pretrained_models[tag]['cfg_path']) + self.cfg_path = os.path.join( + res_path, self.pretrained_models[tag]['cfg_path']) self.ckpt_path = os.path.join( - res_path, pretrained_models[tag]['ckpt_path'] + ".pdparams") + res_path, + self.pretrained_models[tag]['ckpt_path'] + ".pdparams") logger.info(res_path) - logger.info(self.cfg_path) - logger.info(self.ckpt_path) + else: self.cfg_path = os.path.abspath(cfg_path) self.ckpt_path = os.path.abspath(ckpt_path + ".pdparams") self.res_path = os.path.dirname( os.path.dirname(os.path.abspath(self.cfg_path))) - + logger.info(self.cfg_path) + logger.info(self.ckpt_path) + #Init body. self.config = CfgNode(new_allowed=True) self.config.merge_from_file(self.cfg_path) @@ -255,8 +163,8 @@ class ASRExecutor(BaseExecutor): self.collate_fn_test = SpeechCollator.from_config(self.config) self.text_feature = TextFeaturizer( unit_type=self.config.unit_type, vocab=self.vocab) - lm_url = pretrained_models[tag]['lm_url'] - lm_md5 = pretrained_models[tag]['lm_md5'] + lm_url = self.pretrained_models[tag]['lm_url'] + lm_md5 = self.pretrained_models[tag]['lm_md5'] self.download_lm( lm_url, os.path.dirname(self.config.decode.lang_model_path), lm_md5) @@ -269,12 +177,11 @@ class ASRExecutor(BaseExecutor): vocab=self.config.vocab_filepath, spm_model_prefix=self.config.spm_model_prefix) self.config.decode.decoding_method = decode_method - else: raise Exception("wrong type") model_name = model_type[:model_type.rindex( '_')] # model_type: {model_name}_{dataset} - model_class = dynamic_import(model_name, model_alias) + model_class = dynamic_import(model_name, self.model_alias) model_conf = self.config model = model_class.from_config(model_conf) self.model = model @@ -347,12 +254,14 @@ class ASRExecutor(BaseExecutor): else: raise Exception("wrong type") + logger.info("audio feat process success") + @paddle.no_grad() def infer(self, model_type: str): """ Model inference and result stored in self.output. """ - + logger.info("start to infer the model to get the output") cfg = self.config.decode audio = self._inputs["audio"] audio_len = self._inputs["audio_len"] @@ -369,17 +278,22 @@ class ASRExecutor(BaseExecutor): self._outputs["result"] = result_transcripts[0] elif "conformer" in model_type or "transformer" in model_type: - result_transcripts = self.model.decode( - audio, - audio_len, - text_feature=self.text_feature, - decoding_method=cfg.decoding_method, - beam_size=cfg.beam_size, - ctc_weight=cfg.ctc_weight, - decoding_chunk_size=cfg.decoding_chunk_size, - num_decoding_left_chunks=cfg.num_decoding_left_chunks, - simulate_streaming=cfg.simulate_streaming) - self._outputs["result"] = result_transcripts[0][0] + logger.info(f"we will use the transformer like model : {model_type}") + try: + result_transcripts = self.model.decode( + audio, + audio_len, + text_feature=self.text_feature, + decoding_method=cfg.decoding_method, + beam_size=cfg.beam_size, + ctc_weight=cfg.ctc_weight, + decoding_chunk_size=cfg.decoding_chunk_size, + num_decoding_left_chunks=cfg.num_decoding_left_chunks, + simulate_streaming=cfg.simulate_streaming) + self._outputs["result"] = result_transcripts[0][0] + except Exception as e: + logger.exception(e) + else: raise Exception("invalid model name") @@ -426,6 +340,11 @@ class ASRExecutor(BaseExecutor): try: audio, audio_sample_rate = soundfile.read( audio_file, dtype="int16", always_2d=True) + audio_duration = audio.shape[0] / audio_sample_rate + max_duration = 50.0 + if audio_duration >= max_duration: + logger.error("Please input audio file less then 50 seconds.\n") + return except Exception as e: logger.exception(e) logger.error( diff --git a/paddlespeech/cli/asr/pretrained_models.py b/paddlespeech/cli/asr/pretrained_models.py new file mode 100644 index 000000000..cc52c751b --- /dev/null +++ b/paddlespeech/cli/asr/pretrained_models.py @@ -0,0 +1,97 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +pretrained_models = { + # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]". + # e.g. "conformer_wenetspeech-zh-16k" and "panns_cnn6-32k". + # Command line and python api use "{model_name}[_{dataset}]" as --model, usage: + # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav" + "conformer_wenetspeech-zh-16k": { + 'url': + 'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar.gz', + 'md5': + '76cb19ed857e6623856b7cd7ebbfeda4', + 'cfg_path': + 'model.yaml', + 'ckpt_path': + 'exp/conformer/checkpoints/wenetspeech', + }, + "transformer_librispeech-en-16k": { + 'url': + 'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz', + 'md5': + '2c667da24922aad391eacafe37bc1660', + 'cfg_path': + 'model.yaml', + 'ckpt_path': + 'exp/transformer/checkpoints/avg_10', + }, + "deepspeech2offline_aishell-zh-16k": { + 'url': + 'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz', + 'md5': + '932c3593d62fe5c741b59b31318aa314', + 'cfg_path': + 'model.yaml', + 'ckpt_path': + 'exp/deepspeech2/checkpoints/avg_1', + 'lm_url': + 'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm', + 'lm_md5': + '29e02312deb2e59b3c8686c7966d4fe3' + }, + "deepspeech2online_aishell-zh-16k": { + 'url': + 'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz', + 'md5': + '23e16c69730a1cb5d735c98c83c21e16', + 'cfg_path': + 'model.yaml', + 'ckpt_path': + 'exp/deepspeech2_online/checkpoints/avg_1', + 'lm_url': + 'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm', + 'lm_md5': + '29e02312deb2e59b3c8686c7966d4fe3' + }, + "deepspeech2offline_librispeech-en-16k": { + 'url': + 'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz', + 'md5': + 'f5666c81ad015c8de03aac2bc92e5762', + 'cfg_path': + 'model.yaml', + 'ckpt_path': + 'exp/deepspeech2/checkpoints/avg_1', + 'lm_url': + 'https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm', + 'lm_md5': + '099a601759d467cd0a8523ff939819c5' + }, +} + +model_alias = { + "deepspeech2offline": + "paddlespeech.s2t.models.ds2:DeepSpeech2Model", + "deepspeech2online": + "paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline", + "conformer": + "paddlespeech.s2t.models.u2:U2Model", + "conformer_online": + "paddlespeech.s2t.models.u2:U2Model", + "transformer": + "paddlespeech.s2t.models.u2:U2Model", + "wenetspeech": + "paddlespeech.s2t.models.u2:U2Model", +} diff --git a/paddlespeech/cli/cls/infer.py b/paddlespeech/cli/cls/infer.py index f56d8a579..1f637a8fe 100644 --- a/paddlespeech/cli/cls/infer.py +++ b/paddlespeech/cli/cls/infer.py @@ -25,55 +25,23 @@ import yaml from ..executor import BaseExecutor from ..log import logger from ..utils import cli_register -from ..utils import download_and_decompress -from ..utils import MODEL_HOME from ..utils import stats_wrapper +from .pretrained_models import model_alias +from .pretrained_models import pretrained_models from paddleaudio import load from paddleaudio.features import LogMelSpectrogram from paddlespeech.s2t.utils.dynamic_import import dynamic_import __all__ = ['CLSExecutor'] -pretrained_models = { - # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]". - # e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k". - # Command line and python api use "{model_name}[_{dataset}]" as --model, usage: - # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav" - "panns_cnn6-32k": { - 'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn6.tar.gz', - 'md5': '4cf09194a95df024fd12f84712cf0f9c', - 'cfg_path': 'panns.yaml', - 'ckpt_path': 'cnn6.pdparams', - 'label_file': 'audioset_labels.txt', - }, - "panns_cnn10-32k": { - 'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn10.tar.gz', - 'md5': 'cb8427b22176cc2116367d14847f5413', - 'cfg_path': 'panns.yaml', - 'ckpt_path': 'cnn10.pdparams', - 'label_file': 'audioset_labels.txt', - }, - "panns_cnn14-32k": { - 'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn14.tar.gz', - 'md5': 'e3b9b5614a1595001161d0ab95edee97', - 'cfg_path': 'panns.yaml', - 'ckpt_path': 'cnn14.pdparams', - 'label_file': 'audioset_labels.txt', - }, -} - -model_alias = { - "panns_cnn6": "paddlespeech.cls.models.panns:CNN6", - "panns_cnn10": "paddlespeech.cls.models.panns:CNN10", - "panns_cnn14": "paddlespeech.cls.models.panns:CNN14", -} - @cli_register( name='paddlespeech.cls', description='Audio classification infer command.') class CLSExecutor(BaseExecutor): def __init__(self): - super(CLSExecutor, self).__init__() + super().__init__() + self.model_alias = model_alias + self.pretrained_models = pretrained_models self.parser = argparse.ArgumentParser( prog='paddlespeech.cls', add_help=True) @@ -83,7 +51,9 @@ class CLSExecutor(BaseExecutor): '--model', type=str, default='panns_cnn14', - choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()], + choices=[ + tag[:tag.index('-')] for tag in self.pretrained_models.keys() + ], help='Choose model type of cls task.') self.parser.add_argument( '--config', @@ -121,23 +91,6 @@ class CLSExecutor(BaseExecutor): action='store_true', help='Increase logger verbosity of current task.') - def _get_pretrained_path(self, tag: str) -> os.PathLike: - """ - Download and returns pretrained resources path of current task. - """ - support_models = list(pretrained_models.keys()) - assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format( - tag, '\n\t\t'.join(support_models)) - - res_path = os.path.join(MODEL_HOME, tag) - decompressed_path = download_and_decompress(pretrained_models[tag], - res_path) - decompressed_path = os.path.abspath(decompressed_path) - logger.info( - 'Use pretrained model stored in: {}'.format(decompressed_path)) - - return decompressed_path - def _init_from_path(self, model_type: str='panns_cnn14', cfg_path: Optional[os.PathLike]=None, @@ -153,12 +106,12 @@ class CLSExecutor(BaseExecutor): if label_file is None or ckpt_path is None: tag = model_type + '-' + '32k' # panns_cnn14-32k self.res_path = self._get_pretrained_path(tag) - self.cfg_path = os.path.join(self.res_path, - pretrained_models[tag]['cfg_path']) - self.label_file = os.path.join(self.res_path, - pretrained_models[tag]['label_file']) - self.ckpt_path = os.path.join(self.res_path, - pretrained_models[tag]['ckpt_path']) + self.cfg_path = os.path.join( + self.res_path, self.pretrained_models[tag]['cfg_path']) + self.label_file = os.path.join( + self.res_path, self.pretrained_models[tag]['label_file']) + self.ckpt_path = os.path.join( + self.res_path, self.pretrained_models[tag]['ckpt_path']) else: self.cfg_path = os.path.abspath(cfg_path) self.label_file = os.path.abspath(label_file) @@ -175,7 +128,7 @@ class CLSExecutor(BaseExecutor): self._label_list.append(line.strip()) # model - model_class = dynamic_import(model_type, model_alias) + model_class = dynamic_import(model_type, self.model_alias) model_dict = paddle.load(self.ckpt_path) self.model = model_class(extract_embedding=False) self.model.set_state_dict(model_dict) diff --git a/paddlespeech/cli/cls/pretrained_models.py b/paddlespeech/cli/cls/pretrained_models.py new file mode 100644 index 000000000..1d66850aa --- /dev/null +++ b/paddlespeech/cli/cls/pretrained_models.py @@ -0,0 +1,47 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +pretrained_models = { + # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]". + # e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k". + # Command line and python api use "{model_name}[_{dataset}]" as --model, usage: + # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav" + "panns_cnn6-32k": { + 'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn6.tar.gz', + 'md5': '4cf09194a95df024fd12f84712cf0f9c', + 'cfg_path': 'panns.yaml', + 'ckpt_path': 'cnn6.pdparams', + 'label_file': 'audioset_labels.txt', + }, + "panns_cnn10-32k": { + 'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn10.tar.gz', + 'md5': 'cb8427b22176cc2116367d14847f5413', + 'cfg_path': 'panns.yaml', + 'ckpt_path': 'cnn10.pdparams', + 'label_file': 'audioset_labels.txt', + }, + "panns_cnn14-32k": { + 'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn14.tar.gz', + 'md5': 'e3b9b5614a1595001161d0ab95edee97', + 'cfg_path': 'panns.yaml', + 'ckpt_path': 'cnn14.pdparams', + 'label_file': 'audioset_labels.txt', + }, +} + +model_alias = { + "panns_cnn6": "paddlespeech.cls.models.panns:CNN6", + "panns_cnn10": "paddlespeech.cls.models.panns:CNN10", + "panns_cnn14": "paddlespeech.cls.models.panns:CNN14", +} diff --git a/paddlespeech/cli/executor.py b/paddlespeech/cli/executor.py index 064939a85..df0b67838 100644 --- a/paddlespeech/cli/executor.py +++ b/paddlespeech/cli/executor.py @@ -25,6 +25,8 @@ from typing import Union import paddle from .log import logger +from .utils import download_and_decompress +from .utils import MODEL_HOME class BaseExecutor(ABC): @@ -35,19 +37,8 @@ class BaseExecutor(ABC): def __init__(self): self._inputs = OrderedDict() self._outputs = OrderedDict() - - @abstractmethod - def _get_pretrained_path(self, tag: str) -> os.PathLike: - """ - Download and returns pretrained resources path of current task. - - Args: - tag (str): A tag of pretrained model. - - Returns: - os.PathLike: The path on which resources of pretrained model locate. - """ - pass + self.pretrained_models = OrderedDict() + self.model_alias = OrderedDict() @abstractmethod def _init_from_path(self, *args, **kwargs): @@ -227,3 +218,20 @@ class BaseExecutor(ABC): ] for l in loggers: l.disabled = True + + def _get_pretrained_path(self, tag: str) -> os.PathLike: + """ + Download and returns pretrained resources path of current task. + """ + support_models = list(self.pretrained_models.keys()) + assert tag in self.pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format( + tag, '\n\t\t'.join(support_models)) + + res_path = os.path.join(MODEL_HOME, tag) + decompressed_path = download_and_decompress(self.pretrained_models[tag], + res_path) + decompressed_path = os.path.abspath(decompressed_path) + logger.info( + 'Use pretrained model stored in: {}'.format(decompressed_path)) + + return decompressed_path diff --git a/paddlespeech/cli/st/infer.py b/paddlespeech/cli/st/infer.py index e64fc57d1..29d95f799 100644 --- a/paddlespeech/cli/st/infer.py +++ b/paddlespeech/cli/st/infer.py @@ -32,40 +32,24 @@ from ..utils import cli_register from ..utils import download_and_decompress from ..utils import MODEL_HOME from ..utils import stats_wrapper +from .pretrained_models import kaldi_bins +from .pretrained_models import model_alias +from .pretrained_models import pretrained_models from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer from paddlespeech.s2t.utils.dynamic_import import dynamic_import from paddlespeech.s2t.utils.utility import UpdateConfig __all__ = ["STExecutor"] -pretrained_models = { - "fat_st_ted-en-zh": { - "url": - "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/st1_transformer_mtl_noam_ted-en-zh_ckpt_0.1.1.model.tar.gz", - "md5": - "d62063f35a16d91210a71081bd2dd557", - "cfg_path": - "model.yaml", - "ckpt_path": - "exp/transformer_mtl_noam/checkpoints/fat_st_ted-en-zh.pdparams", - } -} - -model_alias = {"fat_st": "paddlespeech.s2t.models.u2_st:U2STModel"} - -kaldi_bins = { - "url": - "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz", - "md5": - "c0682303b3f3393dbf6ed4c4e35a53eb", -} - @cli_register( name="paddlespeech.st", description="Speech translation infer command.") class STExecutor(BaseExecutor): def __init__(self): - super(STExecutor, self).__init__() + super().__init__() + self.model_alias = model_alias + self.pretrained_models = pretrained_models + self.kaldi_bins = kaldi_bins self.parser = argparse.ArgumentParser( prog="paddlespeech.st", add_help=True) @@ -75,7 +59,9 @@ class STExecutor(BaseExecutor): "--model", type=str, default="fat_st_ted", - choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()], + choices=[ + tag[:tag.index('-')] for tag in self.pretrained_models.keys() + ], help="Choose model type of st task.") self.parser.add_argument( "--src_lang", @@ -119,28 +105,11 @@ class STExecutor(BaseExecutor): action='store_true', help='Increase logger verbosity of current task.') - def _get_pretrained_path(self, tag: str) -> os.PathLike: - """ - Download and returns pretrained resources path of current task. - """ - support_models = list(pretrained_models.keys()) - assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format( - tag, '\n\t\t'.join(support_models)) - - res_path = os.path.join(MODEL_HOME, tag) - decompressed_path = download_and_decompress(pretrained_models[tag], - res_path) - decompressed_path = os.path.abspath(decompressed_path) - logger.info( - "Use pretrained model stored in: {}".format(decompressed_path)) - - return decompressed_path - def _set_kaldi_bins(self) -> os.PathLike: """ Download and returns kaldi_bins resources path of current task. """ - decompressed_path = download_and_decompress(kaldi_bins, MODEL_HOME) + decompressed_path = download_and_decompress(self.kaldi_bins, MODEL_HOME) decompressed_path = os.path.abspath(decompressed_path) logger.info("Kaldi_bins stored in: {}".format(decompressed_path)) if "LD_LIBRARY_PATH" in os.environ: @@ -197,7 +166,7 @@ class STExecutor(BaseExecutor): model_conf = self.config model_name = model_type[:model_type.rindex( '_')] # model_type: {model_name}_{dataset} - model_class = dynamic_import(model_name, model_alias) + model_class = dynamic_import(model_name, self.model_alias) self.model = model_class.from_config(model_conf) self.model.eval() diff --git a/paddlespeech/cli/st/pretrained_models.py b/paddlespeech/cli/st/pretrained_models.py new file mode 100644 index 000000000..cc7410d25 --- /dev/null +++ b/paddlespeech/cli/st/pretrained_models.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +pretrained_models = { + "fat_st_ted-en-zh": { + "url": + "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/st1_transformer_mtl_noam_ted-en-zh_ckpt_0.1.1.model.tar.gz", + "md5": + "d62063f35a16d91210a71081bd2dd557", + "cfg_path": + "model.yaml", + "ckpt_path": + "exp/transformer_mtl_noam/checkpoints/fat_st_ted-en-zh.pdparams", + } +} + +model_alias = {"fat_st": "paddlespeech.s2t.models.u2_st:U2STModel"} + +kaldi_bins = { + "url": + "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz", + "md5": + "c0682303b3f3393dbf6ed4c4e35a53eb", +} diff --git a/paddlespeech/cli/stats/infer.py b/paddlespeech/cli/stats/infer.py index 4ef50449c..7cf4f2368 100644 --- a/paddlespeech/cli/stats/infer.py +++ b/paddlespeech/cli/stats/infer.py @@ -16,7 +16,6 @@ from typing import List from prettytable import PrettyTable -from ..log import logger from ..utils import cli_register from ..utils import stats_wrapper @@ -27,7 +26,8 @@ model_name_format = { 'cls': 'Model-Sample Rate', 'st': 'Model-Source language-Target language', 'text': 'Model-Task-Language', - 'tts': 'Model-Language' + 'tts': 'Model-Language', + 'vector': 'Model-Sample Rate' } @@ -36,18 +36,18 @@ model_name_format = { description='Get speech tasks support models list.') class StatsExecutor(): def __init__(self): - super(StatsExecutor, self).__init__() + super().__init__() self.parser = argparse.ArgumentParser( prog='paddlespeech.stats', add_help=True) + self.task_choices = ['asr', 'cls', 'st', 'text', 'tts', 'vector'] self.parser.add_argument( '--task', type=str, default='asr', - choices=['asr', 'cls', 'st', 'text', 'tts'], + choices=self.task_choices, help='Choose speech task.', required=True) - self.task_choices = ['asr', 'cls', 'st', 'text', 'tts'] def show_support_models(self, pretrained_models: dict): fields = model_name_format[self.task].split("-") @@ -61,73 +61,15 @@ class StatsExecutor(): Command line entry. """ parser_args = self.parser.parse_args(argv) - self.task = parser_args.task - if self.task not in self.task_choices: - logger.error( - "Please input correct speech task, choices = ['asr', 'cls', 'st', 'text', 'tts']" - ) + has_exceptions = False + try: + self(parser_args.task) + except Exception as e: + has_exceptions = True + if has_exceptions: return False - - elif self.task == 'asr': - try: - from ..asr.infer import pretrained_models - logger.info( - "Here is the list of ASR pretrained models released by PaddleSpeech that can be used by command line and python API" - ) - self.show_support_models(pretrained_models) - return True - except BaseException: - logger.error("Failed to get the list of ASR pretrained models.") - return False - - elif self.task == 'cls': - try: - from ..cls.infer import pretrained_models - logger.info( - "Here is the list of CLS pretrained models released by PaddleSpeech that can be used by command line and python API" - ) - self.show_support_models(pretrained_models) - return True - except BaseException: - logger.error("Failed to get the list of CLS pretrained models.") - return False - - elif self.task == 'st': - try: - from ..st.infer import pretrained_models - logger.info( - "Here is the list of ST pretrained models released by PaddleSpeech that can be used by command line and python API" - ) - self.show_support_models(pretrained_models) - return True - except BaseException: - logger.error("Failed to get the list of ST pretrained models.") - return False - - elif self.task == 'text': - try: - from ..text.infer import pretrained_models - logger.info( - "Here is the list of TEXT pretrained models released by PaddleSpeech that can be used by command line and python API" - ) - self.show_support_models(pretrained_models) - return True - except BaseException: - logger.error( - "Failed to get the list of TEXT pretrained models.") - return False - - elif self.task == 'tts': - try: - from ..tts.infer import pretrained_models - logger.info( - "Here is the list of TTS pretrained models released by PaddleSpeech that can be used by command line and python API" - ) - self.show_support_models(pretrained_models) - return True - except BaseException: - logger.error("Failed to get the list of TTS pretrained models.") - return False + else: + return True @stats_wrapper def __call__( @@ -138,13 +80,12 @@ class StatsExecutor(): """ self.task = task if self.task not in self.task_choices: - print( - "Please input correct speech task, choices = ['asr', 'cls', 'st', 'text', 'tts']" - ) + print("Please input correct speech task, choices = " + str( + self.task_choices)) elif self.task == 'asr': try: - from ..asr.infer import pretrained_models + from ..asr.pretrained_models import pretrained_models print( "Here is the list of ASR pretrained models released by PaddleSpeech that can be used by command line and python API" ) @@ -154,7 +95,7 @@ class StatsExecutor(): elif self.task == 'cls': try: - from ..cls.infer import pretrained_models + from ..cls.pretrained_models import pretrained_models print( "Here is the list of CLS pretrained models released by PaddleSpeech that can be used by command line and python API" ) @@ -164,7 +105,7 @@ class StatsExecutor(): elif self.task == 'st': try: - from ..st.infer import pretrained_models + from ..st.pretrained_models import pretrained_models print( "Here is the list of ST pretrained models released by PaddleSpeech that can be used by command line and python API" ) @@ -174,7 +115,7 @@ class StatsExecutor(): elif self.task == 'text': try: - from ..text.infer import pretrained_models + from ..text.pretrained_models import pretrained_models print( "Here is the list of TEXT pretrained models released by PaddleSpeech that can be used by command line and python API" ) @@ -184,10 +125,22 @@ class StatsExecutor(): elif self.task == 'tts': try: - from ..tts.infer import pretrained_models + from ..tts.pretrained_models import pretrained_models print( "Here is the list of TTS pretrained models released by PaddleSpeech that can be used by command line and python API" ) self.show_support_models(pretrained_models) except BaseException: print("Failed to get the list of TTS pretrained models.") + + elif self.task == 'vector': + try: + from ..vector.pretrained_models import pretrained_models + print( + "Here is the list of Speaker Recognition pretrained models released by PaddleSpeech that can be used by command line and python API" + ) + self.show_support_models(pretrained_models) + except BaseException: + print( + "Failed to get the list of Speaker Recognition pretrained models." + ) diff --git a/paddlespeech/cli/text/infer.py b/paddlespeech/cli/text/infer.py index dcf306c69..69e62e4b4 100644 --- a/paddlespeech/cli/text/infer.py +++ b/paddlespeech/cli/text/infer.py @@ -25,58 +25,21 @@ from ...s2t.utils.dynamic_import import dynamic_import from ..executor import BaseExecutor from ..log import logger from ..utils import cli_register -from ..utils import download_and_decompress -from ..utils import MODEL_HOME from ..utils import stats_wrapper +from .pretrained_models import model_alias +from .pretrained_models import pretrained_models +from .pretrained_models import tokenizer_alias __all__ = ['TextExecutor'] -pretrained_models = { - # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]". - # e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k". - # Command line and python api use "{model_name}[_{dataset}]" as --model, usage: - # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav" - "ernie_linear_p7_wudao-punc-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p7_wudao-punc-zh.tar.gz', - 'md5': - '12283e2ddde1797c5d1e57036b512746', - 'cfg_path': - 'ckpt/model_config.json', - 'ckpt_path': - 'ckpt/model_state.pdparams', - 'vocab_file': - 'punc_vocab.txt', - }, - "ernie_linear_p3_wudao-punc-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_wudao-punc-zh.tar.gz', - 'md5': - '448eb2fdf85b6a997e7e652e80c51dd2', - 'cfg_path': - 'ckpt/model_config.json', - 'ckpt_path': - 'ckpt/model_state.pdparams', - 'vocab_file': - 'punc_vocab.txt', - }, -} - -model_alias = { - "ernie_linear_p7": "paddlespeech.text.models:ErnieLinear", - "ernie_linear_p3": "paddlespeech.text.models:ErnieLinear", -} - -tokenizer_alias = { - "ernie_linear_p7": "paddlenlp.transformers:ErnieTokenizer", - "ernie_linear_p3": "paddlenlp.transformers:ErnieTokenizer", -} - @cli_register(name='paddlespeech.text', description='Text infer command.') class TextExecutor(BaseExecutor): def __init__(self): - super(TextExecutor, self).__init__() + super().__init__() + self.model_alias = model_alias + self.pretrained_models = pretrained_models + self.tokenizer_alias = tokenizer_alias self.parser = argparse.ArgumentParser( prog='paddlespeech.text', add_help=True) @@ -92,7 +55,9 @@ class TextExecutor(BaseExecutor): '--model', type=str, default='ernie_linear_p7_wudao', - choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()], + choices=[ + tag[:tag.index('-')] for tag in self.pretrained_models.keys() + ], help='Choose model type of text task.') self.parser.add_argument( '--lang', @@ -131,23 +96,6 @@ class TextExecutor(BaseExecutor): action='store_true', help='Increase logger verbosity of current task.') - def _get_pretrained_path(self, tag: str) -> os.PathLike: - """ - Download and returns pretrained resources path of current task. - """ - support_models = list(pretrained_models.keys()) - assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format( - tag, '\n\t\t'.join(support_models)) - - res_path = os.path.join(MODEL_HOME, tag) - decompressed_path = download_and_decompress(pretrained_models[tag], - res_path) - decompressed_path = os.path.abspath(decompressed_path) - logger.info( - 'Use pretrained model stored in: {}'.format(decompressed_path)) - - return decompressed_path - def _init_from_path(self, task: str='punc', model_type: str='ernie_linear_p7_wudao', @@ -167,12 +115,12 @@ class TextExecutor(BaseExecutor): if cfg_path is None or ckpt_path is None or vocab_file is None: tag = '-'.join([model_type, task, lang]) self.res_path = self._get_pretrained_path(tag) - self.cfg_path = os.path.join(self.res_path, - pretrained_models[tag]['cfg_path']) - self.ckpt_path = os.path.join(self.res_path, - pretrained_models[tag]['ckpt_path']) - self.vocab_file = os.path.join(self.res_path, - pretrained_models[tag]['vocab_file']) + self.cfg_path = os.path.join( + self.res_path, self.pretrained_models[tag]['cfg_path']) + self.ckpt_path = os.path.join( + self.res_path, self.pretrained_models[tag]['ckpt_path']) + self.vocab_file = os.path.join( + self.res_path, self.pretrained_models[tag]['vocab_file']) else: self.cfg_path = os.path.abspath(cfg_path) self.ckpt_path = os.path.abspath(ckpt_path) @@ -187,8 +135,8 @@ class TextExecutor(BaseExecutor): self._punc_list.append(line.strip()) # model - model_class = dynamic_import(model_name, model_alias) - tokenizer_class = dynamic_import(model_name, tokenizer_alias) + model_class = dynamic_import(model_name, self.model_alias) + tokenizer_class = dynamic_import(model_name, self.tokenizer_alias) self.model = model_class( cfg_path=self.cfg_path, ckpt_path=self.ckpt_path) self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0') diff --git a/paddlespeech/cli/text/pretrained_models.py b/paddlespeech/cli/text/pretrained_models.py new file mode 100644 index 000000000..817d3caa3 --- /dev/null +++ b/paddlespeech/cli/text/pretrained_models.py @@ -0,0 +1,54 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +pretrained_models = { + # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]". + # e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k". + # Command line and python api use "{model_name}[_{dataset}]" as --model, usage: + # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav" + "ernie_linear_p7_wudao-punc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p7_wudao-punc-zh.tar.gz', + 'md5': + '12283e2ddde1797c5d1e57036b512746', + 'cfg_path': + 'ckpt/model_config.json', + 'ckpt_path': + 'ckpt/model_state.pdparams', + 'vocab_file': + 'punc_vocab.txt', + }, + "ernie_linear_p3_wudao-punc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_wudao-punc-zh.tar.gz', + 'md5': + '448eb2fdf85b6a997e7e652e80c51dd2', + 'cfg_path': + 'ckpt/model_config.json', + 'ckpt_path': + 'ckpt/model_state.pdparams', + 'vocab_file': + 'punc_vocab.txt', + }, +} + +model_alias = { + "ernie_linear_p7": "paddlespeech.text.models:ErnieLinear", + "ernie_linear_p3": "paddlespeech.text.models:ErnieLinear", +} + +tokenizer_alias = { + "ernie_linear_p7": "paddlenlp.transformers:ErnieTokenizer", + "ernie_linear_p3": "paddlenlp.transformers:ErnieTokenizer", +} diff --git a/paddlespeech/cli/tts/infer.py b/paddlespeech/cli/tts/infer.py index c7a1edc93..1c7199306 100644 --- a/paddlespeech/cli/tts/infer.py +++ b/paddlespeech/cli/tts/infer.py @@ -29,9 +29,9 @@ from yacs.config import CfgNode from ..executor import BaseExecutor from ..log import logger from ..utils import cli_register -from ..utils import download_and_decompress -from ..utils import MODEL_HOME from ..utils import stats_wrapper +from .pretrained_models import model_alias +from .pretrained_models import pretrained_models from paddlespeech.s2t.utils.dynamic_import import dynamic_import from paddlespeech.t2s.frontend import English from paddlespeech.t2s.frontend.zh_frontend import Frontend @@ -39,299 +39,14 @@ from paddlespeech.t2s.modules.normalizer import ZScore __all__ = ['TTSExecutor'] -pretrained_models = { - # speedyspeech - "speedyspeech_csmsc-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_ckpt_0.5.zip', - 'md5': - '9edce23b1a87f31b814d9477bf52afbc', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_11400.pdz', - 'speech_stats': - 'feats_stats.npy', - 'phones_dict': - 'phone_id_map.txt', - 'tones_dict': - 'tone_id_map.txt', - }, - - # fastspeech2 - "fastspeech2_csmsc-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip', - 'md5': - '637d28a5e53aa60275612ba4393d5f22', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_76000.pdz', - 'speech_stats': - 'speech_stats.npy', - 'phones_dict': - 'phone_id_map.txt', - }, - "fastspeech2_ljspeech-en": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip', - 'md5': - 'ffed800c93deaf16ca9b3af89bfcd747', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_100000.pdz', - 'speech_stats': - 'speech_stats.npy', - 'phones_dict': - 'phone_id_map.txt', - }, - "fastspeech2_aishell3-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip', - 'md5': - 'f4dd4a5f49a4552b77981f544ab3392e', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_96400.pdz', - 'speech_stats': - 'speech_stats.npy', - 'phones_dict': - 'phone_id_map.txt', - 'speaker_dict': - 'speaker_id_map.txt', - }, - "fastspeech2_vctk-en": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip', - 'md5': - '743e5024ca1e17a88c5c271db9779ba4', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_66200.pdz', - 'speech_stats': - 'speech_stats.npy', - 'phones_dict': - 'phone_id_map.txt', - 'speaker_dict': - 'speaker_id_map.txt', - }, - # tacotron2 - "tacotron2_csmsc-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip', - 'md5': - '0df4b6f0bcbe0d73c5ed6df8867ab91a', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_30600.pdz', - 'speech_stats': - 'speech_stats.npy', - 'phones_dict': - 'phone_id_map.txt', - }, - "tacotron2_ljspeech-en": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.2.0.zip', - 'md5': - '6a5eddd81ae0e81d16959b97481135f3', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_60300.pdz', - 'speech_stats': - 'speech_stats.npy', - 'phones_dict': - 'phone_id_map.txt', - }, - - # pwgan - "pwgan_csmsc-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip', - 'md5': - '2e481633325b5bdf0a3823c714d2c117', - 'config': - 'pwg_default.yaml', - 'ckpt': - 'pwg_snapshot_iter_400000.pdz', - 'speech_stats': - 'pwg_stats.npy', - }, - "pwgan_ljspeech-en": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip', - 'md5': - '53610ba9708fd3008ccaf8e99dacbaf0', - 'config': - 'pwg_default.yaml', - 'ckpt': - 'pwg_snapshot_iter_400000.pdz', - 'speech_stats': - 'pwg_stats.npy', - }, - "pwgan_aishell3-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip', - 'md5': - 'd7598fa41ad362d62f85ffc0f07e3d84', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_1000000.pdz', - 'speech_stats': - 'feats_stats.npy', - }, - "pwgan_vctk-en": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip', - 'md5': - 'b3da1defcde3e578be71eb284cb89f2c', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_1500000.pdz', - 'speech_stats': - 'feats_stats.npy', - }, - # mb_melgan - "mb_melgan_csmsc-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip', - 'md5': - 'ee5f0604e20091f0d495b6ec4618b90d', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_1000000.pdz', - 'speech_stats': - 'feats_stats.npy', - }, - # style_melgan - "style_melgan_csmsc-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip', - 'md5': - '5de2d5348f396de0c966926b8c462755', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_1500000.pdz', - 'speech_stats': - 'feats_stats.npy', - }, - # hifigan - "hifigan_csmsc-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip', - 'md5': - 'dd40a3d88dfcf64513fba2f0f961ada6', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_2500000.pdz', - 'speech_stats': - 'feats_stats.npy', - }, - "hifigan_ljspeech-en": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_ckpt_0.2.0.zip', - 'md5': - '70e9131695decbca06a65fe51ed38a72', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_2500000.pdz', - 'speech_stats': - 'feats_stats.npy', - }, - "hifigan_aishell3-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip', - 'md5': - '3bb49bc75032ed12f79c00c8cc79a09a', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_2500000.pdz', - 'speech_stats': - 'feats_stats.npy', - }, - "hifigan_vctk-en": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip', - 'md5': - '7da8f88359bca2457e705d924cf27bd4', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_2500000.pdz', - 'speech_stats': - 'feats_stats.npy', - }, - - # wavernn - "wavernn_csmsc-zh": { - 'url': - 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip', - 'md5': - 'ee37b752f09bcba8f2af3b777ca38e13', - 'config': - 'default.yaml', - 'ckpt': - 'snapshot_iter_400000.pdz', - 'speech_stats': - 'feats_stats.npy', - } -} - -model_alias = { - # acoustic model - "speedyspeech": - "paddlespeech.t2s.models.speedyspeech:SpeedySpeech", - "speedyspeech_inference": - "paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference", - "fastspeech2": - "paddlespeech.t2s.models.fastspeech2:FastSpeech2", - "fastspeech2_inference": - "paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference", - "tacotron2": - "paddlespeech.t2s.models.tacotron2:Tacotron2", - "tacotron2_inference": - "paddlespeech.t2s.models.tacotron2:Tacotron2Inference", - # voc - "pwgan": - "paddlespeech.t2s.models.parallel_wavegan:PWGGenerator", - "pwgan_inference": - "paddlespeech.t2s.models.parallel_wavegan:PWGInference", - "mb_melgan": - "paddlespeech.t2s.models.melgan:MelGANGenerator", - "mb_melgan_inference": - "paddlespeech.t2s.models.melgan:MelGANInference", - "style_melgan": - "paddlespeech.t2s.models.melgan:StyleMelGANGenerator", - "style_melgan_inference": - "paddlespeech.t2s.models.melgan:StyleMelGANInference", - "hifigan": - "paddlespeech.t2s.models.hifigan:HiFiGANGenerator", - "hifigan_inference": - "paddlespeech.t2s.models.hifigan:HiFiGANInference", - "wavernn": - "paddlespeech.t2s.models.wavernn:WaveRNN", - "wavernn_inference": - "paddlespeech.t2s.models.wavernn:WaveRNNInference", -} - @cli_register( name='paddlespeech.tts', description='Text to Speech infer command.') class TTSExecutor(BaseExecutor): def __init__(self): super().__init__() + self.model_alias = model_alias + self.pretrained_models = pretrained_models self.parser = argparse.ArgumentParser( prog='paddlespeech.tts', add_help=True) @@ -449,22 +164,6 @@ class TTSExecutor(BaseExecutor): action='store_true', help='Increase logger verbosity of current task.') - def _get_pretrained_path(self, tag: str) -> os.PathLike: - """ - Download and returns pretrained resources path of current task. - """ - support_models = list(pretrained_models.keys()) - assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format( - tag, '\n\t\t'.join(support_models)) - - res_path = os.path.join(MODEL_HOME, tag) - decompressed_path = download_and_decompress(pretrained_models[tag], - res_path) - decompressed_path = os.path.abspath(decompressed_path) - logger.info( - 'Use pretrained model stored in: {}'.format(decompressed_path)) - return decompressed_path - def _init_from_path( self, am: str='fastspeech2_csmsc', @@ -490,16 +189,15 @@ class TTSExecutor(BaseExecutor): if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None: am_res_path = self._get_pretrained_path(am_tag) self.am_res_path = am_res_path - self.am_config = os.path.join(am_res_path, - pretrained_models[am_tag]['config']) + self.am_config = os.path.join( + am_res_path, self.pretrained_models[am_tag]['config']) self.am_ckpt = os.path.join(am_res_path, - pretrained_models[am_tag]['ckpt']) + self.pretrained_models[am_tag]['ckpt']) self.am_stat = os.path.join( - am_res_path, pretrained_models[am_tag]['speech_stats']) + am_res_path, self.pretrained_models[am_tag]['speech_stats']) # must have phones_dict in acoustic self.phones_dict = os.path.join( - am_res_path, pretrained_models[am_tag]['phones_dict']) - print("self.phones_dict:", self.phones_dict) + am_res_path, self.pretrained_models[am_tag]['phones_dict']) logger.info(am_res_path) logger.info(self.am_config) logger.info(self.am_ckpt) @@ -509,21 +207,20 @@ class TTSExecutor(BaseExecutor): self.am_stat = os.path.abspath(am_stat) self.phones_dict = os.path.abspath(phones_dict) self.am_res_path = os.path.dirname(os.path.abspath(self.am_config)) - print("self.phones_dict:", self.phones_dict) # for speedyspeech self.tones_dict = None - if 'tones_dict' in pretrained_models[am_tag]: + if 'tones_dict' in self.pretrained_models[am_tag]: self.tones_dict = os.path.join( - am_res_path, pretrained_models[am_tag]['tones_dict']) + am_res_path, self.pretrained_models[am_tag]['tones_dict']) if tones_dict: self.tones_dict = tones_dict # for multi speaker fastspeech2 self.speaker_dict = None - if 'speaker_dict' in pretrained_models[am_tag]: + if 'speaker_dict' in self.pretrained_models[am_tag]: self.speaker_dict = os.path.join( - am_res_path, pretrained_models[am_tag]['speaker_dict']) + am_res_path, self.pretrained_models[am_tag]['speaker_dict']) if speaker_dict: self.speaker_dict = speaker_dict @@ -532,12 +229,12 @@ class TTSExecutor(BaseExecutor): if voc_ckpt is None or voc_config is None or voc_stat is None: voc_res_path = self._get_pretrained_path(voc_tag) self.voc_res_path = voc_res_path - self.voc_config = os.path.join(voc_res_path, - pretrained_models[voc_tag]['config']) - self.voc_ckpt = os.path.join(voc_res_path, - pretrained_models[voc_tag]['ckpt']) + self.voc_config = os.path.join( + voc_res_path, self.pretrained_models[voc_tag]['config']) + self.voc_ckpt = os.path.join( + voc_res_path, self.pretrained_models[voc_tag]['ckpt']) self.voc_stat = os.path.join( - voc_res_path, pretrained_models[voc_tag]['speech_stats']) + voc_res_path, self.pretrained_models[voc_tag]['speech_stats']) logger.info(voc_res_path) logger.info(self.voc_config) logger.info(self.voc_ckpt) @@ -588,8 +285,9 @@ class TTSExecutor(BaseExecutor): # model: {model_name}_{dataset} am_name = am[:am.rindex('_')] - am_class = dynamic_import(am_name, model_alias) - am_inference_class = dynamic_import(am_name + '_inference', model_alias) + am_class = dynamic_import(am_name, self.model_alias) + am_inference_class = dynamic_import(am_name + '_inference', + self.model_alias) if am_name == 'fastspeech2': am = am_class( @@ -618,9 +316,9 @@ class TTSExecutor(BaseExecutor): # vocoder # model: {model_name}_{dataset} voc_name = voc[:voc.rindex('_')] - voc_class = dynamic_import(voc_name, model_alias) + voc_class = dynamic_import(voc_name, self.model_alias) voc_inference_class = dynamic_import(voc_name + '_inference', - model_alias) + self.model_alias) if voc_name != 'wavernn': voc = voc_class(**self.voc_config["generator_params"]) voc.set_state_dict(paddle.load(self.voc_ckpt)["generator_params"]) @@ -735,7 +433,6 @@ class TTSExecutor(BaseExecutor): am_ckpt = args.am_ckpt am_stat = args.am_stat phones_dict = args.phones_dict - print("phones_dict:", phones_dict) tones_dict = args.tones_dict speaker_dict = args.speaker_dict voc = args.voc diff --git a/paddlespeech/cli/tts/pretrained_models.py b/paddlespeech/cli/tts/pretrained_models.py new file mode 100644 index 000000000..65254a935 --- /dev/null +++ b/paddlespeech/cli/tts/pretrained_models.py @@ -0,0 +1,300 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +pretrained_models = { + # speedyspeech + "speedyspeech_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip', + 'md5': + '6f6fa967b408454b6662c8c00c0027cb', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_30600.pdz', + 'speech_stats': + 'feats_stats.npy', + 'phones_dict': + 'phone_id_map.txt', + 'tones_dict': + 'tone_id_map.txt', + }, + + # fastspeech2 + "fastspeech2_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip', + 'md5': + '637d28a5e53aa60275612ba4393d5f22', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_76000.pdz', + 'speech_stats': + 'speech_stats.npy', + 'phones_dict': + 'phone_id_map.txt', + }, + "fastspeech2_ljspeech-en": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip', + 'md5': + 'ffed800c93deaf16ca9b3af89bfcd747', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_100000.pdz', + 'speech_stats': + 'speech_stats.npy', + 'phones_dict': + 'phone_id_map.txt', + }, + "fastspeech2_aishell3-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip', + 'md5': + 'f4dd4a5f49a4552b77981f544ab3392e', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_96400.pdz', + 'speech_stats': + 'speech_stats.npy', + 'phones_dict': + 'phone_id_map.txt', + 'speaker_dict': + 'speaker_id_map.txt', + }, + "fastspeech2_vctk-en": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip', + 'md5': + '743e5024ca1e17a88c5c271db9779ba4', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_66200.pdz', + 'speech_stats': + 'speech_stats.npy', + 'phones_dict': + 'phone_id_map.txt', + 'speaker_dict': + 'speaker_id_map.txt', + }, + # tacotron2 + "tacotron2_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip', + 'md5': + '0df4b6f0bcbe0d73c5ed6df8867ab91a', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_30600.pdz', + 'speech_stats': + 'speech_stats.npy', + 'phones_dict': + 'phone_id_map.txt', + }, + "tacotron2_ljspeech-en": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.2.0.zip', + 'md5': + '6a5eddd81ae0e81d16959b97481135f3', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_60300.pdz', + 'speech_stats': + 'speech_stats.npy', + 'phones_dict': + 'phone_id_map.txt', + }, + + # pwgan + "pwgan_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip', + 'md5': + '2e481633325b5bdf0a3823c714d2c117', + 'config': + 'pwg_default.yaml', + 'ckpt': + 'pwg_snapshot_iter_400000.pdz', + 'speech_stats': + 'pwg_stats.npy', + }, + "pwgan_ljspeech-en": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip', + 'md5': + '53610ba9708fd3008ccaf8e99dacbaf0', + 'config': + 'pwg_default.yaml', + 'ckpt': + 'pwg_snapshot_iter_400000.pdz', + 'speech_stats': + 'pwg_stats.npy', + }, + "pwgan_aishell3-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip', + 'md5': + 'd7598fa41ad362d62f85ffc0f07e3d84', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_1000000.pdz', + 'speech_stats': + 'feats_stats.npy', + }, + "pwgan_vctk-en": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip', + 'md5': + 'b3da1defcde3e578be71eb284cb89f2c', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_1500000.pdz', + 'speech_stats': + 'feats_stats.npy', + }, + # mb_melgan + "mb_melgan_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip', + 'md5': + 'ee5f0604e20091f0d495b6ec4618b90d', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_1000000.pdz', + 'speech_stats': + 'feats_stats.npy', + }, + # style_melgan + "style_melgan_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip', + 'md5': + '5de2d5348f396de0c966926b8c462755', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_1500000.pdz', + 'speech_stats': + 'feats_stats.npy', + }, + # hifigan + "hifigan_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip', + 'md5': + 'dd40a3d88dfcf64513fba2f0f961ada6', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_2500000.pdz', + 'speech_stats': + 'feats_stats.npy', + }, + "hifigan_ljspeech-en": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_ckpt_0.2.0.zip', + 'md5': + '70e9131695decbca06a65fe51ed38a72', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_2500000.pdz', + 'speech_stats': + 'feats_stats.npy', + }, + "hifigan_aishell3-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip', + 'md5': + '3bb49bc75032ed12f79c00c8cc79a09a', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_2500000.pdz', + 'speech_stats': + 'feats_stats.npy', + }, + "hifigan_vctk-en": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip', + 'md5': + '7da8f88359bca2457e705d924cf27bd4', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_2500000.pdz', + 'speech_stats': + 'feats_stats.npy', + }, + + # wavernn + "wavernn_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip', + 'md5': + 'ee37b752f09bcba8f2af3b777ca38e13', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_400000.pdz', + 'speech_stats': + 'feats_stats.npy', + } +} + +model_alias = { + # acoustic model + "speedyspeech": + "paddlespeech.t2s.models.speedyspeech:SpeedySpeech", + "speedyspeech_inference": + "paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference", + "fastspeech2": + "paddlespeech.t2s.models.fastspeech2:FastSpeech2", + "fastspeech2_inference": + "paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference", + "tacotron2": + "paddlespeech.t2s.models.tacotron2:Tacotron2", + "tacotron2_inference": + "paddlespeech.t2s.models.tacotron2:Tacotron2Inference", + # voc + "pwgan": + "paddlespeech.t2s.models.parallel_wavegan:PWGGenerator", + "pwgan_inference": + "paddlespeech.t2s.models.parallel_wavegan:PWGInference", + "mb_melgan": + "paddlespeech.t2s.models.melgan:MelGANGenerator", + "mb_melgan_inference": + "paddlespeech.t2s.models.melgan:MelGANInference", + "style_melgan": + "paddlespeech.t2s.models.melgan:StyleMelGANGenerator", + "style_melgan_inference": + "paddlespeech.t2s.models.melgan:StyleMelGANInference", + "hifigan": + "paddlespeech.t2s.models.hifigan:HiFiGANGenerator", + "hifigan_inference": + "paddlespeech.t2s.models.hifigan:HiFiGANInference", + "wavernn": + "paddlespeech.t2s.models.wavernn:WaveRNN", + "wavernn_inference": + "paddlespeech.t2s.models.wavernn:WaveRNNInference", +} diff --git a/paddlespeech/cli/vector/infer.py b/paddlespeech/cli/vector/infer.py index 175a9723e..1dff6edb4 100644 --- a/paddlespeech/cli/vector/infer.py +++ b/paddlespeech/cli/vector/infer.py @@ -15,6 +15,7 @@ import argparse import os import sys from collections import OrderedDict +from typing import Dict from typing import List from typing import Optional from typing import Union @@ -26,45 +27,24 @@ from yacs.config import CfgNode from ..executor import BaseExecutor from ..log import logger from ..utils import cli_register -from ..utils import download_and_decompress -from ..utils import MODEL_HOME from ..utils import stats_wrapper +from .pretrained_models import model_alias +from .pretrained_models import pretrained_models from paddleaudio.backends import load as load_audio from paddleaudio.compliance.librosa import melspectrogram from paddlespeech.s2t.utils.dynamic_import import dynamic_import from paddlespeech.vector.io.batch import feature_normalize from paddlespeech.vector.modules.sid_model import SpeakerIdetification -pretrained_models = { - # The tags for pretrained_models should be "{model_name}[-{dataset}][-{sr}][-...]". - # e.g. "ecapatdnn_voxceleb12-16k". - # Command line and python api use "{model_name}[-{dataset}]" as --model, usage: - # "paddlespeech vector --task spk --model ecapatdnn_voxceleb12-16k --sr 16000 --input ./input.wav" - "ecapatdnn_voxceleb12-16k": { - 'url': - 'https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz', - 'md5': - 'a1c0dba7d4de997187786ff517d5b4ec', - 'cfg_path': - 'conf/model.yaml', # the yaml config path - 'ckpt_path': - 'model/model', # the format is ${dir}/{model_name}, - # so the first 'model' is dir, the second 'model' is the name - # this means we have a model stored as model/model.pdparams - }, -} - -model_alias = { - "ecapatdnn": "paddlespeech.vector.models.ecapa_tdnn:EcapaTdnn", -} - @cli_register( name="paddlespeech.vector", description="Speech to vector embedding infer command.") class VectorExecutor(BaseExecutor): def __init__(self): - super(VectorExecutor, self).__init__() + super().__init__() + self.model_alias = model_alias + self.pretrained_models = pretrained_models self.parser = argparse.ArgumentParser( prog="paddlespeech.vector", add_help=True) @@ -79,7 +59,7 @@ class VectorExecutor(BaseExecutor): "--task", type=str, default="spk", - choices=["spk"], + choices=["spk", "score"], help="task type in vector domain") self.parser.add_argument( "--input", @@ -127,8 +107,8 @@ class VectorExecutor(BaseExecutor): Returns: bool: - False: some audio occurs error - True: all audio process success + False: some audio occurs error + True: all audio process success """ # stage 0: parse the args and get the required args parser_args = self.parser.parse_args(argv) @@ -147,13 +127,40 @@ class VectorExecutor(BaseExecutor): logger.info(f"task source: {task_source}") # stage 3: process the audio one by one + # we do action according the task type task_result = OrderedDict() has_exceptions = False for id_, input_ in task_source.items(): try: - res = self(input_, model, sample_rate, config, ckpt_path, - device) - task_result[id_] = res + # extract the speaker audio embedding + if parser_args.task == "spk": + logger.info("do vector spk task") + res = self(input_, model, sample_rate, config, ckpt_path, + device) + task_result[id_] = res + elif parser_args.task == "score": + logger.info("do vector score task") + logger.info(f"input content {input_}") + if len(input_.split()) != 2: + logger.error( + f"vector score task input {input_} wav num is not two," + "that is {len(input_.split())}") + sys.exit(-1) + + # get the enroll and test embedding + enroll_audio, test_audio = input_.split() + logger.info( + f"score task, enroll audio: {enroll_audio}, test audio: {test_audio}" + ) + enroll_embedding = self(enroll_audio, model, sample_rate, + config, ckpt_path, device) + test_embedding = self(test_audio, model, sample_rate, + config, ckpt_path, device) + + # get the score + res = self.get_embeddings_score(enroll_embedding, + test_embedding) + task_result[id_] = res except Exception as e: has_exceptions = True task_result[id_] = f'{e.__class__.__name__}: {e}' @@ -172,6 +179,49 @@ class VectorExecutor(BaseExecutor): else: return True + def _get_job_contents( + self, job_input: os.PathLike) -> Dict[str, Union[str, os.PathLike]]: + """ + Read a job input file and return its contents in a dictionary. + Refactor from the Executor._get_job_contents + + Args: + job_input (os.PathLike): The job input file. + + Returns: + Dict[str, str]: Contents of job input. + """ + job_contents = OrderedDict() + with open(job_input) as f: + for line in f: + line = line.strip() + if not line: + continue + k = line.split(' ')[0] + v = ' '.join(line.split(' ')[1:]) + job_contents[k] = v + return job_contents + + def get_embeddings_score(self, enroll_embedding, test_embedding): + """get the enroll embedding and test embedding score + + Args: + enroll_embedding (numpy.array): shape: (emb_size), enroll audio embedding + test_embedding (numpy.array): shape: (emb_size), test audio embedding + + Returns: + score: the score between enroll embedding and test embedding + """ + if not hasattr(self, "score_func"): + self.score_func = paddle.nn.CosineSimilarity(axis=0) + logger.info("create the cosine score function ") + + score = self.score_func( + paddle.to_tensor(enroll_embedding), + paddle.to_tensor(test_embedding)) + + return score.item() + @stats_wrapper def __call__(self, audio_file: os.PathLike, @@ -218,32 +268,6 @@ class VectorExecutor(BaseExecutor): return res - def _get_pretrained_path(self, tag: str) -> os.PathLike: - """get the neural network path from the pretrained model list - we stored all the pretained mode in the variable `pretrained_models` - - Args: - tag (str): model tag in the pretrained model list - - Returns: - os.PathLike: the downloaded pretrained model path in the disk - """ - support_models = list(pretrained_models.keys()) - assert tag in pretrained_models, \ - 'The model "{}" you want to use has not been supported,'\ - 'please choose other models.\n' \ - 'The support models includes\n\t\t{}'.format(tag, "\n\t\t".join(support_models)) - - res_path = os.path.join(MODEL_HOME, tag) - decompressed_path = download_and_decompress(pretrained_models[tag], - res_path) - - decompressed_path = os.path.abspath(decompressed_path) - logger.info( - 'Use pretrained model stored in: {}'.format(decompressed_path)) - - return decompressed_path - def _init_from_path(self, model_type: str='ecapatdnn_voxceleb12', sample_rate: int=16000, @@ -279,10 +303,11 @@ class VectorExecutor(BaseExecutor): res_path = self._get_pretrained_path(tag) self.res_path = res_path - self.cfg_path = os.path.join(res_path, - pretrained_models[tag]['cfg_path']) + self.cfg_path = os.path.join( + res_path, self.pretrained_models[tag]['cfg_path']) self.ckpt_path = os.path.join( - res_path, pretrained_models[tag]['ckpt_path'] + '.pdparams') + res_path, + self.pretrained_models[tag]['ckpt_path'] + '.pdparams') else: # get the model from disk self.cfg_path = os.path.abspath(cfg_path) @@ -302,7 +327,7 @@ class VectorExecutor(BaseExecutor): logger.info("start to dynamic import the model class") model_name = model_type[:model_type.rindex('_')] logger.info(f"model name {model_name}") - model_class = dynamic_import(model_name, model_alias) + model_class = dynamic_import(model_name, self.model_alias) model_conf = self.config.model backbone = model_class(**model_conf) model = SpeakerIdetification( diff --git a/paddlespeech/cli/vector/pretrained_models.py b/paddlespeech/cli/vector/pretrained_models.py new file mode 100644 index 000000000..686a22d8f --- /dev/null +++ b/paddlespeech/cli/vector/pretrained_models.py @@ -0,0 +1,36 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +pretrained_models = { + # The tags for pretrained_models should be "{model_name}[-{dataset}][-{sr}][-...]". + # e.g. "ecapatdnn_voxceleb12-16k". + # Command line and python api use "{model_name}[-{dataset}]" as --model, usage: + # "paddlespeech vector --task spk --model ecapatdnn_voxceleb12-16k --sr 16000 --input ./input.wav" + "ecapatdnn_voxceleb12-16k": { + 'url': + 'https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_0.tar.gz', + 'md5': + 'cc33023c54ab346cd318408f43fcaf95', + 'cfg_path': + 'conf/model.yaml', # the yaml config path + 'ckpt_path': + 'model/model', # the format is ${dir}/{model_name}, + # so the first 'model' is dir, the second 'model' is the name + # this means we have a model stored as model/model.pdparams + }, +} + +model_alias = { + "ecapatdnn": "paddlespeech.vector.models.ecapa_tdnn:EcapaTdnn", +} diff --git a/paddlespeech/s2t/__init__.py b/paddlespeech/s2t/__init__.py index 855ceef96..7acc37160 100644 --- a/paddlespeech/s2t/__init__.py +++ b/paddlespeech/s2t/__init__.py @@ -26,10 +26,10 @@ from paddlespeech.s2t.utils.log import Log #TODO(Hui Zhang): remove fluid import logger = Log(__name__).getlog() -########### hcak logging ############# +########### hack logging ############# logger.warn = logger.warning -########### hcak paddle ############# +########### hack paddle ############# paddle.half = 'float16' paddle.float = 'float32' paddle.double = 'float64' @@ -110,7 +110,7 @@ if not hasattr(paddle, 'cat'): paddle.cat = cat -########### hcak paddle.Tensor ############# +########### hack paddle.Tensor ############# def item(x: paddle.Tensor): return x.numpy().item() diff --git a/paddlespeech/s2t/exps/u2/bin/test_wav.py b/paddlespeech/s2t/exps/u2/bin/test_wav.py index 9904813a5..86c3db89f 100644 --- a/paddlespeech/s2t/exps/u2/bin/test_wav.py +++ b/paddlespeech/s2t/exps/u2/bin/test_wav.py @@ -79,7 +79,6 @@ class U2Infer(): ilen = paddle.to_tensor(feat.shape[0]) xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(axis=0) - decode_config = self.config.decode result_transcripts = self.model.decode( xs, @@ -129,6 +128,7 @@ if __name__ == "__main__": args = parser.parse_args() config = CfgNode(new_allowed=True) + if args.config: config.merge_from_file(args.config) if args.decode_cfg: diff --git a/paddlespeech/s2t/models/u2/u2.py b/paddlespeech/s2t/models/u2/u2.py index 6a98607b6..9b66126ea 100644 --- a/paddlespeech/s2t/models/u2/u2.py +++ b/paddlespeech/s2t/models/u2/u2.py @@ -279,14 +279,13 @@ class U2BaseModel(ASRInterface, nn.Layer): # TODO(Hui Zhang): if end_flag.sum() == running_size: if end_flag.cast(paddle.int64).sum() == running_size: break - + # 2.1 Forward decoder step hyps_mask = subsequent_mask(i).unsqueeze(0).repeat( running_size, 1, 1).to(device) # (B*N, i, i) # logp: (B*N, vocab) logp, cache = self.decoder.forward_one_step( encoder_out, encoder_mask, hyps, hyps_mask, cache) - # 2.2 First beam prune: select topk best prob at current time top_k_logp, top_k_index = logp.topk(beam_size) # (B*N, N) top_k_logp = mask_finished_scores(top_k_logp, end_flag) @@ -708,11 +707,11 @@ class U2BaseModel(ASRInterface, nn.Layer): batch_size = feats.shape[0] if decoding_method in ['ctc_prefix_beam_search', 'attention_rescoring'] and batch_size > 1: - logger.fatal( + logger.error( f'decoding mode {decoding_method} must be running with batch_size == 1' ) + logger.error(f"current batch_size is {batch_size}") sys.exit(1) - if decoding_method == 'attention': hyps = self.recognize( feats, diff --git a/paddlespeech/s2t/modules/ctc.py b/paddlespeech/s2t/modules/ctc.py index 33ad472de..1bb158738 100644 --- a/paddlespeech/s2t/modules/ctc.py +++ b/paddlespeech/s2t/modules/ctc.py @@ -180,7 +180,7 @@ class CTCDecoder(CTCDecoderBase): # init once if self._ext_scorer is not None: return - + if language_model_path != '': logger.info("begin to initialize the external scorer " "for decoding") diff --git a/paddlespeech/server/README.md b/paddlespeech/server/README.md index 819fe440d..3ac68dae6 100644 --- a/paddlespeech/server/README.md +++ b/paddlespeech/server/README.md @@ -35,3 +35,16 @@ ```bash paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input input.wav ``` + + ## Online ASR Server + +### Lanuch online asr server +``` +paddlespeech_server start --config_file conf/ws_conformer_application.yaml +``` + +### Access online asr server + +``` +paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8090 --input input_16k.wav +``` \ No newline at end of file diff --git a/paddlespeech/server/README_cn.md b/paddlespeech/server/README_cn.md index c0a4a7336..5f235313b 100644 --- a/paddlespeech/server/README_cn.md +++ b/paddlespeech/server/README_cn.md @@ -35,3 +35,17 @@ ```bash paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input input.wav ``` + +## 流式ASR + +### 启动流式语音识别服务 + +``` +paddlespeech_server start --config_file conf/ws_conformer_application.yaml +``` + +### 访问流式语音识别服务 + +``` +paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8090 --input zh.wav +``` \ No newline at end of file diff --git a/paddlespeech/server/bin/paddlespeech_client.py b/paddlespeech/server/bin/paddlespeech_client.py index 413f00872..45469178a 100644 --- a/paddlespeech/server/bin/paddlespeech_client.py +++ b/paddlespeech/server/bin/paddlespeech_client.py @@ -12,9 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. import argparse +import asyncio import base64 import io import json +import logging import os import random import time @@ -28,6 +30,7 @@ from ..executor import BaseExecutor from ..util import cli_client_register from ..util import stats_wrapper from paddlespeech.cli.log import logger +from paddlespeech.server.tests.asr.online.websocket_client import ASRAudioHandler from paddlespeech.server.utils.audio_process import wav2pcm from paddlespeech.server.utils.util import wav2base64 @@ -230,6 +233,78 @@ class ASRClientExecutor(BaseExecutor): return res +@cli_client_register( + name='paddlespeech_client.asr_online', + description='visit asr online service') +class ASRClientExecutor(BaseExecutor): + def __init__(self): + super(ASRClientExecutor, self).__init__() + self.parser = argparse.ArgumentParser( + prog='paddlespeech_client.asr', add_help=True) + self.parser.add_argument( + '--server_ip', type=str, default='127.0.0.1', help='server ip') + self.parser.add_argument( + '--port', type=int, default=8091, help='server port') + self.parser.add_argument( + '--input', + type=str, + default=None, + help='Audio file to be recognized', + required=True) + self.parser.add_argument( + '--sample_rate', type=int, default=16000, help='audio sample rate') + self.parser.add_argument( + '--lang', type=str, default="zh_cn", help='language') + self.parser.add_argument( + '--audio_format', type=str, default="wav", help='audio format') + + def execute(self, argv: List[str]) -> bool: + args = self.parser.parse_args(argv) + input_ = args.input + server_ip = args.server_ip + port = args.port + sample_rate = args.sample_rate + lang = args.lang + audio_format = args.audio_format + + try: + time_start = time.time() + res = self( + input=input_, + server_ip=server_ip, + port=port, + sample_rate=sample_rate, + lang=lang, + audio_format=audio_format) + time_end = time.time() + logger.info(res) + logger.info("Response time %f s." % (time_end - time_start)) + return True + except Exception as e: + logger.error("Failed to speech recognition.") + logger.error(e) + return False + + @stats_wrapper + def __call__(self, + input: str, + server_ip: str="127.0.0.1", + port: int=8091, + sample_rate: int=16000, + lang: str="zh_cn", + audio_format: str="wav"): + """ + Python API to call an executor. + """ + logging.basicConfig(level=logging.INFO) + logging.info("asr websocket client start") + handler = ASRAudioHandler(server_ip, port) + loop = asyncio.get_event_loop() + res = loop.run_until_complete(handler.run(input)) + logging.info("asr websocket client finished") + + return res['asr_results'] + @cli_client_register( name='paddlespeech_client.cls', description='visit cls service') class CLSClientExecutor(BaseExecutor): diff --git a/paddlespeech/server/bin/paddlespeech_server.py b/paddlespeech/server/bin/paddlespeech_server.py index f6a7f4295..474a8b79f 100644 --- a/paddlespeech/server/bin/paddlespeech_server.py +++ b/paddlespeech/server/bin/paddlespeech_server.py @@ -23,8 +23,9 @@ from ..util import cli_server_register from ..util import stats_wrapper from paddlespeech.cli.log import logger from paddlespeech.server.engine.engine_pool import init_engine_pool -from paddlespeech.server.restful.api import setup_router +from paddlespeech.server.restful.api import setup_router as setup_http_router from paddlespeech.server.utils.config import get_config +from paddlespeech.server.ws.api import setup_router as setup_ws_router __all__ = ['ServerExecutor', 'ServerStatsExecutor'] @@ -63,7 +64,12 @@ class ServerExecutor(BaseExecutor): """ # init api api_list = list(engine.split("_")[0] for engine in config.engine_list) - api_router = setup_router(api_list) + if config.protocol == "websocket": + api_router = setup_ws_router(api_list) + elif config.protocol == "http": + api_router = setup_http_router(api_list) + else: + raise Exception("unsupported protocol") app.include_router(api_router) if not init_engine_pool(config): diff --git a/paddlespeech/server/conf/tts_online_application.yaml b/paddlespeech/server/conf/tts_online_application.yaml new file mode 100644 index 000000000..a80b3ecec --- /dev/null +++ b/paddlespeech/server/conf/tts_online_application.yaml @@ -0,0 +1,46 @@ +# This is the parameter configuration file for PaddleSpeech Serving. + +################################################################################# +# SERVER SETTING # +################################################################################# +host: 127.0.0.1 +port: 8092 + +# The task format in the engin_list is: _ +# task choices = ['asr_online', 'tts_online'] +# protocol = ['websocket', 'http'] (only one can be selected). +protocol: 'http' +engine_list: ['tts_online'] + + +################################################################################# +# ENGINE CONFIG # +################################################################################# + +################################### TTS ######################################### +################### speech task: tts; engine_type: online ####################### +tts_online: + # am (acoustic model) choices=['fastspeech2_csmsc'] + am: 'fastspeech2_csmsc' + am_config: + am_ckpt: + am_stat: + phones_dict: + tones_dict: + speaker_dict: + spk_id: 0 + + # voc (vocoder) choices=['mb_melgan_csmsc'] + voc: 'mb_melgan_csmsc' + voc_config: + voc_ckpt: + voc_stat: + + # others + lang: 'zh' + device: # set 'gpu:id' or 'cpu' + am_block: 42 + am_pad: 12 + voc_block: 14 + voc_pad: 14 + diff --git a/paddlespeech/server/conf/ws_application.yaml b/paddlespeech/server/conf/ws_application.yaml index ef23593ed..dee8d78ba 100644 --- a/paddlespeech/server/conf/ws_application.yaml +++ b/paddlespeech/server/conf/ws_application.yaml @@ -4,7 +4,7 @@ # SERVER SETTING # ################################################################################# host: 0.0.0.0 -port: 8091 +port: 8090 # The task format in the engin_list is: _ # task choices = ['asr_online', 'tts_online'] @@ -41,11 +41,7 @@ asr_online: shift_ms: 40 sample_rate: 16000 sample_width: 2 - - vad_conf: - aggressiveness: 2 - sample_rate: 16000 - frame_duration_ms: 20 - sample_width: 2 - padding_ms: 200 - padding_ratio: 0.9 + window_n: 7 # frame + shift_n: 4 # frame + window_ms: 20 # ms + shift_ms: 10 # ms diff --git a/paddlespeech/server/conf/ws_conformer_application.yaml b/paddlespeech/server/conf/ws_conformer_application.yaml new file mode 100644 index 000000000..e14833dea --- /dev/null +++ b/paddlespeech/server/conf/ws_conformer_application.yaml @@ -0,0 +1,45 @@ +# This is the parameter configuration file for PaddleSpeech Serving. + +################################################################################# +# SERVER SETTING # +################################################################################# +host: 0.0.0.0 +port: 8090 + +# The task format in the engin_list is: _ +# task choices = ['asr_online', 'tts_online'] +# protocol = ['websocket', 'http'] (only one can be selected). +# websocket only support online engine type. +protocol: 'websocket' +engine_list: ['asr_online'] + + +################################################################################# +# ENGINE CONFIG # +################################################################################# + +################################### ASR ######################################### +################### speech task: asr; engine_type: online ####################### +asr_online: + model_type: 'conformer_online_multicn' + am_model: # the pdmodel file of am static model [optional] + am_params: # the pdiparams file of am static model [optional] + lang: 'zh' + sample_rate: 16000 + cfg_path: + decode_method: + force_yes: True + + am_predictor_conf: + device: # set 'gpu:id' or 'cpu' + switch_ir_optim: True + glog_info: False # True -> print glog + summary: True # False -> do not show predictor config + + chunk_buffer_conf: + window_n: 7 # frame + shift_n: 4 # frame + window_ms: 25 # ms + shift_ms: 10 # ms + sample_rate: 16000 + sample_width: 2 \ No newline at end of file diff --git a/paddlespeech/server/engine/asr/online/asr_engine.py b/paddlespeech/server/engine/asr/online/asr_engine.py index 389175a0a..758cbaabe 100644 --- a/paddlespeech/server/engine/asr/online/asr_engine.py +++ b/paddlespeech/server/engine/asr/online/asr_engine.py @@ -11,6 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import copy import os from typing import Optional @@ -20,13 +21,21 @@ from numpy import float32 from yacs.config import CfgNode from paddlespeech.cli.asr.infer import ASRExecutor +from paddlespeech.cli.asr.infer import model_alias from paddlespeech.cli.log import logger +from paddlespeech.cli.utils import download_and_decompress from paddlespeech.cli.utils import MODEL_HOME from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer from paddlespeech.s2t.frontend.speech import SpeechSegment from paddlespeech.s2t.modules.ctc import CTCDecoder +from paddlespeech.s2t.transform.transformation import Transformation +from paddlespeech.s2t.utils.dynamic_import import dynamic_import +from paddlespeech.s2t.utils.tensor_utils import add_sos_eos +from paddlespeech.s2t.utils.tensor_utils import pad_sequence from paddlespeech.s2t.utils.utility import UpdateConfig +from paddlespeech.server.engine.asr.online.ctc_search import CTCPrefixBeamSearch from paddlespeech.server.engine.base_engine import BaseEngine +from paddlespeech.server.utils.audio_process import pcm2float from paddlespeech.server.utils.paddle_predictor import init_predictor __all__ = ['ASREngine'] @@ -34,9 +43,9 @@ __all__ = ['ASREngine'] pretrained_models = { "deepspeech2online_aishell-zh-16k": { 'url': - 'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.1.1.model.tar.gz', + 'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz', 'md5': - 'd5e076217cf60486519f72c217d21b9b', + '23e16c69730a1cb5d735c98c83c21e16', 'cfg_path': 'model.yaml', 'ckpt_path': @@ -50,16 +59,543 @@ pretrained_models = { 'lm_md5': '29e02312deb2e59b3c8686c7966d4fe3' }, + "conformer_online_multicn-zh-16k": { + 'url': + 'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.3.model.tar.gz', + 'md5': + '0ac93d390552336f2a906aec9e33c5fa', + 'cfg_path': + 'model.yaml', + 'ckpt_path': + 'exp/chunk_conformer/checkpoints/multi_cn', + 'model': + 'exp/chunk_conformer/checkpoints/multi_cn.pdparams', + 'params': + 'exp/chunk_conformer/checkpoints/multi_cn.pdparams', + 'lm_url': + 'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm', + 'lm_md5': + '29e02312deb2e59b3c8686c7966d4fe3' + }, } +# ASR server connection process class +class PaddleASRConnectionHanddler: + def __init__(self, asr_engine): + """Init a Paddle ASR Connection Handler instance + + Args: + asr_engine (ASREngine): the global asr engine + """ + super().__init__() + logger.info( + "create an paddle asr connection handler to process the websocket connection" + ) + self.config = asr_engine.config + self.model_config = asr_engine.executor.config + self.asr_engine = asr_engine + + self.init() + self.reset() + + def init(self): + # model_type, sample_rate and text_feature is shared for deepspeech2 and conformer + self.model_type = self.asr_engine.executor.model_type + self.sample_rate = self.asr_engine.executor.sample_rate + # tokens to text + self.text_feature = self.asr_engine.executor.text_feature + + if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type: + from paddlespeech.s2t.io.collator import SpeechCollator + self.am_predictor = self.asr_engine.executor.am_predictor + + self.collate_fn_test = SpeechCollator.from_config(self.model_config) + self.decoder = CTCDecoder( + odim=self.model_config.output_dim, # is in vocab + enc_n_units=self.model_config.rnn_layer_size * 2, + blank_id=self.model_config.blank_id, + dropout_rate=0.0, + reduction=True, # sum + batch_average=True, # sum / batch_size + grad_norm_type=self.model_config.get('ctc_grad_norm_type', + None)) + + cfg = self.model_config.decode + decode_batch_size = 1 # for online + self.decoder.init_decoder( + decode_batch_size, self.text_feature.vocab_list, + cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta, + cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n, + cfg.num_proc_bsearch) + # frame window samples length and frame shift samples length + + self.win_length = int(self.model_config.window_ms / 1000 * + self.sample_rate) + self.n_shift = int(self.model_config.stride_ms / 1000 * + self.sample_rate) + + elif "conformer" in self.model_type or "transformer" in self.model_type: + # acoustic model + self.model = self.asr_engine.executor.model + + # ctc decoding config + self.ctc_decode_config = self.asr_engine.executor.config.decode + self.searcher = CTCPrefixBeamSearch(self.ctc_decode_config) + + # extract feat, new only fbank in conformer model + self.preprocess_conf = self.model_config.preprocess_config + self.preprocess_args = {"train": False} + self.preprocessing = Transformation(self.preprocess_conf) + + # frame window samples length and frame shift samples length + self.win_length = self.preprocess_conf.process[0]['win_length'] + self.n_shift = self.preprocess_conf.process[0]['n_shift'] + + def extract_feat(self, samples): + if "deepspeech2online" in self.model_type: + # self.reamined_wav stores all the samples, + # include the original remained_wav and this package samples + samples = np.frombuffer(samples, dtype=np.int16) + assert samples.ndim == 1 + + # pcm16 -> pcm 32 + # pcm2float will change the orignal samples, + # so we shoule do pcm2float before concatenate + samples = pcm2float(samples) + + if self.remained_wav is None: + self.remained_wav = samples + else: + assert self.remained_wav.ndim == 1 + self.remained_wav = np.concatenate([self.remained_wav, samples]) + logger.info( + f"The connection remain the audio samples: {self.remained_wav.shape}" + ) + + # read audio + speech_segment = SpeechSegment.from_pcm( + self.remained_wav, self.sample_rate, transcript=" ") + # audio augment + self.collate_fn_test.augmentation.transform_audio(speech_segment) + + # extract speech feature + spectrum, transcript_part = self.collate_fn_test._speech_featurizer.featurize( + speech_segment, self.collate_fn_test.keep_transcription_text) + # CMVN spectrum + if self.collate_fn_test._normalizer: + spectrum = self.collate_fn_test._normalizer.apply(spectrum) + + # spectrum augment + audio = self.collate_fn_test.augmentation.transform_feature( + spectrum) + + audio_len = audio.shape[0] + audio = paddle.to_tensor(audio, dtype='float32') + # audio_len = paddle.to_tensor(audio_len) + audio = paddle.unsqueeze(audio, axis=0) + + if self.cached_feat is None: + self.cached_feat = audio + else: + assert (len(audio.shape) == 3) + assert (len(self.cached_feat.shape) == 3) + self.cached_feat = paddle.concat( + [self.cached_feat, audio], axis=1) + + # set the feat device + if self.device is None: + self.device = self.cached_feat.place + + self.num_frames += audio_len + self.remained_wav = self.remained_wav[self.n_shift * audio_len:] + + logger.info( + f"process the audio feature success, the connection feat shape: {self.cached_feat.shape}" + ) + logger.info( + f"After extract feat, the connection remain the audio samples: {self.remained_wav.shape}" + ) + elif "conformer_online" in self.model_type: + logger.info("Online ASR extract the feat") + samples = np.frombuffer(samples, dtype=np.int16) + assert samples.ndim == 1 + + logger.info(f"This package receive {samples.shape[0]} pcm data") + self.num_samples += samples.shape[0] + + # self.reamined_wav stores all the samples, + # include the original remained_wav and this package samples + if self.remained_wav is None: + self.remained_wav = samples + else: + assert self.remained_wav.ndim == 1 + self.remained_wav = np.concatenate([self.remained_wav, samples]) + logger.info( + f"The connection remain the audio samples: {self.remained_wav.shape}" + ) + if len(self.remained_wav) < self.win_length: + return 0 + + # fbank + x_chunk = self.preprocessing(self.remained_wav, + **self.preprocess_args) + x_chunk = paddle.to_tensor( + x_chunk, dtype="float32").unsqueeze(axis=0) + if self.cached_feat is None: + self.cached_feat = x_chunk + else: + assert (len(x_chunk.shape) == 3) + assert (len(self.cached_feat.shape) == 3) + self.cached_feat = paddle.concat( + [self.cached_feat, x_chunk], axis=1) + + # set the feat device + if self.device is None: + self.device = self.cached_feat.place + + num_frames = x_chunk.shape[1] + self.num_frames += num_frames + self.remained_wav = self.remained_wav[self.n_shift * num_frames:] + + logger.info( + f"process the audio feature success, the connection feat shape: {self.cached_feat.shape}" + ) + logger.info( + f"After extract feat, the connection remain the audio samples: {self.remained_wav.shape}" + ) + # logger.info(f"accumulate samples: {self.num_samples}") + + def reset(self): + if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type: + # for deepspeech2 + self.chunk_state_h_box = copy.deepcopy( + self.asr_engine.executor.chunk_state_h_box) + self.chunk_state_c_box = copy.deepcopy( + self.asr_engine.executor.chunk_state_c_box) + self.decoder.reset_decoder(batch_size=1) + + # for conformer online + self.subsampling_cache = None + self.elayers_output_cache = None + self.conformer_cnn_cache = None + self.encoder_out = None + self.cached_feat = None + self.remained_wav = None + self.offset = 0 + self.num_samples = 0 + self.device = None + self.hyps = [] + self.num_frames = 0 + self.chunk_num = 0 + self.global_frame_offset = 0 + self.result_transcripts = [''] + + def decode(self, is_finished=False): + if "deepspeech2online" in self.model_type: + # x_chunk 是特征数据 + decoding_chunk_size = 1 # decoding_chunk_size=1 in deepspeech2 model + context = 7 # context=7 in deepspeech2 model + subsampling = 4 # subsampling=4 in deepspeech2 model + stride = subsampling * decoding_chunk_size + cached_feature_num = context - subsampling + # decoding window for model + decoding_window = (decoding_chunk_size - 1) * subsampling + context + + if self.cached_feat is None: + logger.info("no audio feat, please input more pcm data") + return + + num_frames = self.cached_feat.shape[1] + logger.info( + f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames" + ) + # the cached feat must be larger decoding_window + if num_frames < decoding_window and not is_finished: + logger.info( + f"frame feat num is less than {decoding_window}, please input more pcm data" + ) + return None, None + + # if is_finished=True, we need at least context frames + if num_frames < context: + logger.info( + "flast {num_frames} is less than context {context} frames, and we cannot do model forward" + ) + return None, None + logger.info("start to do model forward") + # num_frames - context + 1 ensure that current frame can get context window + if is_finished: + # if get the finished chunk, we need process the last context + left_frames = context + else: + # we only process decoding_window frames for one chunk + left_frames = decoding_window + + for cur in range(0, num_frames - left_frames + 1, stride): + end = min(cur + decoding_window, num_frames) + # extract the audio + x_chunk = self.cached_feat[:, cur:end, :].numpy() + x_chunk_lens = np.array([x_chunk.shape[1]]) + trans_best = self.decode_one_chunk(x_chunk, x_chunk_lens) + + self.result_transcripts = [trans_best] + + self.cached_feat = self.cached_feat[:, end - cached_feature_num:, :] + # return trans_best[0] + elif "conformer" in self.model_type or "transformer" in self.model_type: + try: + logger.info( + f"we will use the transformer like model : {self.model_type}" + ) + self.advance_decoding(is_finished) + self.update_result() + + except Exception as e: + logger.exception(e) + else: + raise Exception("invalid model name") + + @paddle.no_grad() + def decode_one_chunk(self, x_chunk, x_chunk_lens): + logger.info("start to decoce one chunk with deepspeech2 model") + input_names = self.am_predictor.get_input_names() + audio_handle = self.am_predictor.get_input_handle(input_names[0]) + audio_len_handle = self.am_predictor.get_input_handle(input_names[1]) + h_box_handle = self.am_predictor.get_input_handle(input_names[2]) + c_box_handle = self.am_predictor.get_input_handle(input_names[3]) + + audio_handle.reshape(x_chunk.shape) + audio_handle.copy_from_cpu(x_chunk) + + audio_len_handle.reshape(x_chunk_lens.shape) + audio_len_handle.copy_from_cpu(x_chunk_lens) + + h_box_handle.reshape(self.chunk_state_h_box.shape) + h_box_handle.copy_from_cpu(self.chunk_state_h_box) + + c_box_handle.reshape(self.chunk_state_c_box.shape) + c_box_handle.copy_from_cpu(self.chunk_state_c_box) + + output_names = self.am_predictor.get_output_names() + output_handle = self.am_predictor.get_output_handle(output_names[0]) + output_lens_handle = self.am_predictor.get_output_handle( + output_names[1]) + output_state_h_handle = self.am_predictor.get_output_handle( + output_names[2]) + output_state_c_handle = self.am_predictor.get_output_handle( + output_names[3]) + + self.am_predictor.run() + + output_chunk_probs = output_handle.copy_to_cpu() + output_chunk_lens = output_lens_handle.copy_to_cpu() + self.chunk_state_h_box = output_state_h_handle.copy_to_cpu() + self.chunk_state_c_box = output_state_c_handle.copy_to_cpu() + + self.decoder.next(output_chunk_probs, output_chunk_lens) + trans_best, trans_beam = self.decoder.decode() + logger.info(f"decode one best result: {trans_best[0]}") + return trans_best[0] + + @paddle.no_grad() + def advance_decoding(self, is_finished=False): + logger.info("start to decode with advanced_decoding method") + cfg = self.ctc_decode_config + decoding_chunk_size = cfg.decoding_chunk_size + num_decoding_left_chunks = cfg.num_decoding_left_chunks + + assert decoding_chunk_size > 0 + subsampling = self.model.encoder.embed.subsampling_rate + context = self.model.encoder.embed.right_context + 1 + stride = subsampling * decoding_chunk_size + cached_feature_num = context - subsampling # processed chunk feature cached for next chunk + + # decoding window for model + decoding_window = (decoding_chunk_size - 1) * subsampling + context + if self.cached_feat is None: + logger.info("no audio feat, please input more pcm data") + return + + num_frames = self.cached_feat.shape[1] + logger.info( + f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames" + ) + + # the cached feat must be larger decoding_window + if num_frames < decoding_window and not is_finished: + logger.info( + f"frame feat num is less than {decoding_window}, please input more pcm data" + ) + return None, None + + # if is_finished=True, we need at least context frames + if num_frames < context: + logger.info( + "flast {num_frames} is less than context {context} frames, and we cannot do model forward" + ) + return None, None + + logger.info("start to do model forward") + required_cache_size = decoding_chunk_size * num_decoding_left_chunks + outputs = [] + + # num_frames - context + 1 ensure that current frame can get context window + if is_finished: + # if get the finished chunk, we need process the last context + left_frames = context + else: + # we only process decoding_window frames for one chunk + left_frames = decoding_window + + # record the end for removing the processed feat + end = None + for cur in range(0, num_frames - left_frames + 1, stride): + end = min(cur + decoding_window, num_frames) + + self.chunk_num += 1 + chunk_xs = self.cached_feat[:, cur:end, :] + (y, self.subsampling_cache, self.elayers_output_cache, + self.conformer_cnn_cache) = self.model.encoder.forward_chunk( + chunk_xs, self.offset, required_cache_size, + self.subsampling_cache, self.elayers_output_cache, + self.conformer_cnn_cache) + outputs.append(y) + + # update the offset + self.offset += y.shape[1] + + ys = paddle.cat(outputs, 1) + if self.encoder_out is None: + self.encoder_out = ys + else: + self.encoder_out = paddle.concat([self.encoder_out, ys], axis=1) + + # get the ctc probs + ctc_probs = self.model.ctc.log_softmax(ys) # (1, maxlen, vocab_size) + ctc_probs = ctc_probs.squeeze(0) + + self.searcher.search(ctc_probs, self.cached_feat.place) + + self.hyps = self.searcher.get_one_best_hyps() + assert self.cached_feat.shape[0] == 1 + assert end >= cached_feature_num + + self.cached_feat = self.cached_feat[0, end - + cached_feature_num:, :].unsqueeze(0) + assert len( + self.cached_feat.shape + ) == 3, f"current cache feat shape is: {self.cached_feat.shape}" + + logger.info( + f"This connection handler encoder out shape: {self.encoder_out.shape}" + ) + + def update_result(self): + logger.info("update the final result") + hyps = self.hyps + self.result_transcripts = [ + self.text_feature.defeaturize(hyp) for hyp in hyps + ] + self.result_tokenids = [hyp for hyp in hyps] + + def get_result(self): + if len(self.result_transcripts) > 0: + return self.result_transcripts[0] + else: + return '' + + @paddle.no_grad() + def rescoring(self): + if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type: + return + + logger.info("rescoring the final result") + if "attention_rescoring" != self.ctc_decode_config.decoding_method: + return + + self.searcher.finalize_search() + self.update_result() + + beam_size = self.ctc_decode_config.beam_size + hyps = self.searcher.get_hyps() + if hyps is None or len(hyps) == 0: + return + + # assert len(hyps) == beam_size + hyp_list = [] + for hyp in hyps: + hyp_content = hyp[0] + # Prevent the hyp is empty + if len(hyp_content) == 0: + hyp_content = (self.model.ctc.blank_id, ) + hyp_content = paddle.to_tensor( + hyp_content, place=self.device, dtype=paddle.long) + hyp_list.append(hyp_content) + hyps_pad = pad_sequence(hyp_list, True, self.model.ignore_id) + hyps_lens = paddle.to_tensor( + [len(hyp[0]) for hyp in hyps], place=self.device, + dtype=paddle.long) # (beam_size,) + hyps_pad, _ = add_sos_eos(hyps_pad, self.model.sos, self.model.eos, + self.model.ignore_id) + hyps_lens = hyps_lens + 1 # Add at begining + + encoder_out = self.encoder_out.repeat(beam_size, 1, 1) + encoder_mask = paddle.ones( + (beam_size, 1, encoder_out.shape[1]), dtype=paddle.bool) + decoder_out, _ = self.model.decoder( + encoder_out, encoder_mask, hyps_pad, + hyps_lens) # (beam_size, max_hyps_len, vocab_size) + # ctc score in ln domain + decoder_out = paddle.nn.functional.log_softmax(decoder_out, axis=-1) + decoder_out = decoder_out.numpy() + + # Only use decoder score for rescoring + best_score = -float('inf') + best_index = 0 + # hyps is List[(Text=List[int], Score=float)], len(hyps)=beam_size + for i, hyp in enumerate(hyps): + score = 0.0 + for j, w in enumerate(hyp[0]): + score += decoder_out[i][j][w] + # last decoder output token is `eos`, for laste decoder input token. + score += decoder_out[i][len(hyp[0])][self.model.eos] + # add ctc score (which in ln domain) + score += hyp[1] * self.ctc_decode_config.ctc_weight + if score > best_score: + best_score = score + best_index = i + + # update the one best result + logger.info(f"best index: {best_index}") + self.hyps = [hyps[best_index][0]] + self.update_result() + + class ASRServerExecutor(ASRExecutor): def __init__(self): super().__init__() pass + def _get_pretrained_path(self, tag: str) -> os.PathLike: + """ + Download and returns pretrained resources path of current task. + """ + support_models = list(pretrained_models.keys()) + assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format( + tag, '\n\t\t'.join(support_models)) + + res_path = os.path.join(MODEL_HOME, tag) + decompressed_path = download_and_decompress(pretrained_models[tag], + res_path) + decompressed_path = os.path.abspath(decompressed_path) + logger.info( + 'Use pretrained model stored in: {}'.format(decompressed_path)) + + return decompressed_path + def _init_from_path(self, - model_type: str='wenetspeech', + model_type: str='deepspeech2online_aishell', am_model: Optional[os.PathLike]=None, am_params: Optional[os.PathLike]=None, lang: str='zh', @@ -70,12 +606,15 @@ class ASRServerExecutor(ASRExecutor): """ Init model and other resources from a specific path. """ - + self.model_type = model_type + self.sample_rate = sample_rate if cfg_path is None or am_model is None or am_params is None: sample_rate_str = '16k' if sample_rate == 16000 else '8k' tag = model_type + '-' + lang + '-' + sample_rate_str + logger.info(f"Load the pretrained model, tag = {tag}") res_path = self._get_pretrained_path(tag) # wenetspeech_zh self.res_path = res_path + self.cfg_path = os.path.join(res_path, pretrained_models[tag]['cfg_path']) @@ -84,9 +623,6 @@ class ASRServerExecutor(ASRExecutor): self.am_params = os.path.join(res_path, pretrained_models[tag]['params']) logger.info(res_path) - logger.info(self.cfg_path) - logger.info(self.am_model) - logger.info(self.am_params) else: self.cfg_path = os.path.abspath(cfg_path) self.am_model = os.path.abspath(am_model) @@ -94,6 +630,10 @@ class ASRServerExecutor(ASRExecutor): self.res_path = os.path.dirname( os.path.dirname(os.path.abspath(self.cfg_path))) + logger.info(self.cfg_path) + logger.info(self.am_model) + logger.info(self.am_params) + #Init body. self.config = CfgNode(new_allowed=True) self.config.merge_from_file(self.cfg_path) @@ -111,59 +651,107 @@ class ASRServerExecutor(ASRExecutor): lm_url = pretrained_models[tag]['lm_url'] lm_md5 = pretrained_models[tag]['lm_md5'] + logger.info(f"Start to load language model {lm_url}") self.download_lm( lm_url, os.path.dirname(self.config.decode.lang_model_path), lm_md5) - elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type: - raise Exception("wrong type") + elif "conformer" in model_type or "transformer" in model_type: + logger.info("start to create the stream conformer asr engine") + if self.config.spm_model_prefix: + self.config.spm_model_prefix = os.path.join( + self.res_path, self.config.spm_model_prefix) + self.vocab = self.config.vocab_filepath + self.text_feature = TextFeaturizer( + unit_type=self.config.unit_type, + vocab=self.config.vocab_filepath, + spm_model_prefix=self.config.spm_model_prefix) + # update the decoding method + if decode_method: + self.config.decode.decoding_method = decode_method + + # we only support ctc_prefix_beam_search and attention_rescoring dedoding method + # Generally we set the decoding_method to attention_rescoring + if self.config.decode.decoding_method not in [ + "ctc_prefix_beam_search", "attention_rescoring" + ]: + logger.info( + "we set the decoding_method to attention_rescoring") + self.config.decode.decoding = "attention_rescoring" + assert self.config.decode.decoding_method in [ + "ctc_prefix_beam_search", "attention_rescoring" + ], f"we only support ctc_prefix_beam_search and attention_rescoring dedoding method, current decoding method is {self.config.decode.decoding_method}" else: raise Exception("wrong type") + if "deepspeech2online" in model_type or "deepspeech2offline" in model_type: + # AM predictor + logger.info("ASR engine start to init the am predictor") + self.am_predictor_conf = am_predictor_conf + self.am_predictor = init_predictor( + model_file=self.am_model, + params_file=self.am_params, + predictor_conf=self.am_predictor_conf) - # AM predictor - self.am_predictor_conf = am_predictor_conf - self.am_predictor = init_predictor( - model_file=self.am_model, - params_file=self.am_params, - predictor_conf=self.am_predictor_conf) - - # decoder - self.decoder = CTCDecoder( - odim=self.config.output_dim, # is in vocab - enc_n_units=self.config.rnn_layer_size * 2, - blank_id=self.config.blank_id, - dropout_rate=0.0, - reduction=True, # sum - batch_average=True, # sum / batch_size - grad_norm_type=self.config.get('ctc_grad_norm_type', None)) - - # init decoder - cfg = self.config.decode - decode_batch_size = 1 # for online - self.decoder.init_decoder( - decode_batch_size, self.text_feature.vocab_list, - cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta, - cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n, - cfg.num_proc_bsearch) - - # init state box - self.chunk_state_h_box = np.zeros( - (self.config.num_rnn_layers, 1, self.config.rnn_layer_size), - dtype=float32) - self.chunk_state_c_box = np.zeros( - (self.config.num_rnn_layers, 1, self.config.rnn_layer_size), - dtype=float32) + # decoder + logger.info("ASR engine start to create the ctc decoder instance") + self.decoder = CTCDecoder( + odim=self.config.output_dim, # is in vocab + enc_n_units=self.config.rnn_layer_size * 2, + blank_id=self.config.blank_id, + dropout_rate=0.0, + reduction=True, # sum + batch_average=True, # sum / batch_size + grad_norm_type=self.config.get('ctc_grad_norm_type', None)) + + # init decoder + logger.info("ASR engine start to init the ctc decoder") + cfg = self.config.decode + decode_batch_size = 1 # for online + self.decoder.init_decoder( + decode_batch_size, self.text_feature.vocab_list, + cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta, + cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n, + cfg.num_proc_bsearch) + + # init state box + self.chunk_state_h_box = np.zeros( + (self.config.num_rnn_layers, 1, self.config.rnn_layer_size), + dtype=float32) + self.chunk_state_c_box = np.zeros( + (self.config.num_rnn_layers, 1, self.config.rnn_layer_size), + dtype=float32) + elif "conformer" in model_type or "transformer" in model_type: + model_name = model_type[:model_type.rindex( + '_')] # model_type: {model_name}_{dataset} + logger.info(f"model name: {model_name}") + model_class = dynamic_import(model_name, model_alias) + model_conf = self.config + model = model_class.from_config(model_conf) + self.model = model + self.model.eval() + + # load model + model_dict = paddle.load(self.am_model) + self.model.set_state_dict(model_dict) + logger.info("create the transformer like model success") + + # update the ctc decoding + self.searcher = CTCPrefixBeamSearch(self.config.decode) + self.transformer_decode_reset() def reset_decoder_and_chunk(self): """reset decoder and chunk state for an new audio """ - self.decoder.reset_decoder(batch_size=1) - # init state box, for new audio request - self.chunk_state_h_box = np.zeros( - (self.config.num_rnn_layers, 1, self.config.rnn_layer_size), - dtype=float32) - self.chunk_state_c_box = np.zeros( - (self.config.num_rnn_layers, 1, self.config.rnn_layer_size), - dtype=float32) + if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type: + self.decoder.reset_decoder(batch_size=1) + # init state box, for new audio request + self.chunk_state_h_box = np.zeros( + (self.config.num_rnn_layers, 1, self.config.rnn_layer_size), + dtype=float32) + self.chunk_state_c_box = np.zeros( + (self.config.num_rnn_layers, 1, self.config.rnn_layer_size), + dtype=float32) + elif "conformer" in self.model_type or "transformer" in self.model_type: + self.transformer_decode_reset() def decode_one_chunk(self, x_chunk, x_chunk_lens, model_type: str): """decode one chunk @@ -174,8 +762,9 @@ class ASRServerExecutor(ASRExecutor): model_type (str): online model type Returns: - [type]: [description] + str: one best result """ + logger.info("start to decoce chunk by chunk") if "deepspeech2online" in model_type: input_names = self.am_predictor.get_input_names() audio_handle = self.am_predictor.get_input_handle(input_names[0]) @@ -214,28 +803,141 @@ class ASRServerExecutor(ASRExecutor): self.decoder.next(output_chunk_probs, output_chunk_lens) trans_best, trans_beam = self.decoder.decode() - + logger.info(f"decode one best result: {trans_best[0]}") return trans_best[0] elif "conformer" in model_type or "transformer" in model_type: - raise Exception("invalid model name") + try: + logger.info( + f"we will use the transformer like model : {self.model_type}" + ) + self.advanced_decoding(x_chunk, x_chunk_lens) + self.update_result() + + return self.result_transcripts[0] + except Exception as e: + logger.exception(e) else: raise Exception("invalid model name") - def _pcm16to32(self, audio): - """pcm int16 to float32 + def advanced_decoding(self, xs: paddle.Tensor, x_chunk_lens): + logger.info("start to decode with advanced_decoding method") + encoder_out, encoder_mask = self.encoder_forward(xs) + ctc_probs = self.model.ctc.log_softmax( + encoder_out) # (1, maxlen, vocab_size) + ctc_probs = ctc_probs.squeeze(0) + self.searcher.search(ctc_probs, xs.place) + # update the one best result + self.hyps = self.searcher.get_one_best_hyps() - Args: - audio(numpy.array): numpy.int16 + # now we supprot ctc_prefix_beam_search and attention_rescoring + if "attention_rescoring" in self.config.decode.decoding_method: + self.rescoring(encoder_out, xs.place) - Returns: - audio(numpy.array): numpy.float32 - """ - if audio.dtype == np.int16: - audio = audio.astype("float32") - bits = np.iinfo(np.int16).bits - audio = audio / (2**(bits - 1)) - return audio + def encoder_forward(self, xs): + logger.info("get the model out from the feat") + cfg = self.config.decode + decoding_chunk_size = cfg.decoding_chunk_size + num_decoding_left_chunks = cfg.num_decoding_left_chunks + + assert decoding_chunk_size > 0 + subsampling = self.model.encoder.embed.subsampling_rate + context = self.model.encoder.embed.right_context + 1 + stride = subsampling * decoding_chunk_size + + # decoding window for model + decoding_window = (decoding_chunk_size - 1) * subsampling + context + num_frames = xs.shape[1] + required_cache_size = decoding_chunk_size * num_decoding_left_chunks + + logger.info("start to do model forward") + outputs = [] + + # num_frames - context + 1 ensure that current frame can get context window + for cur in range(0, num_frames - context + 1, stride): + end = min(cur + decoding_window, num_frames) + chunk_xs = xs[:, cur:end, :] + (y, self.subsampling_cache, self.elayers_output_cache, + self.conformer_cnn_cache) = self.model.encoder.forward_chunk( + chunk_xs, self.offset, required_cache_size, + self.subsampling_cache, self.elayers_output_cache, + self.conformer_cnn_cache) + outputs.append(y) + self.offset += y.shape[1] + + ys = paddle.cat(outputs, 1) + masks = paddle.ones([1, ys.shape[1]], dtype=paddle.bool) + masks = masks.unsqueeze(1) + return ys, masks + + def rescoring(self, encoder_out, device): + logger.info("start to rescoring the hyps") + beam_size = self.config.decode.beam_size + hyps = self.searcher.get_hyps() + assert len(hyps) == beam_size + + hyp_list = [] + for hyp in hyps: + hyp_content = hyp[0] + # Prevent the hyp is empty + if len(hyp_content) == 0: + hyp_content = (self.model.ctc.blank_id, ) + hyp_content = paddle.to_tensor( + hyp_content, place=device, dtype=paddle.long) + hyp_list.append(hyp_content) + hyps_pad = pad_sequence(hyp_list, True, self.model.ignore_id) + hyps_lens = paddle.to_tensor( + [len(hyp[0]) for hyp in hyps], place=device, + dtype=paddle.long) # (beam_size,) + hyps_pad, _ = add_sos_eos(hyps_pad, self.model.sos, self.model.eos, + self.model.ignore_id) + hyps_lens = hyps_lens + 1 # Add at begining + + encoder_out = encoder_out.repeat(beam_size, 1, 1) + encoder_mask = paddle.ones( + (beam_size, 1, encoder_out.shape[1]), dtype=paddle.bool) + decoder_out, _ = self.model.decoder( + encoder_out, encoder_mask, hyps_pad, + hyps_lens) # (beam_size, max_hyps_len, vocab_size) + # ctc score in ln domain + decoder_out = paddle.nn.functional.log_softmax(decoder_out, axis=-1) + decoder_out = decoder_out.numpy() + + # Only use decoder score for rescoring + best_score = -float('inf') + best_index = 0 + # hyps is List[(Text=List[int], Score=float)], len(hyps)=beam_size + for i, hyp in enumerate(hyps): + score = 0.0 + for j, w in enumerate(hyp[0]): + score += decoder_out[i][j][w] + # last decoder output token is `eos`, for laste decoder input token. + score += decoder_out[i][len(hyp[0])][self.model.eos] + # add ctc score (which in ln domain) + score += hyp[1] * self.config.decode.ctc_weight + if score > best_score: + best_score = score + best_index = i + + # update the one best result + self.hyps = [hyps[best_index][0]] + return hyps[best_index][0] + + def transformer_decode_reset(self): + self.subsampling_cache = None + self.elayers_output_cache = None + self.conformer_cnn_cache = None + self.offset = 0 + # decoding reset + self.searcher.reset() + + def update_result(self): + logger.info("update the final result") + hyps = self.hyps + self.result_transcripts = [ + self.text_feature.defeaturize(hyp) for hyp in hyps + ] + self.result_tokenids = [hyp for hyp in hyps] def extract_feat(self, samples, sample_rate): """extract feat @@ -248,34 +950,58 @@ class ASRServerExecutor(ASRExecutor): x_chunk (numpy.array): shape[B, T, D] x_chunk_lens (numpy.array): shape[B] """ - # pcm16 -> pcm 32 - samples = self._pcm16to32(samples) - # read audio - speech_segment = SpeechSegment.from_pcm( - samples, sample_rate, transcript=" ") - # audio augment - self.collate_fn_test.augmentation.transform_audio(speech_segment) + if "deepspeech2online" in self.model_type: + # pcm16 -> pcm 32 + samples = pcm2float(samples) + # read audio + speech_segment = SpeechSegment.from_pcm( + samples, sample_rate, transcript=" ") + # audio augment + self.collate_fn_test.augmentation.transform_audio(speech_segment) - # extract speech feature - spectrum, transcript_part = self.collate_fn_test._speech_featurizer.featurize( - speech_segment, self.collate_fn_test.keep_transcription_text) - # CMVN spectrum - if self.collate_fn_test._normalizer: - spectrum = self.collate_fn_test._normalizer.apply(spectrum) + # extract speech feature + spectrum, transcript_part = self.collate_fn_test._speech_featurizer.featurize( + speech_segment, self.collate_fn_test.keep_transcription_text) + # CMVN spectrum + if self.collate_fn_test._normalizer: + spectrum = self.collate_fn_test._normalizer.apply(spectrum) - # spectrum augment - audio = self.collate_fn_test.augmentation.transform_feature(spectrum) + # spectrum augment + audio = self.collate_fn_test.augmentation.transform_feature( + spectrum) - audio_len = audio.shape[0] - audio = paddle.to_tensor(audio, dtype='float32') - # audio_len = paddle.to_tensor(audio_len) - audio = paddle.unsqueeze(audio, axis=0) + audio_len = audio.shape[0] + audio = paddle.to_tensor(audio, dtype='float32') + # audio_len = paddle.to_tensor(audio_len) + audio = paddle.unsqueeze(audio, axis=0) - x_chunk = audio.numpy() - x_chunk_lens = np.array([audio_len]) + x_chunk = audio.numpy() + x_chunk_lens = np.array([audio_len]) - return x_chunk, x_chunk_lens + return x_chunk, x_chunk_lens + elif "conformer_online" in self.model_type: + + if sample_rate != self.sample_rate: + logger.info(f"audio sample rate {sample_rate} is not match," + "the model sample_rate is {self.sample_rate}") + logger.info(f"ASR Engine use the {self.model_type} to process") + logger.info("Create the preprocess instance") + preprocess_conf = self.config.preprocess_config + preprocess_args = {"train": False} + preprocessing = Transformation(preprocess_conf) + + logger.info("Read the audio file") + logger.info(f"audio shape: {samples.shape}") + # fbank + x_chunk = preprocessing(samples, **preprocess_args) + x_chunk_lens = paddle.to_tensor(x_chunk.shape[0]) + x_chunk = paddle.to_tensor( + x_chunk, dtype="float32").unsqueeze(axis=0) + logger.info( + f"process the audio feature success, feat shape: {x_chunk.shape}" + ) + return x_chunk, x_chunk_lens class ASREngine(BaseEngine): @@ -287,6 +1013,7 @@ class ASREngine(BaseEngine): def __init__(self): super(ASREngine, self).__init__() + logger.info("create the online asr engine instance") def init(self, config: dict) -> bool: """init engine resource @@ -315,7 +1042,10 @@ class ASREngine(BaseEngine): logger.info("Initialize ASR server engine successfully.") return True - def preprocess(self, samples, sample_rate): + def preprocess(self, + samples, + sample_rate, + model_type="deepspeech2online_aishell-zh-16k"): """preprocess Args: @@ -326,6 +1056,7 @@ class ASREngine(BaseEngine): x_chunk (numpy.array): shape[B, T, D] x_chunk_lens (numpy.array): shape[B] """ + # if "deepspeech" in model_type: x_chunk, x_chunk_lens = self.executor.extract_feat(samples, sample_rate) return x_chunk, x_chunk_lens diff --git a/paddlespeech/server/engine/asr/online/ctc_search.py b/paddlespeech/server/engine/asr/online/ctc_search.py new file mode 100644 index 000000000..8aee0a503 --- /dev/null +++ b/paddlespeech/server/engine/asr/online/ctc_search.py @@ -0,0 +1,128 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections import defaultdict +import paddle +from paddlespeech.cli.log import logger +from paddlespeech.s2t.utils.utility import log_add + +__all__ = ['CTCPrefixBeamSearch'] + + +class CTCPrefixBeamSearch: + def __init__(self, config): + """Implement the ctc prefix beam search + + Args: + config (yacs.config.CfgNode): _description_ + """ + self.config = config + self.reset() + + @paddle.no_grad() + def search(self, ctc_probs, device, blank_id=0): + """ctc prefix beam search method decode a chunk feature + + Args: + xs (paddle.Tensor): feature data + ctc_probs (paddle.Tensor): the ctc probability of all the tokens + device (paddle.fluid.core_avx.Place): the feature host device, such as CUDAPlace(0). + blank_id (int, optional): the blank id in the vocab. Defaults to 0. + + Returns: + list: the search result + """ + # decode + logger.info("start to ctc prefix search") + + batch_size = 1 + beam_size = self.config.beam_size + maxlen = ctc_probs.shape[0] + + assert len(ctc_probs.shape) == 2 + + # cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score)) + # blank_ending_score and none_blank_ending_score in ln domain + if self.cur_hyps is None: + self.cur_hyps = [(tuple(), (0.0, -float('inf')))] + # 2. CTC beam search step by step + for t in range(0, maxlen): + logp = ctc_probs[t] # (vocab_size,) + # key: prefix, value (pb, pnb), default value(-inf, -inf) + next_hyps = defaultdict(lambda: (-float('inf'), -float('inf'))) + + # 2.1 First beam prune: select topk best + # do token passing process + top_k_logp, top_k_index = logp.topk(beam_size) # (beam_size,) + for s in top_k_index: + s = s.item() + ps = logp[s].item() + for prefix, (pb, pnb) in self.cur_hyps: + last = prefix[-1] if len(prefix) > 0 else None + if s == blank_id: # blank + n_pb, n_pnb = next_hyps[prefix] + n_pb = log_add([n_pb, pb + ps, pnb + ps]) + next_hyps[prefix] = (n_pb, n_pnb) + elif s == last: + # Update *ss -> *s; + n_pb, n_pnb = next_hyps[prefix] + n_pnb = log_add([n_pnb, pnb + ps]) + next_hyps[prefix] = (n_pb, n_pnb) + # Update *s-s -> *ss, - is for blank + n_prefix = prefix + (s, ) + n_pb, n_pnb = next_hyps[n_prefix] + n_pnb = log_add([n_pnb, pb + ps]) + next_hyps[n_prefix] = (n_pb, n_pnb) + else: + n_prefix = prefix + (s, ) + n_pb, n_pnb = next_hyps[n_prefix] + n_pnb = log_add([n_pnb, pb + ps, pnb + ps]) + next_hyps[n_prefix] = (n_pb, n_pnb) + + # 2.2 Second beam prune + next_hyps = sorted( + next_hyps.items(), + key=lambda x: log_add(list(x[1])), + reverse=True) + self.cur_hyps = next_hyps[:beam_size] + + self.hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in self.cur_hyps] + logger.info("ctc prefix search success") + return self.hyps + + def get_one_best_hyps(self): + """Return the one best result + + Returns: + list: the one best result + """ + return [self.hyps[0][0]] + + def get_hyps(self): + """Return the search hyps + + Returns: + list: return the search hyps + """ + return self.hyps + + def reset(self): + """Rest the search cache value + """ + self.cur_hyps = None + self.hyps = None + + def finalize_search(self): + """do nothing in ctc_prefix_beam_search + """ + pass diff --git a/paddlespeech/server/engine/engine_factory.py b/paddlespeech/server/engine/engine_factory.py index 2a39fb79b..e147a29a6 100644 --- a/paddlespeech/server/engine/engine_factory.py +++ b/paddlespeech/server/engine/engine_factory.py @@ -34,6 +34,9 @@ class EngineFactory(object): elif engine_name == 'tts' and engine_type == 'python': from paddlespeech.server.engine.tts.python.tts_engine import TTSEngine return TTSEngine() + elif engine_name == 'tts' and engine_type == 'online': + from paddlespeech.server.engine.tts.online.tts_engine import TTSEngine + return TTSEngine() elif engine_name == 'cls' and engine_type == 'inference': from paddlespeech.server.engine.cls.paddleinference.cls_engine import CLSEngine return CLSEngine() diff --git a/paddlespeech/server/engine/tts/online/__init__.py b/paddlespeech/server/engine/tts/online/__init__.py new file mode 100644 index 000000000..97043fd7b --- /dev/null +++ b/paddlespeech/server/engine/tts/online/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/paddlespeech/server/engine/tts/online/tts_engine.py b/paddlespeech/server/engine/tts/online/tts_engine.py new file mode 100644 index 000000000..c9135b884 --- /dev/null +++ b/paddlespeech/server/engine/tts/online/tts_engine.py @@ -0,0 +1,628 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import base64 +import math +import os +import time +from typing import Optional + +import numpy as np +import paddle +import yaml +from yacs.config import CfgNode + +from paddlespeech.cli.log import logger +from paddlespeech.cli.tts.infer import TTSExecutor +from paddlespeech.cli.utils import download_and_decompress +from paddlespeech.cli.utils import MODEL_HOME +from paddlespeech.s2t.utils.dynamic_import import dynamic_import +from paddlespeech.server.engine.base_engine import BaseEngine +from paddlespeech.server.utils.audio_process import float2pcm +from paddlespeech.server.utils.util import denorm +from paddlespeech.server.utils.util import get_chunks +from paddlespeech.t2s.frontend import English +from paddlespeech.t2s.frontend.zh_frontend import Frontend +from paddlespeech.t2s.modules.normalizer import ZScore + +__all__ = ['TTSEngine'] + +# support online model +pretrained_models = { + # fastspeech2 + "fastspeech2_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip', + 'md5': + '637d28a5e53aa60275612ba4393d5f22', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_76000.pdz', + 'speech_stats': + 'speech_stats.npy', + 'phones_dict': + 'phone_id_map.txt', + }, + "fastspeech2_cnndecoder_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_ckpt_1.0.0.zip', + 'md5': + '6eb28e22ace73e0ebe7845f86478f89f', + 'config': + 'cnndecoder.yaml', + 'ckpt': + 'snapshot_iter_153000.pdz', + 'speech_stats': + 'speech_stats.npy', + 'phones_dict': + 'phone_id_map.txt', + }, + + # mb_melgan + "mb_melgan_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip', + 'md5': + 'ee5f0604e20091f0d495b6ec4618b90d', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_1000000.pdz', + 'speech_stats': + 'feats_stats.npy', + }, + + # hifigan + "hifigan_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip', + 'md5': + 'dd40a3d88dfcf64513fba2f0f961ada6', + 'config': + 'default.yaml', + 'ckpt': + 'snapshot_iter_2500000.pdz', + 'speech_stats': + 'feats_stats.npy', + }, +} + +model_alias = { + # acoustic model + "fastspeech2": + "paddlespeech.t2s.models.fastspeech2:FastSpeech2", + "fastspeech2_inference": + "paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference", + + # voc + "mb_melgan": + "paddlespeech.t2s.models.melgan:MelGANGenerator", + "mb_melgan_inference": + "paddlespeech.t2s.models.melgan:MelGANInference", + "hifigan": + "paddlespeech.t2s.models.hifigan:HiFiGANGenerator", + "hifigan_inference": + "paddlespeech.t2s.models.hifigan:HiFiGANInference", +} + +__all__ = ['TTSEngine'] + + +class TTSServerExecutor(TTSExecutor): + def __init__(self, am_block, am_pad, voc_block, voc_pad): + super().__init__() + self.am_block = am_block + self.am_pad = am_pad + self.voc_block = voc_block + self.voc_pad = voc_pad + + def get_model_info(self, + field: str, + model_name: str, + ckpt: Optional[os.PathLike], + stat: Optional[os.PathLike]): + """get model information + + Args: + field (str): am or voc + model_name (str): model type, support fastspeech2, higigan, mb_melgan + ckpt (Optional[os.PathLike]): ckpt file + stat (Optional[os.PathLike]): stat file, including mean and standard deviation + + Returns: + [module]: model module + [Tensor]: mean + [Tensor]: standard deviation + """ + + model_class = dynamic_import(model_name, model_alias) + + if field == "am": + odim = self.am_config.n_mels + model = model_class( + idim=self.vocab_size, odim=odim, **self.am_config["model"]) + model.set_state_dict(paddle.load(ckpt)["main_params"]) + + elif field == "voc": + model = model_class(**self.voc_config["generator_params"]) + model.set_state_dict(paddle.load(ckpt)["generator_params"]) + model.remove_weight_norm() + + else: + logger.error("Please set correct field, am or voc") + + model.eval() + model_mu, model_std = np.load(stat) + model_mu = paddle.to_tensor(model_mu) + model_std = paddle.to_tensor(model_std) + + return model, model_mu, model_std + + def _get_pretrained_path(self, tag: str) -> os.PathLike: + """ + Download and returns pretrained resources path of current task. + """ + support_models = list(pretrained_models.keys()) + assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format( + tag, '\n\t\t'.join(support_models)) + + res_path = os.path.join(MODEL_HOME, tag) + decompressed_path = download_and_decompress(pretrained_models[tag], + res_path) + decompressed_path = os.path.abspath(decompressed_path) + logger.info( + 'Use pretrained model stored in: {}'.format(decompressed_path)) + return decompressed_path + + def _init_from_path( + self, + am: str='fastspeech2_csmsc', + am_config: Optional[os.PathLike]=None, + am_ckpt: Optional[os.PathLike]=None, + am_stat: Optional[os.PathLike]=None, + phones_dict: Optional[os.PathLike]=None, + tones_dict: Optional[os.PathLike]=None, + speaker_dict: Optional[os.PathLike]=None, + voc: str='mb_melgan_csmsc', + voc_config: Optional[os.PathLike]=None, + voc_ckpt: Optional[os.PathLike]=None, + voc_stat: Optional[os.PathLike]=None, + lang: str='zh', ): + """ + Init model and other resources from a specific path. + """ + if hasattr(self, 'am_inference') and hasattr(self, 'voc_inference'): + logger.info('Models had been initialized.') + return + # am model info + am_tag = am + '-' + lang + if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None: + am_res_path = self._get_pretrained_path(am_tag) + self.am_res_path = am_res_path + self.am_config = os.path.join(am_res_path, + pretrained_models[am_tag]['config']) + self.am_ckpt = os.path.join(am_res_path, + pretrained_models[am_tag]['ckpt']) + self.am_stat = os.path.join( + am_res_path, pretrained_models[am_tag]['speech_stats']) + # must have phones_dict in acoustic + self.phones_dict = os.path.join( + am_res_path, pretrained_models[am_tag]['phones_dict']) + print("self.phones_dict:", self.phones_dict) + logger.info(am_res_path) + logger.info(self.am_config) + logger.info(self.am_ckpt) + else: + self.am_config = os.path.abspath(am_config) + self.am_ckpt = os.path.abspath(am_ckpt) + self.am_stat = os.path.abspath(am_stat) + self.phones_dict = os.path.abspath(phones_dict) + self.am_res_path = os.path.dirname(os.path.abspath(self.am_config)) + print("self.phones_dict:", self.phones_dict) + + self.tones_dict = None + self.speaker_dict = None + + # voc model info + voc_tag = voc + '-' + lang + if voc_ckpt is None or voc_config is None or voc_stat is None: + voc_res_path = self._get_pretrained_path(voc_tag) + self.voc_res_path = voc_res_path + self.voc_config = os.path.join(voc_res_path, + pretrained_models[voc_tag]['config']) + self.voc_ckpt = os.path.join(voc_res_path, + pretrained_models[voc_tag]['ckpt']) + self.voc_stat = os.path.join( + voc_res_path, pretrained_models[voc_tag]['speech_stats']) + logger.info(voc_res_path) + logger.info(self.voc_config) + logger.info(self.voc_ckpt) + else: + self.voc_config = os.path.abspath(voc_config) + self.voc_ckpt = os.path.abspath(voc_ckpt) + self.voc_stat = os.path.abspath(voc_stat) + self.voc_res_path = os.path.dirname( + os.path.abspath(self.voc_config)) + + # Init body. + with open(self.am_config) as f: + self.am_config = CfgNode(yaml.safe_load(f)) + with open(self.voc_config) as f: + self.voc_config = CfgNode(yaml.safe_load(f)) + + with open(self.phones_dict, "r") as f: + phn_id = [line.strip().split() for line in f.readlines()] + self.vocab_size = len(phn_id) + print("vocab_size:", self.vocab_size) + + # frontend + if lang == 'zh': + self.frontend = Frontend( + phone_vocab_path=self.phones_dict, + tone_vocab_path=self.tones_dict) + + elif lang == 'en': + self.frontend = English(phone_vocab_path=self.phones_dict) + print("frontend done!") + + # am infer info + self.am_name = am[:am.rindex('_')] + if self.am_name == "fastspeech2_cnndecoder": + self.am_inference, self.am_mu, self.am_std = self.get_model_info( + "am", "fastspeech2", self.am_ckpt, self.am_stat) + else: + am, am_mu, am_std = self.get_model_info("am", self.am_name, + self.am_ckpt, self.am_stat) + am_normalizer = ZScore(am_mu, am_std) + am_inference_class = dynamic_import(self.am_name + '_inference', + model_alias) + self.am_inference = am_inference_class(am_normalizer, am) + self.am_inference.eval() + print("acoustic model done!") + + # voc infer info + self.voc_name = voc[:voc.rindex('_')] + voc, voc_mu, voc_std = self.get_model_info("voc", self.voc_name, + self.voc_ckpt, self.voc_stat) + voc_normalizer = ZScore(voc_mu, voc_std) + voc_inference_class = dynamic_import(self.voc_name + '_inference', + model_alias) + self.voc_inference = voc_inference_class(voc_normalizer, voc) + self.voc_inference.eval() + print("voc done!") + + def get_phone(self, sentence, lang, merge_sentences, get_tone_ids): + tone_ids = None + if lang == 'zh': + input_ids = self.frontend.get_input_ids( + sentence, + merge_sentences=merge_sentences, + get_tone_ids=get_tone_ids) + phone_ids = input_ids["phone_ids"] + if get_tone_ids: + tone_ids = input_ids["tone_ids"] + elif lang == 'en': + input_ids = self.frontend.get_input_ids( + sentence, merge_sentences=merge_sentences) + phone_ids = input_ids["phone_ids"] + else: + print("lang should in {'zh', 'en'}!") + + def depadding(self, data, chunk_num, chunk_id, block, pad, upsample): + """ + Streaming inference removes the result of pad inference + """ + front_pad = min(chunk_id * block, pad) + # first chunk + if chunk_id == 0: + data = data[:block * upsample] + # last chunk + elif chunk_id == chunk_num - 1: + data = data[front_pad * upsample:] + # middle chunk + else: + data = data[front_pad * upsample:(front_pad + block) * upsample] + + return data + + @paddle.no_grad() + def infer( + self, + text: str, + lang: str='zh', + am: str='fastspeech2_csmsc', + spk_id: int=0, ): + """ + Model inference and result stored in self.output. + """ + + am_block = self.am_block + am_pad = self.am_pad + am_upsample = 1 + voc_block = self.voc_block + voc_pad = self.voc_pad + voc_upsample = self.voc_config.n_shift + # first_flag 用于标记首包 + first_flag = 1 + + get_tone_ids = False + merge_sentences = False + frontend_st = time.time() + if lang == 'zh': + input_ids = self.frontend.get_input_ids( + text, + merge_sentences=merge_sentences, + get_tone_ids=get_tone_ids) + phone_ids = input_ids["phone_ids"] + if get_tone_ids: + tone_ids = input_ids["tone_ids"] + elif lang == 'en': + input_ids = self.frontend.get_input_ids( + text, merge_sentences=merge_sentences) + phone_ids = input_ids["phone_ids"] + else: + print("lang should in {'zh', 'en'}!") + frontend_et = time.time() + self.frontend_time = frontend_et - frontend_st + + for i in range(len(phone_ids)): + part_phone_ids = phone_ids[i] + voc_chunk_id = 0 + + # fastspeech2_csmsc + if am == "fastspeech2_csmsc": + # am + mel = self.am_inference(part_phone_ids) + if first_flag == 1: + first_am_et = time.time() + self.first_am_infer = first_am_et - frontend_et + + # voc streaming + mel_chunks = get_chunks(mel, voc_block, voc_pad, "voc") + voc_chunk_num = len(mel_chunks) + voc_st = time.time() + for i, mel_chunk in enumerate(mel_chunks): + sub_wav = self.voc_inference(mel_chunk) + sub_wav = self.depadding(sub_wav, voc_chunk_num, i, + voc_block, voc_pad, voc_upsample) + if first_flag == 1: + first_voc_et = time.time() + self.first_voc_infer = first_voc_et - first_am_et + self.first_response_time = first_voc_et - frontend_st + first_flag = 0 + + yield sub_wav + + # fastspeech2_cnndecoder_csmsc + elif am == "fastspeech2_cnndecoder_csmsc": + # am + orig_hs, h_masks = self.am_inference.encoder_infer( + part_phone_ids) + + # streaming voc chunk info + mel_len = orig_hs.shape[1] + voc_chunk_num = math.ceil(mel_len / self.voc_block) + start = 0 + end = min(self.voc_block + self.voc_pad, mel_len) + + # streaming am + hss = get_chunks(orig_hs, self.am_block, self.am_pad, "am") + am_chunk_num = len(hss) + for i, hs in enumerate(hss): + before_outs, _ = self.am_inference.decoder(hs) + after_outs = before_outs + self.am_inference.postnet( + before_outs.transpose((0, 2, 1))).transpose((0, 2, 1)) + normalized_mel = after_outs[0] + sub_mel = denorm(normalized_mel, self.am_mu, self.am_std) + sub_mel = self.depadding(sub_mel, am_chunk_num, i, am_block, + am_pad, am_upsample) + + if i == 0: + mel_streaming = sub_mel + else: + mel_streaming = np.concatenate( + (mel_streaming, sub_mel), axis=0) + + # streaming voc + # 当流式AM推理的mel帧数大于流式voc推理的chunk size，开始进行流式voc 推理 + while (mel_streaming.shape[0] >= end and + voc_chunk_id < voc_chunk_num): + if first_flag == 1: + first_am_et = time.time() + self.first_am_infer = first_am_et - frontend_et + voc_chunk = mel_streaming[start:end, :] + voc_chunk = paddle.to_tensor(voc_chunk) + sub_wav = self.voc_inference(voc_chunk) + + sub_wav = self.depadding(sub_wav, voc_chunk_num, + voc_chunk_id, voc_block, + voc_pad, voc_upsample) + if first_flag == 1: + first_voc_et = time.time() + self.first_voc_infer = first_voc_et - first_am_et + self.first_response_time = first_voc_et - frontend_st + first_flag = 0 + + yield sub_wav + + voc_chunk_id += 1 + start = max(0, voc_chunk_id * voc_block - voc_pad) + end = min((voc_chunk_id + 1) * voc_block + voc_pad, + mel_len) + + else: + logger.error( + "Only support fastspeech2_csmsc or fastspeech2_cnndecoder_csmsc on streaming tts." + ) + + self.final_response_time = time.time() - frontend_st + + +class TTSEngine(BaseEngine): + """TTS server engine + + Args: + metaclass: Defaults to Singleton. + """ + + def __init__(self, name=None): + """Initialize TTS server engine + """ + super().__init__() + + def init(self, config: dict) -> bool: + self.config = config + assert ( + config.am == "fastspeech2_csmsc" or + config.am == "fastspeech2_cnndecoder_csmsc" + ) and ( + config.voc == "hifigan_csmsc" or config.voc == "mb_melgan_csmsc" + ), 'Please check config, am support: fastspeech2, voc support: hifigan_csmsc-zh or mb_melgan_csmsc.' + + assert ( + config.voc_block > 0 and config.voc_pad > 0 + ), "Please set correct voc_block and voc_pad, they should be more than 0." + + try: + if self.config.device: + self.device = self.config.device + else: + self.device = paddle.get_device() + paddle.set_device(self.device) + except Exception as e: + logger.error( + "Set device failed, please check if device is already used and the parameter 'device' in the yaml file" + ) + logger.error("Initialize TTS server engine Failed on device: %s." % + (self.device)) + return False + + self.executor = TTSServerExecutor(config.am_block, config.am_pad, + config.voc_block, config.voc_pad) + + try: + self.executor._init_from_path( + am=self.config.am, + am_config=self.config.am_config, + am_ckpt=self.config.am_ckpt, + am_stat=self.config.am_stat, + phones_dict=self.config.phones_dict, + tones_dict=self.config.tones_dict, + speaker_dict=self.config.speaker_dict, + voc=self.config.voc, + voc_config=self.config.voc_config, + voc_ckpt=self.config.voc_ckpt, + voc_stat=self.config.voc_stat, + lang=self.config.lang) + except Exception as e: + logger.error("Failed to get model related files.") + logger.error("Initialize TTS server engine Failed on device: %s." % + (self.device)) + return False + + logger.info("Initialize TTS server engine successfully on device: %s." % + (self.device)) + + # warm up + try: + self.warm_up() + except Exception as e: + logger.error("Failed to warm up on tts engine.") + return False + + return True + + def warm_up(self): + """warm up + """ + if self.config.lang == 'zh': + sentence = "您好，欢迎使用语音合成服务。" + if self.config.lang == 'en': + sentence = "Hello and welcome to the speech synthesis service." + logger.info( + "*******************************warm up ********************************" + ) + for i in range(3): + for wav in self.executor.infer( + text=sentence, + lang=self.config.lang, + am=self.config.am, + spk_id=0, ): + logger.info( + f"The first response time of the {i} warm up: {self.executor.first_response_time} s" + ) + break + logger.info( + "**********************************************************************" + ) + + def preprocess(self, text_bese64: str=None, text_bytes: bytes=None): + # Convert byte to text + if text_bese64: + text_bytes = base64.b64decode(text_bese64) # base64 to bytes + text = text_bytes.decode('utf-8') # bytes to text + + return text + + def run(self, + sentence: str, + spk_id: int=0, + speed: float=1.0, + volume: float=1.0, + sample_rate: int=0, + save_path: str=None): + """ run include inference and postprocess. + + Args: + sentence (str): text to be synthesized + spk_id (int, optional): speaker id for multi-speaker speech synthesis. Defaults to 0. + speed (float, optional): speed. Defaults to 1.0. + volume (float, optional): volume. Defaults to 1.0. + sample_rate (int, optional): target sample rate for synthesized audio, + 0 means the same as the model sampling rate. Defaults to 0. + save_path (str, optional): The save path of the synthesized audio. + None means do not save audio. Defaults to None. + + Returns: + wav_base64: The base64 format of the synthesized audio. + """ + + wav_list = [] + + for wav in self.executor.infer( + text=sentence, + lang=self.config.lang, + am=self.config.am, + spk_id=spk_id, ): + + # wav type: float32, convert to pcm (base64) + wav = float2pcm(wav) # float32 to int16 + wav_bytes = wav.tobytes() # to bytes + wav_base64 = base64.b64encode(wav_bytes).decode('utf8') # to base64 + wav_list.append(wav) + + yield wav_base64 + + wav_all = np.concatenate(wav_list, axis=0) + duration = len(wav_all) / self.executor.am_config.fs + logger.info(f"sentence: {sentence}") + logger.info(f"The durations of audio is: {duration} s") + logger.info( + f"first response time: {self.executor.first_response_time} s") + logger.info( + f"final response time: {self.executor.final_response_time} s") + logger.info(f"RTF: {self.executor.final_response_time / duration}") + logger.info( + f"Other info: front time: {self.executor.frontend_time} s, first am infer time: {self.executor.first_am_infer} s, first voc infer time: {self.executor.first_voc_infer} s," + ) diff --git a/paddlespeech/server/restful/tts_api.py b/paddlespeech/server/restful/tts_api.py index 4e9bbe23e..d1268428a 100644 --- a/paddlespeech/server/restful/tts_api.py +++ b/paddlespeech/server/restful/tts_api.py @@ -15,6 +15,7 @@ import traceback from typing import Union from fastapi import APIRouter +from fastapi.responses import StreamingResponse from paddlespeech.cli.log import logger from paddlespeech.server.engine.engine_pool import get_engine_pool @@ -125,3 +126,14 @@ def tts(request_body: TTSRequest): traceback.print_exc() return response + + +@router.post("/paddlespeech/streaming/tts") +async def stream_tts(request_body: TTSRequest): + text = request_body.text + + engine_pool = get_engine_pool() + tts_engine = engine_pool['tts'] + logger.info("Get tts engine successfully.") + + return StreamingResponse(tts_engine.run(sentence=text)) diff --git a/paddlespeech/server/tests/__init__.py b/paddlespeech/server/tests/__init__.py new file mode 100644 index 000000000..97043fd7b --- /dev/null +++ b/paddlespeech/server/tests/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/paddlespeech/server/tests/asr/__init__.py b/paddlespeech/server/tests/asr/__init__.py new file mode 100644 index 000000000..97043fd7b --- /dev/null +++ b/paddlespeech/server/tests/asr/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/paddlespeech/server/tests/asr/offline/__init__.py b/paddlespeech/server/tests/asr/offline/__init__.py new file mode 100644 index 000000000..97043fd7b --- /dev/null +++ b/paddlespeech/server/tests/asr/offline/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/paddlespeech/server/tests/asr/http_client.py b/paddlespeech/server/tests/asr/offline/http_client.py similarity index 100% rename from paddlespeech/server/tests/asr/http_client.py rename to paddlespeech/server/tests/asr/offline/http_client.py diff --git a/paddlespeech/server/tests/asr/online/README_cn.md b/paddlespeech/server/tests/asr/online/README_cn.md new file mode 100644 index 000000000..3e7d7a15c --- /dev/null +++ b/paddlespeech/server/tests/asr/online/README_cn.md @@ -0,0 +1,49 @@ +([简体中文](./README_cn.md)|English) + +# 语音服务 + +## 介绍 +本文档介绍如何使用流式ASR的三种不同客户端:网页、麦克风、Python模拟流式服务。 + + +## 使用方法 +### 1. 安装 +请看 [安装文档](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md). + +推荐使用 **paddlepaddle 2.2.1** 或以上版本。 +你可以从 medium，hard 三中方式中选择一种方式安装 PaddleSpeech。 + + +### 2. 准备测试文件 + +这个 ASR client 的输入应该是一个 WAV 文件（`.wav`），并且采样率必须与模型的采样率相同。 + +可以下载此 ASR client的示例音频： +```bash +wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav +``` + +### 2. 流式 ASR 客户端使用方法 + +- Python模拟流式服务命令行 + ``` + + # 流式ASR + paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8091 --input ./zh.wav + + ``` + + +- 麦克风 + ``` + # 直接调用麦克风设备 + python microphone_client.py + + ``` + + +- 网页 + ``` + # 进入web目录后参考相关readme.md + + ``` diff --git a/paddlespeech/server/tests/asr/online/__init__.py b/paddlespeech/server/tests/asr/online/__init__.py new file mode 100644 index 000000000..97043fd7b --- /dev/null +++ b/paddlespeech/server/tests/asr/online/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/paddlespeech/server/tests/asr/online/web/app.py b/paddlespeech/server/tests/asr/online/web/app.py new file mode 100644 index 000000000..22993c08e --- /dev/null +++ b/paddlespeech/server/tests/asr/online/web/app.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright 2021 Mobvoi Inc. All Rights Reserved. +# Author: zhendong.peng@mobvoi.com (Zhendong Peng) +import argparse + +from flask import Flask +from flask import render_template + +parser = argparse.ArgumentParser(description='training your network') +parser.add_argument('--port', default=19999, type=int, help='port id') +args = parser.parse_args() + +app = Flask(__name__) + + +@app.route('/') +def index(): + return render_template('index.html') + + +if __name__ == '__main__': + app.run(host='0.0.0.0', port=args.port, debug=True) diff --git a/paddlespeech/server/tests/asr/online/web/paddle_web_demo.png b/paddlespeech/server/tests/asr/online/web/paddle_web_demo.png new file mode 100644 index 000000000..214edffd0 Binary files /dev/null and b/paddlespeech/server/tests/asr/online/web/paddle_web_demo.png differ diff --git a/paddlespeech/server/tests/asr/online/web/readme.md b/paddlespeech/server/tests/asr/online/web/readme.md new file mode 100644 index 000000000..8310a2571 --- /dev/null +++ b/paddlespeech/server/tests/asr/online/web/readme.md @@ -0,0 +1,18 @@ +# paddlespeech serving 网页Demo + +- 感谢[wenet](https://github.com/wenet-e2e/wenet)团队的前端demo代码. + + +## 使用方法 +### 1. 在本地电脑启动网页服务 + ``` + python app.py + + ``` + +### 2. 本地电脑浏览器 + +在浏览器中输入127.0.0.1:19999 即可看到相关网页Demo。 + +![图片](./paddle_web_demo.png) + diff --git a/paddlespeech/server/tests/asr/online/web/static/css/font-awesome.min.css b/paddlespeech/server/tests/asr/online/web/static/css/font-awesome.min.css new file mode 100644 index 000000000..540440ce8 --- /dev/null +++ b/paddlespeech/server/tests/asr/online/web/static/css/font-awesome.min.css @@ -0,0 +1,4 @@ +/*! + * Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome + * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) + */@font-face{font-family:'FontAwesome';src:url('../fonts/fontawesome-webfont.eot?v=4.7.0');src:url('../fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') 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+h2 { + font-size: 14px; +} + +h3 { + font-size: 14px; +} + +ul, +ol, +li { + list-style: none; +} + +a { + text-decoration: none; +} + +a:hover { + text-decoration: none; +} + +fieldset, +img { + border: none; +} + +table { + border-collapse: collapse; + border-spacing: 0; +} + +i { + font-style: normal; +} + +label { + position: inherit; +} + +.clearfix:after { + content: "."; + display: block; + height: 0; + clear: both; + visibility: hidden; +} + +.clearfix { + zoom: 1; + display: block; +} + +html, +body { + font-family: Tahoma, Arial, 'microsoft yahei', 'Roboto', 'Droid Sans', 'Helvetica Neue', 'Droid Sans Fallback', 'Heiti SC', 'Hiragino Sans GB', 'Simsun', 'sans-self'; +} + + + +.audio-banner { + width: 100%; + overflow: auto; + padding: 0; + background: url('../image/voice-dictation.svg'); + background-size: cover; +} +.weaper { + width: 1200px; + height: 155px; + margin: 72px auto; +} +.text-content { + width: 670px; + height: 100%; + float: left; +} +.text-content .title { + font-size: 34px; + font-family: 'PingFangSC-Medium'; + font-weight: 500; + color: rgba(255, 255, 255, 1); + line-height: 48px; +} +.text-content .con { + font-size: 16px; + font-family: PingFangSC-Light; + font-weight: 300; + color: rgba(255, 255, 255, 1); + line-height: 30px; +} +.img-con { + width: 416px; + height: 100%; + float: right; +} +.img-con img { + width: 100%; + height: 100%; +} +.con-container { + margin-top: 34px; +} + +.audio-advantage { + background: #f8f9fa; +} +.asr-advantage { + width: 1200px; + margin: 0 auto; +} +.asr-advantage h2 { + text-align: center; + font-size: 22px; + padding: 30px 0 0 0; +} +.asr-advantage > ul > li { + box-sizing: border-box; + padding: 0 16px; + width: 33%; + text-align: center; + margin-bottom: 35px; +} +.asr-advantage > ul > li .icons{ + margin-top: 10px; + margin-bottom: 20px; + width: 42px; + height: 42px; +} +.service-item-content { + margin-top: 35px; + display: flex; + justify-content: center; + flex-wrap: wrap; +} +.service-item-content img { + width: 160px; + vertical-align: bottom; +} +.service-item-content > li { + box-sizing: border-box; + padding: 0 16px; + width: 33%; + text-align: center; + margin-bottom: 35px; +} +.service-item-content > li .service-item-content-title { + line-height: 1.5; + font-weight: 700; + margin-top: 10px; +} +.service-item-content > li .service-item-content-desc { + margin-top: 5px; + line-height: 1.8; + color: #657384; +} + + +.audio-scene-con { + width: 100%; + padding-bottom: 84px; + background: #fff; +} +.audio-scene { + overflow: auto; + width: 1200px; + background: #fff; + text-align: center; + padding: 0; + margin: 0 auto; +} +.audio-scene h2 { + padding: 30px 0 0 0; + font-size: 22px; + text-align: center; +} + +.audio-experience { + width: 100%; + height: 538px; + background: #fff; + padding: 0; + margin: 0; + overflow: auto; +} +.asr-box { + width: 1200px; + height: 394px; + margin: 64px auto; +} +.asr-box h2 { + font-size: 22px; + text-align: center; + margin-bottom: 64px; +} +.voice-container { + position: relative; + width: 1200px; + height: 308px; + background: rgba(255, 255, 255, 1); + border-radius: 8px; + border: 1px solid rgba(225, 225, 225, 1); +} +.voice-container .voice { + height: 236px; + width: 100%; + border-radius: 8px; +} +.voice-container .voice textarea { + height: 100%; + width: 100%; + border: none; + outline: none; + border-radius: 8px; + padding: 25px; + font-size: 14px; + box-sizing: border-box; + resize: none; +} +.voice-input { + width: 100%; + height: 72px; + box-sizing: border-box; + padding-left: 35px; + background: rgba(242, 244, 245, 1); + border-radius: 8px; + line-height: 72px; +} +.voice-input .el-select { + width: 492px; +} +.start-voice { + display: inline-block; + margin-left: 10px; +} +.start-voice .time { + margin-right: 25px; +} +.asr-advantage > ul > li { + margin-bottom: 77px; +} +#msg { + width: 100%; + line-height: 40px; + font-size: 14px; + margin-left: 330px; +} +#captcha { + margin-left: 350px !important; + display: inline-block; + position: relative; +} +.black { + position: fixed; + width: 100%; + height: 100%; + z-index: 5; + background: rgba(0, 0, 0, 0.5); + top: 0; + left: 0; +} +.container { + position: fixed; + z-index: 6; + top: 25%; + left: 10%; +} +.audio-scene-con { + width: 100%; + padding-bottom: 84px; + background: #fff; +} +#sound { + color: #fff; + cursor: pointer; + background: #147ede; + padding: 10px; + margin-top: 30px; + margin-left: 135px; + width: 176px; + height: 30px !important; + text-align: center; + line-height: 30px !important; + border-radius: 10px; +} +.con-ten { + position: absolute; + width: 100%; + height: 100%; + z-index: 5; + background: #fff; + opacity: 0.5; + top: 0; + left: 0; +} +.websocket-url { + width: 320px; + height: 20px; + border: 1px solid #dcdfe6; + line-height: 20px; + padding: 10px; + border-radius: 4px; +} +.voice-btn { + color: #fff; + background-color: #409eff; + font-weight: 500; + padding: 12px 20px; + font-size: 14px; + border-radius: 4px; + border: 0; + cursor: pointer; +} +.voice-btn.end { + display: none; +} +.result-text { + background: #fff; + padding: 20px; +} +.voice-footer { + border-top: 1px solid #dddede; + background: #f7f9fa; + text-align: center; + margin-bottom: 8px; + color: #333; + font-size: 12px; + padding: 20px 0; +} + +/** line animate **/ +.time-box { + display: none; + margin-left: 10px; + width: 300px; +} +.total-time { + font-size: 14px; + color: #545454; +} +.voice-btn.end.show, +.time-box.show { + display: inline; +} +.start-taste-line { + margin-right: 20px; + display: inline-block; +} +.start-taste-line hr { + background-color: #187cff; + width: 3px; + height: 8px; + margin: 0 3px; + display: inline-block; + border: none; +} +.hr { + animation: note 0.2s ease-in-out; + animation-iteration-count: infinite; + animation-direction: alternate; +} +.hr-one { + animation-delay: -0.9s; +} +.hr-two { + animation-delay: -0.8s; +} +.hr-three { + animation-delay: -0.7s; +} 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All rights reserved. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/paddlespeech/server/tests/asr/online/web/static/fonts/fontawesome-webfont.ttf b/paddlespeech/server/tests/asr/online/web/static/fonts/fontawesome-webfont.ttf new file mode 100644 index 000000000..35acda2fa Binary files /dev/null and b/paddlespeech/server/tests/asr/online/web/static/fonts/fontawesome-webfont.ttf differ diff --git a/paddlespeech/server/tests/asr/online/web/static/fonts/fontawesome-webfont.woff b/paddlespeech/server/tests/asr/online/web/static/fonts/fontawesome-webfont.woff new file mode 100644 index 000000000..400014a4b Binary files /dev/null and b/paddlespeech/server/tests/asr/online/web/static/fonts/fontawesome-webfont.woff differ diff --git a/paddlespeech/server/tests/asr/online/web/static/fonts/fontawesome-webfont.woff2 b/paddlespeech/server/tests/asr/online/web/static/fonts/fontawesome-webfont.woff2 new file mode 100644 index 000000000..4d13fc604 Binary files /dev/null and b/paddlespeech/server/tests/asr/online/web/static/fonts/fontawesome-webfont.woff2 differ diff --git a/paddlespeech/server/tests/asr/online/web/static/image/PaddleSpeech_logo.png b/paddlespeech/server/tests/asr/online/web/static/image/PaddleSpeech_logo.png new file mode 100644 index 000000000..fb2527754 Binary files /dev/null and b/paddlespeech/server/tests/asr/online/web/static/image/PaddleSpeech_logo.png differ diff --git a/paddlespeech/server/tests/asr/online/web/static/image/voice-dictation.svg b/paddlespeech/server/tests/asr/online/web/static/image/voice-dictation.svg new file mode 100644 index 000000000..d35971499 --- /dev/null +++ b/paddlespeech/server/tests/asr/online/web/static/image/voice-dictation.svg @@ -0,0 +1,94 @@ + + + + 背景 + Created with Sketch. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/paddlespeech/server/tests/asr/online/web/static/js/SoundRecognizer.js b/paddlespeech/server/tests/asr/online/web/static/js/SoundRecognizer.js new file mode 100644 index 000000000..5ef3d2e89 --- /dev/null +++ b/paddlespeech/server/tests/asr/online/web/static/js/SoundRecognizer.js @@ -0,0 +1,133 @@ +SoundRecognizer = { + rec: null, + wave: null, + SampleRate: 16000, + testBitRate: 16, + isCloseRecorder: false, + SendInterval: 300, + realTimeSendTryType: 'pcm', + realTimeSendTryEncBusy: 0, + realTimeSendTryTime: 0, + realTimeSendTryNumber: 0, + transferUploadNumberMax: 0, + realTimeSendTryChunk: null, + soundType: "pcm", + init: function (config) { + this.soundType = config.soundType || 'pcm'; + this.SampleRate = config.sampleRate || 16000; + this.recwaveElm = config.recwaveElm || ''; + this.TransferUpload = config.translerCallBack || this.TransferProcess; + this.initRecorder(); + }, + RealTimeSendTryReset: function (type) { + this.realTimeSendTryType = type; + this.realTimeSendTryTime = 0; + }, + RealTimeSendTry: function (rec, isClose) { + var that = this; + var t1 = Date.now(), endT = 0, recImpl = Recorder.prototype; + if (this.realTimeSendTryTime == 0) { + this.realTimeSendTryTime = t1; + this.realTimeSendTryEncBusy = 0; + this.realTimeSendTryNumber = 0; + this.transferUploadNumberMax = 0; + this.realTimeSendTryChunk = null; + } + if (!isClose && t1 - this.realTimeSendTryTime < this.SendInterval) { + return;//控制缓冲达到指定间隔才进行传输 + } + this.realTimeSendTryTime = t1; + var number = ++this.realTimeSendTryNumber; + + //借用SampleData函数进行数据的连续处理，采样率转换是顺带的 + var chunk = Recorder.SampleData(rec.buffers, rec.srcSampleRate, this.SampleRate, this.realTimeSendTryChunk, { frameType: isClose ? "" : this.realTimeSendTryType }); + + //清理已处理完的缓冲数据，释放内存以支持长时间录音，最后完成录音时不能调用stop，因为数据已经被清掉了 + for (var i = this.realTimeSendTryChunk ? this.realTimeSendTryChunk.index : 0; i < chunk.index; i++) { + rec.buffers[i] = null; + } + this.realTimeSendTryChunk = chunk; + + //没有新数据，或结束时的数据量太小，不能进行mock转码 + if (chunk.data.length == 0 || isClose && chunk.data.length < 2000) { + this.TransferUpload(number, null, 0, null, isClose); + return; + } + //实时编码队列阻塞处理 + if (!isClose) { + if (this.realTimeSendTryEncBusy >= 2) { + console.log("编码队列阻塞，已丢弃一帧", 1); + return; + } + } + this.realTimeSendTryEncBusy++; + + //通过mock方法实时转码成mp3、wav + var encStartTime = Date.now(); + var recMock = Recorder({ + type: this.realTimeSendTryType + , sampleRate: this.SampleRate //采样率 + , bitRate: this.testBitRate //比特率 + }); + recMock.mock(chunk.data, chunk.sampleRate); + recMock.stop(function (blob, duration) { + that.realTimeSendTryEncBusy && (that.realTimeSendTryEncBusy--); + blob.encTime = Date.now() - encStartTime; + + //转码好就推入传输 + that.TransferUpload(number, blob, duration, recMock, isClose); + }, function (msg) { + that.realTimeSendTryEncBusy && (that.realTimeSendTryEncBusy--); + //转码错误？没想到什么时候会产生错误！ + console.log("不应该出现的错误:" + msg, 1); + }); + }, + recordClose: function () { + try { + this.rec.close(function () { + this.isCloseRecorder = true; + }); + this.RealTimeSendTry(this.rec, true);//最后一次发送 + } catch (ex) { + // recordClose(); + } + }, + recordEnd: function () { + try { + this.rec.stop(function (blob, time) { + this.recordClose(); + }, function (s) { + this.recordClose(); + }); + } catch (ex) { + } + }, + initRecorder: function () { + var that = this; + var rec = Recorder({ + type: that.soundType + , bitRate: that.testBitRate + , sampleRate: that.SampleRate + , onProcess: function (buffers, level, time, sampleRate) { + that.wave.input(buffers[buffers.length - 1], level, sampleRate); + that.RealTimeSendTry(rec, false);//推入实时处理，因为是unknown格式，这里简化函数调用，没有用到buffers和bufferSampleRate，因为这些数据和rec.buffers是完全相同的。 + } + }); + + rec.open(function () { + that.wave = Recorder.FrequencyHistogramView({ + elem: that.recwaveElm, lineCount: 90 + , position: 0 + , minHeight: 1 + , stripeEnable: false + }); + rec.start(); + that.isCloseRecorder = false; + that.RealTimeSendTryReset(that.soundType);//重置 + }); + this.rec = rec; + }, + TransferProcess: function (number, blobOrNull, duration, blobRec, isClose) { + + } +} \ No newline at end of file diff --git a/paddlespeech/server/tests/asr/online/web/static/js/jquery-3.2.1.min.js b/paddlespeech/server/tests/asr/online/web/static/js/jquery-3.2.1.min.js new file mode 100644 index 000000000..644d35e27 --- /dev/null +++ b/paddlespeech/server/tests/asr/online/web/static/js/jquery-3.2.1.min.js @@ -0,0 +1,4 @@ +/*! jQuery v3.2.1 | (c) JS Foundation and other contributors | jquery.org/license */ +!function(a,b){"use strict";"object"==typeof module&&"object"==typeof 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e)h.setRequestHeader(g,e[g]);c=function(a){return function(){c&&(c=d=h.onload=h.onerror=h.onabort=h.onreadystatechange=null,"abort"===a?h.abort():"error"===a?"number"!=typeof h.status?f(0,"error"):f(h.status,h.statusText):f(Rb[h.status]||h.status,h.statusText,"text"!==(h.responseType||"text")||"string"!=typeof h.responseText?{binary:h.response}:{text:h.responseText},h.getAllResponseHeaders()))}},h.onload=c(),d=h.onerror=c("error"),void 0!==h.onabort?h.onabort=d:h.onreadystatechange=function(){4===h.readyState&&a.setTimeout(function(){c&&d()})},c=c("abort");try{h.send(b.hasContent&&b.data||null)}catch(i){if(c)throw i}},abort:function(){c&&c()}}}),r.ajaxPrefilter(function(a){a.crossDomain&&(a.contents.script=!1)}),r.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\b(?:java|ecma)script\b/},converters:{"text script":function(a){return r.globalEval(a),a}}}),r.ajaxPrefilter("script",function(a){void 0===a.cache&&(a.cache=!1),a.crossDomain&&(a.type="GET")}),r.ajaxTransport("script",function(a){if(a.crossDomain){var b,c;return{send:function(e,f){b=r(" + + + + + + + + + + +
+
+
+
+

PaddleSpeech Serving简介

+

+ PaddleSpeech 是基于飞桨 PaddlePaddle 的语音方向的开源模型库，用于语音和音频中的各种关键任务的开发。PaddleSpeech Serving是基于python + fastapi 的语音算法模型的C/S类型后端服务，旨在统一paddle speech下的各语音算子来对外提供后端服务。 +

+
+
+ +
+
+
+
+
+

产品体验

+
+
+
+
+
+
+
+
+ WebSocket URL： + +
+ + +
+ 识别中， 秒后自动停止识别 +
+
+
+
+
此处显示识别结果
+
+
+
+
+
+ + + + \ No newline at end of file diff --git a/paddlespeech/server/tests/asr/online/websocket_client.py b/paddlespeech/server/tests/asr/online/websocket_client.py index 58b1a452c..49cbd7033 100644 --- a/paddlespeech/server/tests/asr/online/websocket_client.py +++ b/paddlespeech/server/tests/asr/online/websocket_client.py @@ -15,8 +15,10 @@ # -*- coding: UTF-8 -*- import argparse import asyncio +import codecs import json import logging +import os import numpy as np import soundfile @@ -32,34 +34,32 @@ class ASRAudioHandler: def read_wave(self, wavfile_path: str): samples, sample_rate = soundfile.read(wavfile_path, dtype='int16') x_len = len(samples) - chunk_stride = 40 * 16 #40ms, sample_rate = 16kHz - chunk_size = 80 * 16 #80ms, sample_rate = 16kHz - if (x_len - chunk_size) % chunk_stride != 0: - padding_len_x = chunk_stride - (x_len - chunk_size) % chunk_stride + chunk_size = 85 * 16 #80ms, sample_rate = 16kHz + if x_len % chunk_size!= 0: + padding_len_x = chunk_size - x_len % chunk_size else: padding_len_x = 0 padding = np.zeros((padding_len_x), dtype=samples.dtype) padded_x = np.concatenate([samples, padding], axis=0) - num_chunk = (x_len + padding_len_x - chunk_size) / chunk_stride + 1 + assert (x_len + padding_len_x) % chunk_size == 0 + num_chunk = (x_len + padding_len_x) / chunk_size num_chunk = int(num_chunk) - for i in range(0, num_chunk): - start = i * chunk_stride + start = i * chunk_size end = start + chunk_size x_chunk = padded_x[start:end] yield x_chunk async def run(self, wavfile_path: str): logging.info("send a message to the server") - # 读取音频 # self.read_wave() - # 发送 websocket 的 handshake 协议头 + # send websocket handshake protocal async with websockets.connect(self.url) as ws: - # server 端已经接收到 handshake 协议头 - # 发送开始指令 + # server has already received handshake protocal + # client start to send the command audio_info = json.dumps( { "name": "test.wav", @@ -77,6 +77,7 @@ class ASRAudioHandler: for chunk_data in self.read_wave(wavfile_path): await ws.send(chunk_data.tobytes()) msg = await ws.recv() + msg = json.loads(msg) logging.info("receive msg={}".format(msg)) # finished @@ -91,16 +92,37 @@ class ASRAudioHandler: separators=(',', ': ')) await ws.send(audio_info) msg = await ws.recv() - logging.info("receive msg={}".format(msg)) + + # decode the bytes to str + msg = json.loads(msg) + logging.info("final receive msg={}".format(msg)) + result = msg + return result def main(args): logging.basicConfig(level=logging.INFO) logging.info("asr websocket client start") - handler = ASRAudioHandler("127.0.0.1", 8091) + handler = ASRAudioHandler("127.0.0.1", 8090) loop = asyncio.get_event_loop() - loop.run_until_complete(handler.run(args.wavfile)) - logging.info("asr websocket client finished") + + # support to process single audio file + if args.wavfile and os.path.exists(args.wavfile): + logging.info(f"start to process the wavscp: {args.wavfile}") + result = loop.run_until_complete(handler.run(args.wavfile)) + result = result["asr_results"] + logging.info(f"asr websocket client finished : {result}") + + # support to process batch audios from wav.scp + if args.wavscp and os.path.exists(args.wavscp): + logging.info(f"start to process the wavscp: {args.wavscp}") + with codecs.open(args.wavscp, 'r', encoding='utf-8') as f,\ + codecs.open("result.txt", 'w', encoding='utf-8') as w: + for line in f: + utt_name, utt_path = line.strip().split() + result = loop.run_until_complete(handler.run(utt_path)) + result = result["asr_results"] + w.write(f"{utt_name} {result}\n") if __name__ == "__main__": @@ -110,6 +132,8 @@ if __name__ == "__main__": action="store", help="wav file path ", default="./16_audio.wav") + parser.add_argument( + "--wavscp", type=str, default=None, help="The batch audios dict text") args = parser.parse_args() main(args) diff --git a/paddlespeech/server/tests/tts/test_client.py b/paddlespeech/server/tests/tts/offline/http_client.py similarity index 90% rename from paddlespeech/server/tests/tts/test_client.py rename to paddlespeech/server/tests/tts/offline/http_client.py index e42c9bcfa..1bdee4c18 100644 --- a/paddlespeech/server/tests/tts/test_client.py +++ b/paddlespeech/server/tests/tts/offline/http_client.py @@ -33,7 +33,8 @@ def tts_client(args): text: A sentence to be synthesized outfile: Synthetic audio file """ - url = 'http://127.0.0.1:8090/paddlespeech/tts' + url = "http://" + str(args.server) + ":" + str( + args.port) + "/paddlespeech/tts" request = { "text": args.text, "spk_id": args.spk_id, @@ -72,7 +73,7 @@ if __name__ == "__main__": parser.add_argument( '--text', type=str, - default="你好，欢迎使用语音合成服务", + default="您好，欢迎使用语音合成服务。", help='A sentence to be synthesized') parser.add_argument('--spk_id', type=int, default=0, help='Speaker id') parser.add_argument('--speed', type=float, default=1.0, help='Audio speed') @@ -88,6 +89,9 @@ if __name__ == "__main__": type=str, default="./out.wav", help='Synthesized audio file') + parser.add_argument( + "--server", type=str, help="server ip", default="127.0.0.1") + parser.add_argument("--port", type=int, help="server port", default=8090) args = parser.parse_args() st = time.time() diff --git a/paddlespeech/server/tests/tts/online/http_client.py b/paddlespeech/server/tests/tts/online/http_client.py new file mode 100644 index 000000000..cbc1f5c02 --- /dev/null +++ b/paddlespeech/server/tests/tts/online/http_client.py @@ -0,0 +1,100 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import base64 +import json +import os +import time + +import requests + +from paddlespeech.server.utils.audio_process import pcm2wav + + +def save_audio(buffer, audio_path) -> bool: + if args.save_path.endswith("pcm"): + with open(args.save_path, "wb") as f: + f.write(buffer) + elif args.save_path.endswith("wav"): + with open("./tmp.pcm", "wb") as f: + f.write(buffer) + pcm2wav("./tmp.pcm", audio_path, channels=1, bits=16, sample_rate=24000) + os.system("rm ./tmp.pcm") + else: + print("Only supports saved audio format is pcm or wav") + return False + + return True + + +def test(args): + params = { + "text": args.text, + "spk_id": args.spk_id, + "speed": args.speed, + "volume": args.volume, + "sample_rate": args.sample_rate, + "save_path": '' + } + + buffer = b'' + flag = 1 + url = "http://" + str(args.server) + ":" + str( + args.port) + "/paddlespeech/streaming/tts" + st = time.time() + html = requests.post(url, json.dumps(params), stream=True) + for chunk in html.iter_content(chunk_size=1024): + chunk = base64.b64decode(chunk) # bytes + if flag: + first_response = time.time() - st + print(f"首包响应：{first_response} s") + flag = 0 + buffer += chunk + + final_response = time.time() - st + duration = len(buffer) / 2.0 / 24000 + + print(f"尾包响应：{final_response} s") + print(f"音频时长：{duration} s") + print(f"RTF: {final_response / duration}") + + if args.save_path is not None: + if save_audio(buffer, args.save_path): + print("音频保存至：", args.save_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + '--text', + type=str, + default="您好，欢迎使用语音合成服务。", + help='A sentence to be synthesized') + parser.add_argument('--spk_id', type=int, default=0, help='Speaker id') + parser.add_argument('--speed', type=float, default=1.0, help='Audio speed') + parser.add_argument( + '--volume', type=float, default=1.0, help='Audio volume') + parser.add_argument( + '--sample_rate', + type=int, + default=0, + help='Sampling rate, the default is the same as the model') + parser.add_argument( + "--server", type=str, help="server ip", default="127.0.0.1") + parser.add_argument("--port", type=int, help="server port", default=8092) + parser.add_argument( + "--save_path", type=str, help="save audio path", default=None) + + args = parser.parse_args() + test(args) diff --git a/paddlespeech/server/tests/tts/online/http_client_playaudio.py b/paddlespeech/server/tests/tts/online/http_client_playaudio.py new file mode 100644 index 000000000..1e7e8064e --- /dev/null +++ b/paddlespeech/server/tests/tts/online/http_client_playaudio.py @@ -0,0 +1,112 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import base64 +import json +import threading +import time + +import pyaudio +import requests + +mutex = threading.Lock() +buffer = b'' +p = pyaudio.PyAudio() +stream = p.open( + format=p.get_format_from_width(2), channels=1, rate=24000, output=True) +max_fail = 50 + + +def play_audio(): + global stream + global buffer + global max_fail + while True: + if not buffer: + max_fail -= 1 + time.sleep(0.05) + if max_fail < 0: + break + mutex.acquire() + stream.write(buffer) + buffer = b'' + mutex.release() + + +def test(args): + global mutex + global buffer + params = { + "text": args.text, + "spk_id": args.spk_id, + "speed": args.speed, + "volume": args.volume, + "sample_rate": args.sample_rate, + "save_path": '' + } + + all_bytes = 0.0 + t = threading.Thread(target=play_audio) + flag = 1 + url = "http://" + str(args.server) + ":" + str( + args.port) + "/paddlespeech/streaming/tts" + st = time.time() + html = requests.post(url, json.dumps(params), stream=True) + for chunk in html.iter_content(chunk_size=1024): + mutex.acquire() + chunk = base64.b64decode(chunk) # bytes + buffer += chunk + mutex.release() + if flag: + first_response = time.time() - st + print(f"首包响应：{first_response} s") + flag = 0 + t.start() + all_bytes += len(chunk) + + final_response = time.time() - st + duration = all_bytes / 2 / 24000 + + print(f"尾包响应：{final_response} s") + print(f"音频时长：{duration} s") + print(f"RTF: {final_response / duration}") + + t.join() + stream.stop_stream() + stream.close() + p.terminate() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + '--text', + type=str, + default="您好，欢迎使用语音合成服务。", + help='A sentence to be synthesized') + parser.add_argument('--spk_id', type=int, default=0, help='Speaker id') + parser.add_argument('--speed', type=float, default=1.0, help='Audio speed') + parser.add_argument( + '--volume', type=float, default=1.0, help='Audio volume') + parser.add_argument( + '--sample_rate', + type=int, + default=0, + help='Sampling rate, the default is the same as the model') + parser.add_argument( + "--server", type=str, help="server ip", default="127.0.0.1") + parser.add_argument("--port", type=int, help="server port", default=8092) + + args = parser.parse_args() + test(args) diff --git a/paddlespeech/server/tests/tts/online/ws_client.py b/paddlespeech/server/tests/tts/online/ws_client.py new file mode 100644 index 000000000..eef010cf2 --- /dev/null +++ b/paddlespeech/server/tests/tts/online/ws_client.py @@ -0,0 +1,126 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import _thread as thread +import argparse +import base64 +import json +import ssl +import time + +import websocket + +flag = 1 +st = 0.0 +all_bytes = b'' + + +class WsParam(object): + # 初始化 + def __init__(self, text, server="127.0.0.1", port=8090): + self.server = server + self.port = port + self.url = "ws://" + self.server + ":" + str(self.port) + "/ws/tts" + self.text = text + + # 生成url + def create_url(self): + return self.url + + +def on_message(ws, message): + global flag + global st + global all_bytes + + try: + message = json.loads(message) + audio = message["audio"] + audio = base64.b64decode(audio) # bytes + status = message["status"] + all_bytes += audio + + if status == 0: + print("create successfully.") + elif status == 1: + if flag: + print(f"首包响应：{time.time() - st} s") + flag = 0 + elif status == 2: + final_response = time.time() - st + duration = len(all_bytes) / 2.0 / 24000 + print(f"尾包响应：{final_response} s") + print(f"音频时长：{duration} s") + print(f"RTF: {final_response / duration}") + with open("./out.pcm", "wb") as f: + f.write(all_bytes) + print("ws is closed") + ws.close() + else: + print("infer error") + + except Exception as e: + print("receive msg,but parse exception:", e) + + +# 收到websocket错误的处理 +def on_error(ws, error): + print("### error:", error) + + +# 收到websocket关闭的处理 +def on_close(ws): + print("### closed ###") + + +# 收到websocket连接建立的处理 +def on_open(ws): + def run(*args): + global st + text_base64 = str( + base64.b64encode((wsParam.text).encode('utf-8')), "UTF8") + d = {"text": text_base64} + d = json.dumps(d) + print("Start sending text data") + st = time.time() + ws.send(d) + + thread.start_new_thread(run, ()) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--text", + type=str, + help="A sentence to be synthesized", + default="您好，欢迎使用语音合成服务。") + parser.add_argument( + "--server", type=str, help="server ip", default="127.0.0.1") + parser.add_argument("--port", type=int, help="server port", default=8092) + args = parser.parse_args() + + print("***************************************") + print("Server ip: ", args.server) + print("Server port: ", args.port) + print("Sentence to be synthesized: ", args.text) + print("***************************************") + + wsParam = WsParam(text=args.text, server=args.server, port=args.port) + + websocket.enableTrace(False) + wsUrl = wsParam.create_url() + ws = websocket.WebSocketApp( + wsUrl, on_message=on_message, on_error=on_error, on_close=on_close) + ws.on_open = on_open + ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE}) diff --git a/paddlespeech/server/tests/tts/online/ws_client_playaudio.py b/paddlespeech/server/tests/tts/online/ws_client_playaudio.py new file mode 100644 index 000000000..cdeb362df --- /dev/null +++ b/paddlespeech/server/tests/tts/online/ws_client_playaudio.py @@ -0,0 +1,160 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import _thread as thread +import argparse +import base64 +import json +import ssl +import threading +import time + +import pyaudio +import websocket + +mutex = threading.Lock() +buffer = b'' +p = pyaudio.PyAudio() +stream = p.open( + format=p.get_format_from_width(2), channels=1, rate=24000, output=True) +flag = 1 +st = 0.0 +all_bytes = 0.0 + + +class WsParam(object): + # 初始化 + def __init__(self, text, server="127.0.0.1", port=8090): + self.server = server + self.port = port + self.url = "ws://" + self.server + ":" + str(self.port) + "/ws/tts" + self.text = text + + # 生成url + def create_url(self): + return self.url + + +def play_audio(): + global stream + global buffer + while True: + time.sleep(0.05) + if not buffer: # buffer 为空 + break + mutex.acquire() + stream.write(buffer) + buffer = b'' + mutex.release() + + +t = threading.Thread(target=play_audio) + + +def on_message(ws, message): + global flag + global t + global buffer + global st + global all_bytes + + try: + message = json.loads(message) + audio = message["audio"] + audio = base64.b64decode(audio) # bytes + status = message["status"] + all_bytes += len(audio) + + if status == 0: + print("create successfully.") + elif status == 1: + mutex.acquire() + buffer += audio + mutex.release() + if flag: + print(f"首包响应：{time.time() - st} s") + flag = 0 + print("Start playing audio") + t.start() + elif status == 2: + final_response = time.time() - st + duration = all_bytes / 2 / 24000 + print(f"尾包响应：{final_response} s") + print(f"音频时长：{duration} s") + print(f"RTF: {final_response / duration}") + print("ws is closed") + ws.close() + else: + print("infer error") + + except Exception as e: + print("receive msg,but parse exception:", e) + + +# 收到websocket错误的处理 +def on_error(ws, error): + print("### error:", error) + + +# 收到websocket关闭的处理 +def on_close(ws): + print("### closed ###") + + +# 收到websocket连接建立的处理 +def on_open(ws): + def run(*args): + global st + text_base64 = str( + base64.b64encode((wsParam.text).encode('utf-8')), "UTF8") + d = {"text": text_base64} + d = json.dumps(d) + print("Start sending text data") + st = time.time() + ws.send(d) + + thread.start_new_thread(run, ()) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--text", + type=str, + help="A sentence to be synthesized", + default="您好，欢迎使用语音合成服务。") + parser.add_argument( + "--server", type=str, help="server ip", default="127.0.0.1") + parser.add_argument("--port", type=int, help="server port", default=8092) + args = parser.parse_args() + + print("***************************************") + print("Server ip: ", args.server) + print("Server port: ", args.port) + print("Sentence to be synthesized: ", args.text) + print("***************************************") + + wsParam = WsParam(text=args.text, server=args.server, port=args.port) + + websocket.enableTrace(False) + wsUrl = wsParam.create_url() + ws = websocket.WebSocketApp( + wsUrl, on_message=on_message, on_error=on_error, on_close=on_close) + ws.on_open = on_open + ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE}) + + t.join() + print("End of playing audio") + stream.stop_stream() + stream.close() + p.terminate() diff --git a/paddlespeech/server/utils/audio_process.py b/paddlespeech/server/utils/audio_process.py index 3cbb495a6..e85b9a27e 100644 --- a/paddlespeech/server/utils/audio_process.py +++ b/paddlespeech/server/utils/audio_process.py @@ -103,3 +103,40 @@ def change_speed(sample_raw, speed_rate, sample_rate): sample_rate_in=sample_rate).squeeze(-1).astype(np.float32).copy() return sample_speed + + +def float2pcm(sig, dtype='int16'): + """Convert floating point signal with a range from -1 to 1 to PCM. + + Args: + sig (array): Input array, must have floating point type. + dtype (str, optional): Desired (integer) data type. Defaults to 'int16'. + + Returns: + numpy.ndarray: Integer data, scaled and clipped to the range of the given + """ + sig = np.asarray(sig) + if sig.dtype.kind != 'f': + raise TypeError("'sig' must be a float array") + dtype = np.dtype(dtype) + if dtype.kind not in 'iu': + raise TypeError("'dtype' must be an integer type") + + i = np.iinfo(dtype) + abs_max = 2**(i.bits - 1) + offset = i.min + abs_max + return (sig * abs_max + offset).clip(i.min, i.max).astype(dtype) + + +def pcm2float(data): + """pcm int16 to float32 + Args: + audio(numpy.array): numpy.int16 + Returns: + audio(numpy.array): numpy.float32 + """ + if data.dtype == np.int16: + data = data.astype("float32") + bits = np.iinfo(np.int16).bits + data = data / (2**(bits - 1)) + return data diff --git a/paddlespeech/server/utils/buffer.py b/paddlespeech/server/utils/buffer.py index 682357b34..d4e6cd493 100644 --- a/paddlespeech/server/utils/buffer.py +++ b/paddlespeech/server/utils/buffer.py @@ -24,15 +24,38 @@ class Frame(object): class ChunkBuffer(object): def __init__(self, - frame_duration_ms=80, - shift_ms=40, + window_n=7, + shift_n=4, + window_ms=20, + shift_ms=10, sample_rate=16000, sample_width=2): - self.sample_rate = sample_rate - self.frame_duration_ms = frame_duration_ms + """audio sample data point buffer + + Args: + window_n (int, optional): decode window frame length. Defaults to 7 frame. + shift_n (int, optional): decode shift frame length. Defaults to 4 frame. + window_ms (int, optional): frame length, ms. Defaults to 20 ms. + shift_ms (int, optional): shift length, ms. Defaults to 10 ms. + sample_rate (int, optional): audio sample rate. Defaults to 16000. + sample_width (int, optional): sample point bytes. Defaults to 2 bytes. + """ + self.window_n = window_n + self.shift_n = shift_n + self.window_ms = window_ms self.shift_ms = shift_ms - self.remained_audio = b'' + self.sample_rate = sample_rate self.sample_width = sample_width # int16 = 2; float32 = 4 + self.remained_audio = b'' + + self.window_sec = float((self.window_n - 1) * self.shift_ms + + self.window_ms) / 1000.0 + self.shift_sec = float(self.shift_n * self.shift_ms / 1000.0) + + self.window_bytes = int(self.window_sec * self.sample_rate * + self.sample_width) + self.shift_bytes = int(self.shift_sec * self.sample_rate * + self.sample_width) def frame_generator(self, audio): """Generates audio frames from PCM audio data. @@ -40,20 +63,16 @@ class ChunkBuffer(object): the sample rate. Yields Frames of the requested duration. """ + audio = self.remained_audio + audio self.remained_audio = b'' - n = int(self.sample_rate * (self.frame_duration_ms / 1000.0) * - self.sample_width) - shift_n = int(self.sample_rate * (self.shift_ms / 1000.0) * - self.sample_width) offset = 0 timestamp = 0.0 - duration = (float(n) / self.sample_rate) / self.sample_width - shift_duration = (float(shift_n) / self.sample_rate) / self.sample_width - while offset + n <= len(audio): - yield Frame(audio[offset:offset + n], timestamp, duration) - timestamp += shift_duration - offset += shift_n + while offset + self.window_bytes <= len(audio): + yield Frame(audio[offset:offset + self.window_bytes], timestamp, + self.window_sec) + timestamp += self.shift_sec + offset += self.shift_bytes self.remained_audio += audio[offset:] diff --git a/paddlespeech/server/utils/util.py b/paddlespeech/server/utils/util.py index e9104fa2d..72ee0060e 100644 --- a/paddlespeech/server/utils/util.py +++ b/paddlespeech/server/utils/util.py @@ -11,6 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the import base64 +import math def wav2base64(wav_file: str): @@ -31,3 +32,46 @@ def self_check(): """ self check resource """ return True + + +def denorm(data, mean, std): + """stream am model need to denorm + """ + return data * std + mean + + +def get_chunks(data, block_size, pad_size, step): + """Divide data into multiple chunks + + Args: + data (tensor): data + block_size (int): [description] + pad_size (int): [description] + step (str): set "am" or "voc", generate chunk for step am or vocoder(voc) + + Returns: + list: chunks list + """ + + if block_size == -1: + return [data] + + if step == "am": + data_len = data.shape[1] + elif step == "voc": + data_len = data.shape[0] + else: + print("Please set correct type to get chunks, am or voc") + + chunks = [] + n = math.ceil(data_len / block_size) + for i in range(n): + start = max(0, i * block_size - pad_size) + end = min((i + 1) * block_size + pad_size, data_len) + if step == "am": + chunks.append(data[:, start:end, :]) + elif step == "voc": + chunks.append(data[start:end, :]) + else: + print("Please set correct type to get chunks, am or voc") + return chunks diff --git a/paddlespeech/server/ws/api.py b/paddlespeech/server/ws/api.py index 10664d114..313fd16f5 100644 --- a/paddlespeech/server/ws/api.py +++ b/paddlespeech/server/ws/api.py @@ -16,6 +16,7 @@ from typing import List from fastapi import APIRouter from paddlespeech.server.ws.asr_socket import router as asr_router +from paddlespeech.server.ws.tts_socket import router as tts_router _router = APIRouter() @@ -31,7 +32,7 @@ def setup_router(api_list: List): if api_name == 'asr': _router.include_router(asr_router) elif api_name == 'tts': - pass + _router.include_router(tts_router) else: pass diff --git a/paddlespeech/server/ws/asr_socket.py b/paddlespeech/server/ws/asr_socket.py index ea19816b6..a865703dd 100644 --- a/paddlespeech/server/ws/asr_socket.py +++ b/paddlespeech/server/ws/asr_socket.py @@ -13,12 +13,12 @@ # limitations under the License. import json -import numpy as np from fastapi import APIRouter from fastapi import WebSocket from fastapi import WebSocketDisconnect from starlette.websockets import WebSocketState as WebSocketState +from paddlespeech.server.engine.asr.online.asr_engine import PaddleASRConnectionHanddler from paddlespeech.server.engine.engine_pool import get_engine_pool from paddlespeech.server.utils.buffer import ChunkBuffer from paddlespeech.server.utils.vad import VADAudio @@ -28,22 +28,29 @@ router = APIRouter() @router.websocket('/ws/asr') async def websocket_endpoint(websocket: WebSocket): - await websocket.accept() engine_pool = get_engine_pool() asr_engine = engine_pool['asr'] + connection_handler = None # init buffer + # each websocekt connection has its own chunk buffer chunk_buffer_conf = asr_engine.config.chunk_buffer_conf chunk_buffer = ChunkBuffer( - sample_rate=chunk_buffer_conf['sample_rate'], - sample_width=chunk_buffer_conf['sample_width']) + window_n=chunk_buffer_conf.window_n, + shift_n=chunk_buffer_conf.shift_n, + window_ms=chunk_buffer_conf.window_ms, + shift_ms=chunk_buffer_conf.shift_ms, + sample_rate=chunk_buffer_conf.sample_rate, + sample_width=chunk_buffer_conf.sample_width) + # init vad - vad_conf = asr_engine.config.vad_conf - vad = VADAudio( - aggressiveness=vad_conf['aggressiveness'], - rate=vad_conf['sample_rate'], - frame_duration_ms=vad_conf['frame_duration_ms']) + vad_conf = asr_engine.config.get('vad_conf', None) + if vad_conf: + vad = VADAudio( + aggressiveness=vad_conf['aggressiveness'], + rate=vad_conf['sample_rate'], + frame_duration_ms=vad_conf['frame_duration_ms']) try: while True: @@ -60,13 +67,21 @@ async def websocket_endpoint(websocket: WebSocket): if message['signal'] == 'start': resp = {"status": "ok", "signal": "server_ready"} # do something at begining here + # create the instance to process the audio + connection_handler = PaddleASRConnectionHanddler(asr_engine) await websocket.send_json(resp) elif message['signal'] == 'end': - engine_pool = get_engine_pool() - asr_engine = engine_pool['asr'] # reset single engine for an new connection - asr_engine.reset() - resp = {"status": "ok", "signal": "finished"} + connection_handler.decode(is_finished=True) + connection_handler.rescoring() + asr_results = connection_handler.get_result() + connection_handler.reset() + + resp = { + "status": "ok", + "signal": "finished", + 'asr_results': asr_results + } await websocket.send_json(resp) break else: @@ -75,26 +90,11 @@ async def websocket_endpoint(websocket: WebSocket): elif "bytes" in message: message = message["bytes"] - # vad for input bytes audio - vad.add_audio(message) - message = b''.join(f for f in vad.vad_collector() - if f is not None) + connection_handler.extract_feat(message) + connection_handler.decode(is_finished=False) + asr_results = connection_handler.get_result() - engine_pool = get_engine_pool() - asr_engine = engine_pool['asr'] - asr_results = "" - frames = chunk_buffer.frame_generator(message) - for frame in frames: - samples = np.frombuffer(frame.bytes, dtype=np.int16) - sample_rate = asr_engine.config.sample_rate - x_chunk, x_chunk_lens = asr_engine.preprocess(samples, - sample_rate) - asr_engine.run(x_chunk, x_chunk_lens) - asr_results = asr_engine.postprocess() - - asr_results = asr_engine.postprocess() resp = {'asr_results': asr_results} - await websocket.send_json(resp) except WebSocketDisconnect: pass diff --git a/paddlespeech/server/ws/tts_socket.py b/paddlespeech/server/ws/tts_socket.py new file mode 100644 index 000000000..11458b3cf --- /dev/null +++ b/paddlespeech/server/ws/tts_socket.py @@ -0,0 +1,62 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import json + +from fastapi import APIRouter +from fastapi import WebSocket +from fastapi import WebSocketDisconnect +from starlette.websockets import WebSocketState as WebSocketState + +from paddlespeech.cli.log import logger +from paddlespeech.server.engine.engine_pool import get_engine_pool + +router = APIRouter() + + +@router.websocket('/ws/tts') +async def websocket_endpoint(websocket: WebSocket): + await websocket.accept() + + try: + # careful here, changed the source code from starlette.websockets + assert websocket.application_state == WebSocketState.CONNECTED + message = await websocket.receive() + websocket._raise_on_disconnect(message) + + # get engine + engine_pool = get_engine_pool() + tts_engine = engine_pool['tts'] + + # 获取 message 并转文本 + message = json.loads(message["text"]) + text_bese64 = message["text"] + sentence = tts_engine.preprocess(text_bese64=text_bese64) + + # run + wav_generator = tts_engine.run(sentence) + + while True: + try: + tts_results = next(wav_generator) + resp = {"status": 1, "audio": tts_results} + await websocket.send_json(resp) + logger.info("streaming audio...") + except StopIteration as e: + resp = {"status": 2, "audio": ''} + await websocket.send_json(resp) + logger.info("Complete the transmission of audio streams") + break + + except WebSocketDisconnect: + pass diff --git a/paddlespeech/t2s/exps/fastspeech2/preprocess.py b/paddlespeech/t2s/exps/fastspeech2/preprocess.py index 5bda75451..db1842b2e 100644 --- a/paddlespeech/t2s/exps/fastspeech2/preprocess.py +++ b/paddlespeech/t2s/exps/fastspeech2/preprocess.py @@ -86,6 +86,9 @@ def process_sentence(config: Dict[str, Any], logmel = mel_extractor.get_log_mel_fbank(wav) # change duration according to mel_length compare_duration_and_mel_length(sentences, utt_id, logmel) + # utt_id may be popped in compare_duration_and_mel_length + if utt_id not in sentences: + return None phones = sentences[utt_id][0] durations = sentences[utt_id][1] num_frames = logmel.shape[0] diff --git a/paddlespeech/t2s/exps/inference.py b/paddlespeech/t2s/exps/inference.py index 1188ddfb1..98e73e102 100644 --- a/paddlespeech/t2s/exps/inference.py +++ b/paddlespeech/t2s/exps/inference.py @@ -14,97 +14,21 @@ import argparse from pathlib import Path -import numpy +import paddle import soundfile as sf -from paddle import inference from timer import timer +from paddlespeech.t2s.exps.syn_utils import get_am_output from paddlespeech.t2s.exps.syn_utils import get_frontend +from paddlespeech.t2s.exps.syn_utils import get_predictor from paddlespeech.t2s.exps.syn_utils import get_sentences +from paddlespeech.t2s.exps.syn_utils import get_voc_output from paddlespeech.t2s.utils import str2bool -def get_predictor(args, filed='am'): - full_name = '' - if filed == 'am': - full_name = args.am - elif filed == 'voc': - full_name = args.voc - model_name = full_name[:full_name.rindex('_')] - config = inference.Config( - str(Path(args.inference_dir) / (full_name + ".pdmodel")), - str(Path(args.inference_dir) / (full_name + ".pdiparams"))) - if args.device == "gpu": - config.enable_use_gpu(100, 0) - elif args.device == "cpu": - config.disable_gpu() - # This line must be commented for fastspeech2, if not, it will OOM - if model_name != 'fastspeech2': - config.enable_memory_optim() - predictor = inference.create_predictor(config) - return predictor - - -def get_am_output(args, am_predictor, frontend, merge_sentences, input): - am_name = args.am[:args.am.rindex('_')] - am_dataset = args.am[args.am.rindex('_') + 1:] - am_input_names = am_predictor.get_input_names() - get_tone_ids = False - get_spk_id = False - if am_name == 'speedyspeech': - get_tone_ids = True - if am_dataset in {"aishell3", "vctk"} and args.speaker_dict: - get_spk_id = True - spk_id = numpy.array([args.spk_id]) - if args.lang == 'zh': - input_ids = frontend.get_input_ids( - input, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids) - phone_ids = input_ids["phone_ids"] - elif args.lang == 'en': - input_ids = frontend.get_input_ids( - input, merge_sentences=merge_sentences) - phone_ids = input_ids["phone_ids"] - else: - print("lang should in {'zh', 'en'}!") - - if get_tone_ids: - tone_ids = input_ids["tone_ids"] - tones = tone_ids[0].numpy() - tones_handle = am_predictor.get_input_handle(am_input_names[1]) - tones_handle.reshape(tones.shape) - tones_handle.copy_from_cpu(tones) - if get_spk_id: - spk_id_handle = am_predictor.get_input_handle(am_input_names[1]) - spk_id_handle.reshape(spk_id.shape) - spk_id_handle.copy_from_cpu(spk_id) - phones = phone_ids[0].numpy() - phones_handle = am_predictor.get_input_handle(am_input_names[0]) - phones_handle.reshape(phones.shape) - phones_handle.copy_from_cpu(phones) - - am_predictor.run() - am_output_names = am_predictor.get_output_names() - am_output_handle = am_predictor.get_output_handle(am_output_names[0]) - am_output_data = am_output_handle.copy_to_cpu() - return am_output_data - - -def get_voc_output(args, voc_predictor, input): - voc_input_names = voc_predictor.get_input_names() - mel_handle = voc_predictor.get_input_handle(voc_input_names[0]) - mel_handle.reshape(input.shape) - mel_handle.copy_from_cpu(input) - - voc_predictor.run() - voc_output_names = voc_predictor.get_output_names() - voc_output_handle = voc_predictor.get_output_handle(voc_output_names[0]) - wav = voc_output_handle.copy_to_cpu() - return wav - - def parse_args(): parser = argparse.ArgumentParser( - description="Paddle Infernce with speedyspeech & parallel wavegan.") + description="Paddle Infernce with acoustic model & vocoder.") # acoustic model parser.add_argument( '--am', @@ -178,21 +102,35 @@ def parse_args(): # only inference for models trained with csmsc now def main(): args = parse_args() + + paddle.set_device(args.device) + # frontend - frontend = get_frontend(args) + frontend = get_frontend( + lang=args.lang, + phones_dict=args.phones_dict, + tones_dict=args.tones_dict) # am_predictor - am_predictor = get_predictor(args, filed='am') + am_predictor = get_predictor( + model_dir=args.inference_dir, + model_file=args.am + ".pdmodel", + params_file=args.am + ".pdiparams", + device=args.device) # model: {model_name}_{dataset} am_dataset = args.am[args.am.rindex('_') + 1:] # voc_predictor - voc_predictor = get_predictor(args, filed='voc') + voc_predictor = get_predictor( + model_dir=args.inference_dir, + model_file=args.voc + ".pdmodel", + params_file=args.voc + ".pdiparams", + device=args.device) output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) - sentences = get_sentences(args) + sentences = get_sentences(text_file=args.text, lang=args.lang) merge_sentences = True fs = 24000 if am_dataset != 'ljspeech' else 22050 @@ -200,13 +138,15 @@ def main(): for utt_id, sentence in sentences[:3]: with timer() as t: am_output_data = get_am_output( - args, + input=sentence, am_predictor=am_predictor, + am=args.am, frontend=frontend, + lang=args.lang, merge_sentences=merge_sentences, - input=sentence) + speaker_dict=args.speaker_dict, ) wav = get_voc_output( - args, voc_predictor=voc_predictor, input=am_output_data) + voc_predictor=voc_predictor, input=am_output_data) speed = wav.size / t.elapse rtf = fs / speed print( @@ -220,13 +160,15 @@ def main(): for utt_id, sentence in sentences: with timer() as t: am_output_data = get_am_output( - args, + input=sentence, am_predictor=am_predictor, + am=args.am, frontend=frontend, + lang=args.lang, merge_sentences=merge_sentences, - input=sentence) + speaker_dict=args.speaker_dict, ) wav = get_voc_output( - args, voc_predictor=voc_predictor, input=am_output_data) + voc_predictor=voc_predictor, input=am_output_data) N += wav.size T += t.elapse diff --git a/paddlespeech/t2s/exps/inference_streaming.py b/paddlespeech/t2s/exps/inference_streaming.py new file mode 100644 index 000000000..b680f19a9 --- /dev/null +++ b/paddlespeech/t2s/exps/inference_streaming.py @@ -0,0 +1,248 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +from pathlib import Path + +import numpy as np +import paddle +import soundfile as sf +from timer import timer + +from paddlespeech.t2s.exps.syn_utils import denorm +from paddlespeech.t2s.exps.syn_utils import get_am_sublayer_output +from paddlespeech.t2s.exps.syn_utils import get_chunks +from paddlespeech.t2s.exps.syn_utils import get_frontend +from paddlespeech.t2s.exps.syn_utils import get_predictor +from paddlespeech.t2s.exps.syn_utils import get_sentences +from paddlespeech.t2s.exps.syn_utils import get_streaming_am_output +from paddlespeech.t2s.exps.syn_utils import get_voc_output +from paddlespeech.t2s.utils import str2bool + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Paddle Infernce with acoustic model & vocoder.") + # acoustic model + parser.add_argument( + '--am', + type=str, + default='fastspeech2_csmsc', + choices=['fastspeech2_csmsc'], + help='Choose acoustic model type of tts task.') + parser.add_argument( + "--am_stat", + type=str, + default=None, + help="mean and standard deviation used to normalize spectrogram when training acoustic model." + ) + parser.add_argument( + "--phones_dict", type=str, default=None, help="phone vocabulary file.") + parser.add_argument( + "--tones_dict", type=str, default=None, help="tone vocabulary file.") + parser.add_argument( + "--speaker_dict", type=str, default=None, help="speaker id map file.") + parser.add_argument( + '--spk_id', + type=int, + default=0, + help='spk id for multi speaker acoustic model') + # voc + parser.add_argument( + '--voc', + type=str, + default='pwgan_csmsc', + choices=['pwgan_csmsc', 'mb_melgan_csmsc', 'hifigan_csmsc'], + help='Choose vocoder type of tts task.') + # other + parser.add_argument( + '--lang', + type=str, + default='zh', + help='Choose model language. zh or en') + parser.add_argument( + "--text", + type=str, + help="text to synthesize, a 'utt_id sentence' pair per line") + parser.add_argument( + "--inference_dir", type=str, help="dir to save inference models") + parser.add_argument("--output_dir", type=str, help="output dir") + # inference + parser.add_argument( + "--device", + default="gpu", + choices=["gpu", "cpu"], + help="Device selected for inference.", ) + # streaming related + parser.add_argument( + "--am_streaming", + type=str2bool, + default=False, + help="whether use streaming acoustic model") + parser.add_argument( + "--chunk_size", type=int, default=42, help="chunk size of am streaming") + parser.add_argument( + "--pad_size", type=int, default=12, help="pad size of am streaming") + + args, _ = parser.parse_known_args() + return args + + +# only inference for models trained with csmsc now +def main(): + args = parse_args() + + paddle.set_device(args.device) + + # frontend + frontend = get_frontend( + lang=args.lang, + phones_dict=args.phones_dict, + tones_dict=args.tones_dict) + + # am_predictor + + am_encoder_infer_predictor = get_predictor( + model_dir=args.inference_dir, + model_file=args.am + "_am_encoder_infer" + ".pdmodel", + params_file=args.am + "_am_encoder_infer" + ".pdiparams", + device=args.device) + am_decoder_predictor = get_predictor( + model_dir=args.inference_dir, + model_file=args.am + "_am_decoder" + ".pdmodel", + params_file=args.am + "_am_decoder" + ".pdiparams", + device=args.device) + am_postnet_predictor = get_predictor( + model_dir=args.inference_dir, + model_file=args.am + "_am_postnet" + ".pdmodel", + params_file=args.am + "_am_postnet" + ".pdiparams", + device=args.device) + am_mu, am_std = np.load(args.am_stat) + # model: {model_name}_{dataset} + am_dataset = args.am[args.am.rindex('_') + 1:] + + # voc_predictor + voc_predictor = get_predictor( + model_dir=args.inference_dir, + model_file=args.voc + ".pdmodel", + params_file=args.voc + ".pdiparams", + device=args.device) + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + sentences = get_sentences(text_file=args.text, lang=args.lang) + + merge_sentences = True + + fs = 24000 if am_dataset != 'ljspeech' else 22050 + # warmup + for utt_id, sentence in sentences[:3]: + with timer() as t: + normalized_mel = get_streaming_am_output( + input=sentence, + am_encoder_infer_predictor=am_encoder_infer_predictor, + am_decoder_predictor=am_decoder_predictor, + am_postnet_predictor=am_postnet_predictor, + frontend=frontend, + lang=args.lang, + merge_sentences=merge_sentences, ) + mel = denorm(normalized_mel, am_mu, am_std) + wav = get_voc_output(voc_predictor=voc_predictor, input=mel) + speed = wav.size / t.elapse + rtf = fs / speed + print( + f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}." + ) + + print("warm up done!") + + N = 0 + T = 0 + chunk_size = args.chunk_size + pad_size = args.pad_size + get_tone_ids = False + for utt_id, sentence in sentences: + with timer() as t: + # frontend + if args.lang == 'zh': + input_ids = frontend.get_input_ids( + sentence, + merge_sentences=merge_sentences, + get_tone_ids=get_tone_ids) + phone_ids = input_ids["phone_ids"] + else: + print("lang should be 'zh' here!") + phones = phone_ids[0].numpy() + # acoustic model + orig_hs = get_am_sublayer_output( + am_encoder_infer_predictor, input=phones) + + if args.am_streaming: + hss = get_chunks(orig_hs, chunk_size, pad_size) + chunk_num = len(hss) + mel_list = [] + for i, hs in enumerate(hss): + am_decoder_output = get_am_sublayer_output( + am_decoder_predictor, input=hs) + am_postnet_output = get_am_sublayer_output( + am_postnet_predictor, + input=np.transpose(am_decoder_output, (0, 2, 1))) + am_output_data = am_decoder_output + np.transpose( + am_postnet_output, (0, 2, 1)) + normalized_mel = am_output_data[0] + + sub_mel = denorm(normalized_mel, am_mu, am_std) + # clip output part of pad + if i == 0: + sub_mel = sub_mel[:-pad_size] + elif i == chunk_num - 1: + # 最后一块的右侧一定没有 pad 够 + sub_mel = sub_mel[pad_size:] + else: + # 倒数几块的右侧也可能没有 pad 够 + sub_mel = sub_mel[pad_size:(chunk_size + pad_size) - + sub_mel.shape[0]] + mel_list.append(sub_mel) + mel = np.concatenate(mel_list, axis=0) + + else: + am_decoder_output = get_am_sublayer_output( + am_decoder_predictor, input=orig_hs) + + am_postnet_output = get_am_sublayer_output( + am_postnet_predictor, + input=np.transpose(am_decoder_output, (0, 2, 1))) + am_output_data = am_decoder_output + np.transpose( + am_postnet_output, (0, 2, 1)) + normalized_mel = am_output_data[0] + mel = denorm(normalized_mel, am_mu, am_std) + # vocoder + wav = get_voc_output(voc_predictor=voc_predictor, input=mel) + + N += wav.size + T += t.elapse + speed = wav.size / t.elapse + rtf = fs / speed + + sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000) + print( + f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}." + ) + + print(f"{utt_id} done!") + print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }") + + +if __name__ == "__main__": + main() diff --git a/paddlespeech/t2s/exps/ort_predict.py b/paddlespeech/t2s/exps/ort_predict.py new file mode 100644 index 000000000..2e8596ded --- /dev/null +++ b/paddlespeech/t2s/exps/ort_predict.py @@ -0,0 +1,154 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +from pathlib import Path + +import jsonlines +import numpy as np +import paddle +import soundfile as sf +from timer import timer + +from paddlespeech.t2s.exps.syn_utils import get_sess +from paddlespeech.t2s.exps.syn_utils import get_test_dataset +from paddlespeech.t2s.utils import str2bool + + +def ort_predict(args): + + # construct dataset for evaluation + with jsonlines.open(args.test_metadata, 'r') as reader: + test_metadata = list(reader) + am_name = args.am[:args.am.rindex('_')] + am_dataset = args.am[args.am.rindex('_') + 1:] + test_dataset = get_test_dataset(test_metadata=test_metadata, am=args.am) + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + fs = 24000 if am_dataset != 'ljspeech' else 22050 + + # am + am_sess = get_sess( + model_dir=args.inference_dir, + model_file=args.am + ".onnx", + device=args.device, + cpu_threads=args.cpu_threads) + + # vocoder + voc_sess = get_sess( + model_dir=args.inference_dir, + model_file=args.voc + ".onnx", + device=args.device, + cpu_threads=args.cpu_threads) + + # am warmup + for T in [27, 38, 54]: + am_input_feed = {} + if am_name == 'fastspeech2': + phone_ids = np.random.randint(1, 266, size=(T, )) + am_input_feed.update({'text': phone_ids}) + elif am_name == 'speedyspeech': + phone_ids = np.random.randint(1, 92, size=(T, )) + tone_ids = np.random.randint(1, 5, size=(T, )) + am_input_feed.update({'phones': phone_ids, 'tones': tone_ids}) + am_sess.run(None, input_feed=am_input_feed) + + # voc warmup + for T in [227, 308, 544]: + data = np.random.rand(T, 80).astype("float32") + voc_sess.run(None, {"logmel": data}) + print("warm up done!") + + N = 0 + T = 0 + am_input_feed = {} + for example in test_dataset: + utt_id = example['utt_id'] + if am_name == 'fastspeech2': + phone_ids = example["text"] + am_input_feed.update({'text': phone_ids}) + elif am_name == 'speedyspeech': + phone_ids = example["phones"] + tone_ids = example["tones"] + am_input_feed.update({'phones': phone_ids, 'tones': tone_ids}) + with timer() as t: + mel = am_sess.run(output_names=None, input_feed=am_input_feed) + mel = mel[0] + wav = voc_sess.run(output_names=None, input_feed={'logmel': mel}) + N += len(wav[0]) + T += t.elapse + speed = len(wav[0]) / t.elapse + rtf = fs / speed + sf.write( + str(output_dir / (utt_id + ".wav")), + np.array(wav)[0], + samplerate=fs) + print( + f"{utt_id}, mel: {mel.shape}, wave: {len(wav[0])}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}." + ) + print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }") + + +def parse_args(): + parser = argparse.ArgumentParser(description="Infernce with onnxruntime.") + # acoustic model + parser.add_argument( + '--am', + type=str, + default='fastspeech2_csmsc', + choices=['fastspeech2_csmsc', 'speedyspeech_csmsc'], + help='Choose acoustic model type of tts task.') + + # voc + parser.add_argument( + '--voc', + type=str, + default='hifigan_csmsc', + choices=['hifigan_csmsc', 'mb_melgan_csmsc', 'pwgan_csmsc'], + help='Choose vocoder type of tts task.') + # other + parser.add_argument( + "--inference_dir", type=str, help="dir to save inference models") + parser.add_argument("--test_metadata", type=str, help="test metadata.") + parser.add_argument("--output_dir", type=str, help="output dir") + + # inference + parser.add_argument( + "--use_trt", + type=str2bool, + default=False, + help="Whether to use inference engin TensorRT.", ) + + parser.add_argument( + "--device", + default="gpu", + choices=["gpu", "cpu"], + help="Device selected for inference.", ) + parser.add_argument('--cpu_threads', type=int, default=1) + + args, _ = parser.parse_known_args() + return args + + +def main(): + args = parse_args() + + paddle.set_device(args.device) + + ort_predict(args) + + +if __name__ == "__main__": + main() diff --git a/paddlespeech/t2s/exps/ort_predict_e2e.py b/paddlespeech/t2s/exps/ort_predict_e2e.py new file mode 100644 index 000000000..a2ef8e4c6 --- /dev/null +++ b/paddlespeech/t2s/exps/ort_predict_e2e.py @@ -0,0 +1,188 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +from pathlib import Path + +import numpy as np +import paddle +import soundfile as sf +from timer import timer + +from paddlespeech.t2s.exps.syn_utils import get_frontend +from paddlespeech.t2s.exps.syn_utils import get_sentences +from paddlespeech.t2s.exps.syn_utils import get_sess +from paddlespeech.t2s.utils import str2bool + + +def ort_predict(args): + + # frontend + frontend = get_frontend( + lang=args.lang, + phones_dict=args.phones_dict, + tones_dict=args.tones_dict) + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + sentences = get_sentences(text_file=args.text, lang=args.lang) + + am_name = args.am[:args.am.rindex('_')] + am_dataset = args.am[args.am.rindex('_') + 1:] + fs = 24000 if am_dataset != 'ljspeech' else 22050 + + am_sess = get_sess( + model_dir=args.inference_dir, + model_file=args.am + ".onnx", + device=args.device, + cpu_threads=args.cpu_threads) + + # vocoder + voc_sess = get_sess( + model_dir=args.inference_dir, + model_file=args.voc + ".onnx", + device=args.device, + cpu_threads=args.cpu_threads) + + # frontend warmup + # Loading model cost 0.5+ seconds + if args.lang == 'zh': + frontend.get_input_ids("你好，欢迎使用飞桨框架进行深度学习研究！", merge_sentences=True) + else: + print("lang should in be 'zh' here!") + + # am warmup + for T in [27, 38, 54]: + am_input_feed = {} + if am_name == 'fastspeech2': + phone_ids = np.random.randint(1, 266, size=(T, )) + am_input_feed.update({'text': phone_ids}) + elif am_name == 'speedyspeech': + phone_ids = np.random.randint(1, 92, size=(T, )) + tone_ids = np.random.randint(1, 5, size=(T, )) + am_input_feed.update({'phones': phone_ids, 'tones': tone_ids}) + am_sess.run(None, input_feed=am_input_feed) + + # voc warmup + for T in [227, 308, 544]: + data = np.random.rand(T, 80).astype("float32") + voc_sess.run(None, input_feed={"logmel": data}) + print("warm up done!") + + N = 0 + T = 0 + merge_sentences = True + get_tone_ids = False + am_input_feed = {} + if am_name == 'speedyspeech': + get_tone_ids = True + for utt_id, sentence in sentences: + with timer() as t: + if args.lang == 'zh': + input_ids = frontend.get_input_ids( + sentence, + merge_sentences=merge_sentences, + get_tone_ids=get_tone_ids) + phone_ids = input_ids["phone_ids"] + if get_tone_ids: + tone_ids = input_ids["tone_ids"] + else: + print("lang should in be 'zh' here!") + # merge_sentences=True here, so we only use the first item of phone_ids + phone_ids = phone_ids[0].numpy() + if am_name == 'fastspeech2': + am_input_feed.update({'text': phone_ids}) + elif am_name == 'speedyspeech': + tone_ids = tone_ids[0].numpy() + am_input_feed.update({'phones': phone_ids, 'tones': tone_ids}) + mel = am_sess.run(output_names=None, input_feed=am_input_feed) + mel = mel[0] + wav = voc_sess.run(output_names=None, input_feed={'logmel': mel}) + + N += len(wav[0]) + T += t.elapse + speed = len(wav[0]) / t.elapse + rtf = fs / speed + sf.write( + str(output_dir / (utt_id + ".wav")), + np.array(wav)[0], + samplerate=fs) + print( + f"{utt_id}, mel: {mel.shape}, wave: {len(wav[0])}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}." + ) + print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }") + + +def parse_args(): + parser = argparse.ArgumentParser(description="Infernce with onnxruntime.") + # acoustic model + parser.add_argument( + '--am', + type=str, + default='fastspeech2_csmsc', + choices=['fastspeech2_csmsc', 'speedyspeech_csmsc'], + help='Choose acoustic model type of tts task.') + parser.add_argument( + "--phones_dict", type=str, default=None, help="phone vocabulary file.") + parser.add_argument( + "--tones_dict", type=str, default=None, help="tone vocabulary file.") + + # voc + parser.add_argument( + '--voc', + type=str, + default='hifigan_csmsc', + choices=['hifigan_csmsc', 'mb_melgan_csmsc', 'pwgan_csmsc'], + help='Choose vocoder type of tts task.') + # other + parser.add_argument( + "--inference_dir", type=str, help="dir to save inference models") + parser.add_argument( + "--text", + type=str, + help="text to synthesize, a 'utt_id sentence' pair per line") + parser.add_argument("--output_dir", type=str, help="output dir") + parser.add_argument( + '--lang', + type=str, + default='zh', + help='Choose model language. zh or en') + + # inference + parser.add_argument( + "--use_trt", + type=str2bool, + default=False, + help="Whether to use inference engin TensorRT.", ) + + parser.add_argument( + "--device", + default="gpu", + choices=["gpu", "cpu"], + help="Device selected for inference.", ) + parser.add_argument('--cpu_threads', type=int, default=1) + + args, _ = parser.parse_known_args() + return args + + +def main(): + args = parse_args() + + paddle.set_device(args.device) + + ort_predict(args) + + +if __name__ == "__main__": + main() diff --git a/paddlespeech/t2s/exps/ort_predict_streaming.py b/paddlespeech/t2s/exps/ort_predict_streaming.py new file mode 100644 index 000000000..5d2c66bc9 --- /dev/null +++ b/paddlespeech/t2s/exps/ort_predict_streaming.py @@ -0,0 +1,256 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +from pathlib import Path + +import numpy as np +import paddle +import soundfile as sf +from timer import timer + +from paddlespeech.t2s.exps.syn_utils import denorm +from paddlespeech.t2s.exps.syn_utils import get_chunks +from paddlespeech.t2s.exps.syn_utils import get_frontend +from paddlespeech.t2s.exps.syn_utils import get_sentences +from paddlespeech.t2s.exps.syn_utils import get_sess +from paddlespeech.t2s.utils import str2bool + + +def ort_predict(args): + + # frontend + frontend = get_frontend( + lang=args.lang, + phones_dict=args.phones_dict, + tones_dict=args.tones_dict) + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + sentences = get_sentences(text_file=args.text, lang=args.lang) + + am_name = args.am[:args.am.rindex('_')] + am_dataset = args.am[args.am.rindex('_') + 1:] + fs = 24000 if am_dataset != 'ljspeech' else 22050 + + # streaming acoustic model + am_encoder_infer_sess = get_sess( + model_dir=args.inference_dir, + model_file=args.am + "_am_encoder_infer" + ".onnx", + device=args.device, + cpu_threads=args.cpu_threads) + am_decoder_sess = get_sess( + model_dir=args.inference_dir, + model_file=args.am + "_am_decoder" + ".onnx", + device=args.device, + cpu_threads=args.cpu_threads) + + am_postnet_sess = get_sess( + model_dir=args.inference_dir, + model_file=args.am + "_am_postnet" + ".onnx", + device=args.device, + cpu_threads=args.cpu_threads) + am_mu, am_std = np.load(args.am_stat) + + # vocoder + voc_sess = get_sess( + model_dir=args.inference_dir, + model_file=args.voc + ".onnx", + device=args.device, + cpu_threads=args.cpu_threads) + + # frontend warmup + # Loading model cost 0.5+ seconds + if args.lang == 'zh': + frontend.get_input_ids("你好，欢迎使用飞桨框架进行深度学习研究！", merge_sentences=True) + else: + print("lang should in be 'zh' here!") + + # am warmup + for T in [27, 38, 54]: + phone_ids = np.random.randint(1, 266, size=(T, )) + am_encoder_infer_sess.run(None, input_feed={'text': phone_ids}) + + am_decoder_input = np.random.rand(1, T * 15, 384).astype('float32') + am_decoder_sess.run(None, input_feed={'xs': am_decoder_input}) + + am_postnet_input = np.random.rand(1, 80, T * 15).astype('float32') + am_postnet_sess.run(None, input_feed={'xs': am_postnet_input}) + + # voc warmup + for T in [227, 308, 544]: + data = np.random.rand(T, 80).astype("float32") + voc_sess.run(None, input_feed={"logmel": data}) + print("warm up done!") + + N = 0 + T = 0 + merge_sentences = True + get_tone_ids = False + chunk_size = args.chunk_size + pad_size = args.pad_size + + for utt_id, sentence in sentences: + with timer() as t: + if args.lang == 'zh': + input_ids = frontend.get_input_ids( + sentence, + merge_sentences=merge_sentences, + get_tone_ids=get_tone_ids) + phone_ids = input_ids["phone_ids"] + else: + print("lang should in be 'zh' here!") + # merge_sentences=True here, so we only use the first item of phone_ids + phone_ids = phone_ids[0].numpy() + orig_hs = am_encoder_infer_sess.run( + None, input_feed={'text': phone_ids}) + if args.am_streaming: + hss = get_chunks(orig_hs[0], chunk_size, pad_size) + chunk_num = len(hss) + mel_list = [] + for i, hs in enumerate(hss): + am_decoder_output = am_decoder_sess.run( + None, input_feed={'xs': hs}) + am_postnet_output = am_postnet_sess.run( + None, + input_feed={ + 'xs': np.transpose(am_decoder_output[0], (0, 2, 1)) + }) + am_output_data = am_decoder_output + np.transpose( + am_postnet_output[0], (0, 2, 1)) + normalized_mel = am_output_data[0][0] + + sub_mel = denorm(normalized_mel, am_mu, am_std) + # clip output part of pad + if i == 0: + sub_mel = sub_mel[:-pad_size] + elif i == chunk_num - 1: + # 最后一块的右侧一定没有 pad 够 + sub_mel = sub_mel[pad_size:] + else: + # 倒数几块的右侧也可能没有 pad 够 + sub_mel = sub_mel[pad_size:(chunk_size + pad_size) - + sub_mel.shape[0]] + mel_list.append(sub_mel) + mel = np.concatenate(mel_list, axis=0) + else: + am_decoder_output = am_decoder_sess.run( + None, input_feed={'xs': orig_hs[0]}) + am_postnet_output = am_postnet_sess.run( + None, + input_feed={ + 'xs': np.transpose(am_decoder_output[0], (0, 2, 1)) + }) + am_output_data = am_decoder_output + np.transpose( + am_postnet_output[0], (0, 2, 1)) + normalized_mel = am_output_data[0] + mel = denorm(normalized_mel, am_mu, am_std) + mel = mel[0] + # vocoder + + wav = voc_sess.run(output_names=None, input_feed={'logmel': mel}) + + N += len(wav[0]) + T += t.elapse + speed = len(wav[0]) / t.elapse + rtf = fs / speed + sf.write( + str(output_dir / (utt_id + ".wav")), + np.array(wav)[0], + samplerate=fs) + print( + f"{utt_id}, mel: {mel.shape}, wave: {len(wav[0])}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}." + ) + print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }") + + +def parse_args(): + parser = argparse.ArgumentParser(description="Infernce with onnxruntime.") + # acoustic model + parser.add_argument( + '--am', + type=str, + default='fastspeech2_csmsc', + choices=['fastspeech2_csmsc'], + help='Choose acoustic model type of tts task.') + parser.add_argument( + "--am_stat", + type=str, + default=None, + help="mean and standard deviation used to normalize spectrogram when training acoustic model." + ) + parser.add_argument( + "--phones_dict", type=str, default=None, help="phone vocabulary file.") + parser.add_argument( + "--tones_dict", type=str, default=None, help="tone vocabulary file.") + + # voc + parser.add_argument( + '--voc', + type=str, + default='hifigan_csmsc', + choices=['hifigan_csmsc', 'mb_melgan_csmsc', 'pwgan_csmsc'], + help='Choose vocoder type of tts task.') + # other + parser.add_argument( + "--inference_dir", type=str, help="dir to save inference models") + parser.add_argument( + "--text", + type=str, + help="text to synthesize, a 'utt_id sentence' pair per line") + parser.add_argument("--output_dir", type=str, help="output dir") + parser.add_argument( + '--lang', + type=str, + default='zh', + help='Choose model language. zh or en') + + # inference + parser.add_argument( + "--use_trt", + type=str2bool, + default=False, + help="Whether to use inference engin TensorRT.", ) + + parser.add_argument( + "--device", + default="gpu", + choices=["gpu", "cpu"], + help="Device selected for inference.", ) + parser.add_argument('--cpu_threads', type=int, default=1) + + # streaming related + parser.add_argument( + "--am_streaming", + type=str2bool, + default=False, + help="whether use streaming acoustic model") + parser.add_argument( + "--chunk_size", type=int, default=42, help="chunk size of am streaming") + parser.add_argument( + "--pad_size", type=int, default=12, help="pad size of am streaming") + + args, _ = parser.parse_known_args() + return args + + +def main(): + args = parse_args() + + paddle.set_device(args.device) + + ort_predict(args) + + +if __name__ == "__main__": + main() diff --git a/paddlespeech/t2s/exps/speedyspeech/preprocess.py b/paddlespeech/t2s/exps/speedyspeech/preprocess.py index 3f81c4e14..e833d1394 100644 --- a/paddlespeech/t2s/exps/speedyspeech/preprocess.py +++ b/paddlespeech/t2s/exps/speedyspeech/preprocess.py @@ -79,6 +79,9 @@ def process_sentence(config: Dict[str, Any], logmel = mel_extractor.get_log_mel_fbank(wav) # change duration according to mel_length compare_duration_and_mel_length(sentences, utt_id, logmel) + # utt_id may be popped in compare_duration_and_mel_length + if utt_id not in sentences: + return None labels = sentences[utt_id][0] # extract phone and duration phones = [] diff --git a/paddlespeech/t2s/exps/syn_utils.py b/paddlespeech/t2s/exps/syn_utils.py index c52cb3727..ce0aee05e 100644 --- a/paddlespeech/t2s/exps/syn_utils.py +++ b/paddlespeech/t2s/exps/syn_utils.py @@ -11,12 +11,21 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import math import os +from pathlib import Path +from typing import Any +from typing import Dict +from typing import List +from typing import Optional import numpy as np +import onnxruntime as ort import paddle +from paddle import inference from paddle import jit from paddle.static import InputSpec +from yacs.config import CfgNode from paddlespeech.s2t.utils.dynamic_import import dynamic_import from paddlespeech.t2s.datasets.data_table import DataTable @@ -62,29 +71,50 @@ model_alias = { } +def denorm(data, mean, std): + return data * std + mean + + +def get_chunks(data, chunk_size: int, pad_size: int): + data_len = data.shape[1] + chunks = [] + n = math.ceil(data_len / chunk_size) + for i in range(n): + start = max(0, i * chunk_size - pad_size) + end = min((i + 1) * chunk_size + pad_size, data_len) + chunks.append(data[:, start:end, :]) + return chunks + + # input -def get_sentences(args): +def get_sentences(text_file: Optional[os.PathLike], lang: str='zh'): # construct dataset for evaluation sentences = [] - with open(args.text, 'rt') as f: + with open(text_file, 'rt') as f: for line in f: items = line.strip().split() utt_id = items[0] - if 'lang' in args and args.lang == 'zh': + if lang == 'zh': sentence = "".join(items[1:]) - elif 'lang' in args and args.lang == 'en': + elif lang == 'en': sentence = " ".join(items[1:]) sentences.append((utt_id, sentence)) return sentences -def get_test_dataset(args, test_metadata, am_name, am_dataset): +def get_test_dataset(test_metadata: List[Dict[str, Any]], + am: str, + speaker_dict: Optional[os.PathLike]=None, + voice_cloning: bool=False): + # model: {model_name}_{dataset} + am_name = am[:am.rindex('_')] + am_dataset = am[am.rindex('_') + 1:] if am_name == 'fastspeech2': fields = ["utt_id", "text"] - if am_dataset in {"aishell3", "vctk"} and args.speaker_dict: + if am_dataset in {"aishell3", "vctk"} and speaker_dict is not None: print("multiple speaker fastspeech2!") fields += ["spk_id"] - elif 'voice_cloning' in args and args.voice_cloning: + elif voice_cloning: print("voice cloning!") fields += ["spk_emb"] else: @@ -93,7 +123,7 @@ def get_test_dataset(args, test_metadata, am_name, am_dataset): fields = ["utt_id", "phones", "tones"] elif am_name == 'tacotron2': fields = ["utt_id", "text"] - if 'voice_cloning' in args and args.voice_cloning: + if voice_cloning: print("voice cloning!") fields += ["spk_emb"] @@ -102,12 +132,14 @@ def get_test_dataset(args, test_metadata, am_name, am_dataset): # frontend -def get_frontend(args): - if 'lang' in args and args.lang == 'zh': +def get_frontend(lang: str='zh', + phones_dict: Optional[os.PathLike]=None, + tones_dict: Optional[os.PathLike]=None): + if lang == 'zh': frontend = Frontend( - phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict) - elif 'lang' in args and args.lang == 'en': - frontend = English(phone_vocab_path=args.phones_dict) + phone_vocab_path=phones_dict, tone_vocab_path=tones_dict) + elif lang == 'en': + frontend = English(phone_vocab_path=phones_dict) else: print("wrong lang!") print("frontend done!") @@ -115,30 +147,37 @@ def get_frontend(args): # dygraph -def get_am_inference(args, am_config): - with open(args.phones_dict, "r") as f: +def get_am_inference( + am: str='fastspeech2_csmsc', + am_config: CfgNode=None, + am_ckpt: Optional[os.PathLike]=None, + am_stat: Optional[os.PathLike]=None, + phones_dict: Optional[os.PathLike]=None, + tones_dict: Optional[os.PathLike]=None, + speaker_dict: Optional[os.PathLike]=None, ): + with open(phones_dict, "r") as f: phn_id = [line.strip().split() for line in f.readlines()] vocab_size = len(phn_id) print("vocab_size:", vocab_size) tone_size = None - if 'tones_dict' in args and args.tones_dict: - with open(args.tones_dict, "r") as f: + if tones_dict is not None: + with open(tones_dict, "r") as f: tone_id = [line.strip().split() for line in f.readlines()] tone_size = len(tone_id) print("tone_size:", tone_size) spk_num = None - if 'speaker_dict' in args and args.speaker_dict: - with open(args.speaker_dict, 'rt') as f: + if speaker_dict is not None: + with open(speaker_dict, 'rt') as f: spk_id = [line.strip().split() for line in f.readlines()] spk_num = len(spk_id) print("spk_num:", spk_num) odim = am_config.n_mels # model: {model_name}_{dataset} - am_name = args.am[:args.am.rindex('_')] - am_dataset = args.am[args.am.rindex('_') + 1:] + am_name = am[:am.rindex('_')] + am_dataset = am[am.rindex('_') + 1:] am_class = dynamic_import(am_name, model_alias) am_inference_class = dynamic_import(am_name + '_inference', model_alias) @@ -155,34 +194,38 @@ def get_am_inference(args, am_config): elif am_name == 'tacotron2': am = am_class(idim=vocab_size, odim=odim, **am_config["model"]) - am.set_state_dict(paddle.load(args.am_ckpt)["main_params"]) + am.set_state_dict(paddle.load(am_ckpt)["main_params"]) am.eval() - am_mu, am_std = np.load(args.am_stat) + am_mu, am_std = np.load(am_stat) am_mu = paddle.to_tensor(am_mu) am_std = paddle.to_tensor(am_std) am_normalizer = ZScore(am_mu, am_std) am_inference = am_inference_class(am_normalizer, am) am_inference.eval() print("acoustic model done!") - return am_inference, am_name, am_dataset + return am_inference -def get_voc_inference(args, voc_config): +def get_voc_inference( + voc: str='pwgan_csmsc', + voc_config: Optional[os.PathLike]=None, + voc_ckpt: Optional[os.PathLike]=None, + voc_stat: Optional[os.PathLike]=None, ): # model: {model_name}_{dataset} - voc_name = args.voc[:args.voc.rindex('_')] + voc_name = voc[:voc.rindex('_')] voc_class = dynamic_import(voc_name, model_alias) voc_inference_class = dynamic_import(voc_name + '_inference', model_alias) if voc_name != 'wavernn': voc = voc_class(**voc_config["generator_params"]) - voc.set_state_dict(paddle.load(args.voc_ckpt)["generator_params"]) + voc.set_state_dict(paddle.load(voc_ckpt)["generator_params"]) voc.remove_weight_norm() voc.eval() else: voc = voc_class(**voc_config["model"]) - voc.set_state_dict(paddle.load(args.voc_ckpt)["main_params"]) + voc.set_state_dict(paddle.load(voc_ckpt)["main_params"]) voc.eval() - voc_mu, voc_std = np.load(args.voc_stat) + voc_mu, voc_std = np.load(voc_stat) voc_mu = paddle.to_tensor(voc_mu) voc_std = paddle.to_tensor(voc_std) voc_normalizer = ZScore(voc_mu, voc_std) @@ -192,10 +235,16 @@ def get_voc_inference(args, voc_config): return voc_inference -# to static -def am_to_static(args, am_inference, am_name, am_dataset): +# dygraph to static graph +def am_to_static(am_inference, + am: str='fastspeech2_csmsc', + inference_dir=Optional[os.PathLike], + speaker_dict: Optional[os.PathLike]=None): + # model: {model_name}_{dataset} + am_name = am[:am.rindex('_')] + am_dataset = am[am.rindex('_') + 1:] if am_name == 'fastspeech2': - if am_dataset in {"aishell3", "vctk"} and args.speaker_dict: + if am_dataset in {"aishell3", "vctk"} and speaker_dict is not None: am_inference = jit.to_static( am_inference, input_spec=[ @@ -207,7 +256,7 @@ def am_to_static(args, am_inference, am_name, am_dataset): am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)]) elif am_name == 'speedyspeech': - if am_dataset in {"aishell3", "vctk"} and args.speaker_dict: + if am_dataset in {"aishell3", "vctk"} and speaker_dict is not None: am_inference = jit.to_static( am_inference, input_spec=[ @@ -228,16 +277,171 @@ def am_to_static(args, am_inference, am_name, am_dataset): am_inference = jit.to_static( am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)]) - paddle.jit.save(am_inference, os.path.join(args.inference_dir, args.am)) - am_inference = paddle.jit.load(os.path.join(args.inference_dir, args.am)) + paddle.jit.save(am_inference, os.path.join(inference_dir, am)) + am_inference = paddle.jit.load(os.path.join(inference_dir, am)) return am_inference -def voc_to_static(args, voc_inference): +def voc_to_static(voc_inference, + voc: str='pwgan_csmsc', + inference_dir=Optional[os.PathLike]): voc_inference = jit.to_static( voc_inference, input_spec=[ InputSpec([-1, 80], dtype=paddle.float32), ]) - paddle.jit.save(voc_inference, os.path.join(args.inference_dir, args.voc)) - voc_inference = paddle.jit.load(os.path.join(args.inference_dir, args.voc)) + paddle.jit.save(voc_inference, os.path.join(inference_dir, voc)) + voc_inference = paddle.jit.load(os.path.join(inference_dir, voc)) return voc_inference + + +# inference +def get_predictor(model_dir: Optional[os.PathLike]=None, + model_file: Optional[os.PathLike]=None, + params_file: Optional[os.PathLike]=None, + device: str='cpu'): + + config = inference.Config( + str(Path(model_dir) / model_file), str(Path(model_dir) / params_file)) + if device == "gpu": + config.enable_use_gpu(100, 0) + elif device == "cpu": + config.disable_gpu() + config.enable_memory_optim() + predictor = inference.create_predictor(config) + return predictor + + +def get_am_output( + input: str, + am_predictor, + am, + frontend, + lang: str='zh', + merge_sentences: bool=True, + speaker_dict: Optional[os.PathLike]=None, + spk_id: int=0, ): + am_name = am[:am.rindex('_')] + am_dataset = am[am.rindex('_') + 1:] + am_input_names = am_predictor.get_input_names() + get_tone_ids = False + get_spk_id = False + if am_name == 'speedyspeech': + get_tone_ids = True + if am_dataset in {"aishell3", "vctk"} and speaker_dict: + get_spk_id = True + spk_id = np.array([spk_id]) + if lang == 'zh': + input_ids = frontend.get_input_ids( + input, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids) + phone_ids = input_ids["phone_ids"] + elif lang == 'en': + input_ids = frontend.get_input_ids( + input, merge_sentences=merge_sentences) + phone_ids = input_ids["phone_ids"] + else: + print("lang should in {'zh', 'en'}!") + + if get_tone_ids: + tone_ids = input_ids["tone_ids"] + tones = tone_ids[0].numpy() + tones_handle = am_predictor.get_input_handle(am_input_names[1]) + tones_handle.reshape(tones.shape) + tones_handle.copy_from_cpu(tones) + if get_spk_id: + spk_id_handle = am_predictor.get_input_handle(am_input_names[1]) + spk_id_handle.reshape(spk_id.shape) + spk_id_handle.copy_from_cpu(spk_id) + phones = phone_ids[0].numpy() + phones_handle = am_predictor.get_input_handle(am_input_names[0]) + phones_handle.reshape(phones.shape) + phones_handle.copy_from_cpu(phones) + + am_predictor.run() + am_output_names = am_predictor.get_output_names() + am_output_handle = am_predictor.get_output_handle(am_output_names[0]) + am_output_data = am_output_handle.copy_to_cpu() + return am_output_data + + +def get_voc_output(voc_predictor, input): + voc_input_names = voc_predictor.get_input_names() + mel_handle = voc_predictor.get_input_handle(voc_input_names[0]) + mel_handle.reshape(input.shape) + mel_handle.copy_from_cpu(input) + + voc_predictor.run() + voc_output_names = voc_predictor.get_output_names() + voc_output_handle = voc_predictor.get_output_handle(voc_output_names[0]) + wav = voc_output_handle.copy_to_cpu() + return wav + + +def get_am_sublayer_output(am_sublayer_predictor, input): + am_sublayer_input_names = am_sublayer_predictor.get_input_names() + input_handle = am_sublayer_predictor.get_input_handle( + am_sublayer_input_names[0]) + input_handle.reshape(input.shape) + input_handle.copy_from_cpu(input) + + am_sublayer_predictor.run() + am_sublayer_names = am_sublayer_predictor.get_output_names() + am_sublayer_handle = am_sublayer_predictor.get_output_handle( + am_sublayer_names[0]) + am_sublayer_output = am_sublayer_handle.copy_to_cpu() + return am_sublayer_output + + +def get_streaming_am_output(input: str, + am_encoder_infer_predictor, + am_decoder_predictor, + am_postnet_predictor, + frontend, + lang: str='zh', + merge_sentences: bool=True): + get_tone_ids = False + if lang == 'zh': + input_ids = frontend.get_input_ids( + input, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids) + phone_ids = input_ids["phone_ids"] + else: + print("lang should be 'zh' here!") + + phones = phone_ids[0].numpy() + am_encoder_infer_output = get_am_sublayer_output( + am_encoder_infer_predictor, input=phones) + + am_decoder_output = get_am_sublayer_output( + am_decoder_predictor, input=am_encoder_infer_output) + + am_postnet_output = get_am_sublayer_output( + am_postnet_predictor, input=np.transpose(am_decoder_output, (0, 2, 1))) + am_output_data = am_decoder_output + np.transpose(am_postnet_output, + (0, 2, 1)) + normalized_mel = am_output_data[0] + return normalized_mel + + +# onnx +def get_sess(model_dir: Optional[os.PathLike]=None, + model_file: Optional[os.PathLike]=None, + device: str='cpu', + cpu_threads: int=1, + use_trt: bool=False): + + model_dir = str(Path(model_dir) / model_file) + sess_options = ort.SessionOptions() + sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL + sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL + + if device == "gpu": + # fastspeech2/mb_melgan can't use trt now! + if use_trt: + providers = ['TensorrtExecutionProvider'] + else: + providers = ['CUDAExecutionProvider'] + elif device == "cpu": + providers = ['CPUExecutionProvider'] + sess_options.intra_op_num_threads = cpu_threads + sess = ort.InferenceSession( + model_dir, providers=providers, sess_options=sess_options) + return sess diff --git a/paddlespeech/t2s/exps/synthesize.py b/paddlespeech/t2s/exps/synthesize.py index abb1eb4eb..dd66e54e9 100644 --- a/paddlespeech/t2s/exps/synthesize.py +++ b/paddlespeech/t2s/exps/synthesize.py @@ -50,11 +50,29 @@ def evaluate(args): print(voc_config) # acoustic model - am_inference, am_name, am_dataset = get_am_inference(args, am_config) - test_dataset = get_test_dataset(args, test_metadata, am_name, am_dataset) + am_name = args.am[:args.am.rindex('_')] + am_dataset = args.am[args.am.rindex('_') + 1:] + + am_inference = get_am_inference( + am=args.am, + am_config=am_config, + am_ckpt=args.am_ckpt, + am_stat=args.am_stat, + phones_dict=args.phones_dict, + tones_dict=args.tones_dict, + speaker_dict=args.speaker_dict) + test_dataset = get_test_dataset( + test_metadata=test_metadata, + am=args.am, + speaker_dict=args.speaker_dict, + voice_cloning=args.voice_cloning) # vocoder - voc_inference = get_voc_inference(args, voc_config) + voc_inference = get_voc_inference( + voc=args.voc, + voc_config=voc_config, + voc_ckpt=args.voc_ckpt, + voc_stat=args.voc_stat) output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) diff --git a/paddlespeech/t2s/exps/synthesize_e2e.py b/paddlespeech/t2s/exps/synthesize_e2e.py index 10b33c60a..2f14ef564 100644 --- a/paddlespeech/t2s/exps/synthesize_e2e.py +++ b/paddlespeech/t2s/exps/synthesize_e2e.py @@ -42,24 +42,48 @@ def evaluate(args): print(am_config) print(voc_config) - sentences = get_sentences(args) + sentences = get_sentences(text_file=args.text, lang=args.lang) # frontend - frontend = get_frontend(args) + frontend = get_frontend( + lang=args.lang, + phones_dict=args.phones_dict, + tones_dict=args.tones_dict) # acoustic model - am_inference, am_name, am_dataset = get_am_inference(args, am_config) + am_name = args.am[:args.am.rindex('_')] + am_dataset = args.am[args.am.rindex('_') + 1:] + + am_inference = get_am_inference( + am=args.am, + am_config=am_config, + am_ckpt=args.am_ckpt, + am_stat=args.am_stat, + phones_dict=args.phones_dict, + tones_dict=args.tones_dict, + speaker_dict=args.speaker_dict) # vocoder - voc_inference = get_voc_inference(args, voc_config) + voc_inference = get_voc_inference( + voc=args.voc, + voc_config=voc_config, + voc_ckpt=args.voc_ckpt, + voc_stat=args.voc_stat) # whether dygraph to static if args.inference_dir: # acoustic model - am_inference = am_to_static(args, am_inference, am_name, am_dataset) + am_inference = am_to_static( + am_inference=am_inference, + am=args.am, + inference_dir=args.inference_dir, + speaker_dict=args.speaker_dict) # vocoder - voc_inference = voc_to_static(args, voc_inference) + voc_inference = voc_to_static( + voc_inference=voc_inference, + voc=args.voc, + inference_dir=args.inference_dir) output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) @@ -68,13 +92,15 @@ def evaluate(args): # but still not stopping in the end (NOTE by yuantian01 Feb 9 2022) if am_name == 'tacotron2': merge_sentences = True + + get_tone_ids = False + if am_name == 'speedyspeech': + get_tone_ids = True + N = 0 T = 0 for utt_id, sentence in sentences: with timer() as t: - get_tone_ids = False - if am_name == 'speedyspeech': - get_tone_ids = True if args.lang == 'zh': input_ids = frontend.get_input_ids( sentence, diff --git a/paddlespeech/t2s/exps/synthesize_streaming.py b/paddlespeech/t2s/exps/synthesize_streaming.py index f38b2d352..3659cb490 100644 --- a/paddlespeech/t2s/exps/synthesize_streaming.py +++ b/paddlespeech/t2s/exps/synthesize_streaming.py @@ -12,39 +12,29 @@ # See the License for the specific language governing permissions and # limitations under the License. import argparse -import math +import os from pathlib import Path import numpy as np import paddle import soundfile as sf import yaml +from paddle import jit +from paddle.static import InputSpec from timer import timer from yacs.config import CfgNode from paddlespeech.s2t.utils.dynamic_import import dynamic_import +from paddlespeech.t2s.exps.syn_utils import denorm +from paddlespeech.t2s.exps.syn_utils import get_chunks from paddlespeech.t2s.exps.syn_utils import get_frontend from paddlespeech.t2s.exps.syn_utils import get_sentences from paddlespeech.t2s.exps.syn_utils import get_voc_inference from paddlespeech.t2s.exps.syn_utils import model_alias +from paddlespeech.t2s.exps.syn_utils import voc_to_static from paddlespeech.t2s.utils import str2bool -def denorm(data, mean, std): - return data * std + mean - - -def get_chunks(data, chunk_size, pad_size): - data_len = data.shape[1] - chunks = [] - n = math.ceil(data_len / chunk_size) - for i in range(n): - start = max(0, i * chunk_size - pad_size) - end = min((i + 1) * chunk_size + pad_size, data_len) - chunks.append(data[:, start:end, :]) - return chunks - - def evaluate(args): # Init body. @@ -59,10 +49,13 @@ def evaluate(args): print(am_config) print(voc_config) - sentences = get_sentences(args) + sentences = get_sentences(text_file=args.text, lang=args.lang) # frontend - frontend = get_frontend(args) + frontend = get_frontend( + lang=args.lang, + phones_dict=args.phones_dict, + tones_dict=args.tones_dict) with open(args.phones_dict, "r") as f: phn_id = [line.strip().split() for line in f.readlines()] @@ -70,7 +63,6 @@ def evaluate(args): print("vocab_size:", vocab_size) # acoustic model, only support fastspeech2 here now! - # am_inference, am_name, am_dataset = get_am_inference(args, am_config) # model: {model_name}_{dataset} am_name = args.am[:args.am.rindex('_')] am_dataset = args.am[args.am.rindex('_') + 1:] @@ -84,12 +76,60 @@ def evaluate(args): am_mu = paddle.to_tensor(am_mu) am_std = paddle.to_tensor(am_std) + # am sub layers + am_encoder_infer = am.encoder_infer + am_decoder = am.decoder + am_postnet = am.postnet + # vocoder - voc_inference = get_voc_inference(args, voc_config) + voc_inference = get_voc_inference( + voc=args.voc, + voc_config=voc_config, + voc_ckpt=args.voc_ckpt, + voc_stat=args.voc_stat) + + # whether dygraph to static + if args.inference_dir: + # fastspeech2 cnndecoder to static + # am.encoder_infer + am_encoder_infer = jit.to_static( + am_encoder_infer, input_spec=[InputSpec([-1], dtype=paddle.int64)]) + paddle.jit.save(am_encoder_infer, + os.path.join(args.inference_dir, + args.am + "_am_encoder_infer")) + am_encoder_infer = paddle.jit.load( + os.path.join(args.inference_dir, args.am + "_am_encoder_infer")) + + # am.decoder + am_decoder = jit.to_static( + am_decoder, + input_spec=[InputSpec([1, -1, 384], dtype=paddle.float32)]) + paddle.jit.save(am_decoder, + os.path.join(args.inference_dir, + args.am + "_am_decoder")) + am_decoder = paddle.jit.load( + os.path.join(args.inference_dir, args.am + "_am_decoder")) + + # am.postnet + am_postnet = jit.to_static( + am_postnet, + input_spec=[InputSpec([1, 80, -1], dtype=paddle.float32)]) + paddle.jit.save(am_postnet, + os.path.join(args.inference_dir, + args.am + "_am_postnet")) + am_postnet = paddle.jit.load( + os.path.join(args.inference_dir, args.am + "_am_postnet")) + + # vocoder + voc_inference = voc_to_static( + voc_inference=voc_inference, + voc=args.voc, + inference_dir=args.inference_dir) output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) merge_sentences = True + get_tone_ids = False N = 0 T = 0 @@ -98,8 +138,6 @@ def evaluate(args): for utt_id, sentence in sentences: with timer() as t: - get_tone_ids = False - if args.lang == 'zh': input_ids = frontend.get_input_ids( sentence, @@ -108,20 +146,19 @@ def evaluate(args): phone_ids = input_ids["phone_ids"] else: - print("lang should in be 'zh' here!") + print("lang should be 'zh' here!") # merge_sentences=True here, so we only use the first item of phone_ids phone_ids = phone_ids[0] with paddle.no_grad(): # acoustic model - orig_hs, h_masks = am.encoder_infer(phone_ids) - + orig_hs = am_encoder_infer(phone_ids) if args.am_streaming: hss = get_chunks(orig_hs, chunk_size, pad_size) chunk_num = len(hss) mel_list = [] for i, hs in enumerate(hss): - before_outs, _ = am.decoder(hs) - after_outs = before_outs + am.postnet( + before_outs = am_decoder(hs) + after_outs = before_outs + am_postnet( before_outs.transpose((0, 2, 1))).transpose( (0, 2, 1)) normalized_mel = after_outs[0] @@ -140,8 +177,8 @@ def evaluate(args): mel = paddle.concat(mel_list, axis=0) else: - before_outs, _ = am.decoder(orig_hs) - after_outs = before_outs + am.postnet( + before_outs = am_decoder(orig_hs) + after_outs = before_outs + am_postnet( before_outs.transpose((0, 2, 1))).transpose((0, 2, 1)) normalized_mel = after_outs[0] mel = denorm(normalized_mel, am_mu, am_std) @@ -202,16 +239,9 @@ def parse_args(): default='pwgan_csmsc', choices=[ 'pwgan_csmsc', - 'pwgan_ljspeech', - 'pwgan_aishell3', - 'pwgan_vctk', 'mb_melgan_csmsc', 'style_melgan_csmsc', 'hifigan_csmsc', - 'hifigan_ljspeech', - 'hifigan_aishell3', - 'hifigan_vctk', - 'wavernn_csmsc', ], help='Choose vocoder type of tts task.') parser.add_argument( @@ -234,13 +264,19 @@ def parse_args(): default='zh', help='Choose model language. zh or en') + parser.add_argument( + "--inference_dir", + type=str, + default=None, + help="dir to save inference models") + parser.add_argument( "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") parser.add_argument( "--text", type=str, help="text to synthesize, a 'utt_id sentence' pair per line.") - + # streaming related parser.add_argument( "--am_streaming", type=str2bool, diff --git a/paddlespeech/t2s/exps/tacotron2/preprocess.py b/paddlespeech/t2s/exps/tacotron2/preprocess.py index 7f41089eb..14a0d7eae 100644 --- a/paddlespeech/t2s/exps/tacotron2/preprocess.py +++ b/paddlespeech/t2s/exps/tacotron2/preprocess.py @@ -82,6 +82,9 @@ def process_sentence(config: Dict[str, Any], logmel = mel_extractor.get_log_mel_fbank(wav) # change duration according to mel_length compare_duration_and_mel_length(sentences, utt_id, logmel) + # utt_id may be popped in compare_duration_and_mel_length + if utt_id not in sentences: + return None phones = sentences[utt_id][0] durations = sentences[utt_id][1] num_frames = logmel.shape[0] diff --git a/paddlespeech/t2s/exps/voice_cloning.py b/paddlespeech/t2s/exps/voice_cloning.py index 1afd21dff..9257b07de 100644 --- a/paddlespeech/t2s/exps/voice_cloning.py +++ b/paddlespeech/t2s/exps/voice_cloning.py @@ -66,10 +66,19 @@ def voice_cloning(args): print("frontend done!") # acoustic model - am_inference, *_ = get_am_inference(args, am_config) + am_inference = get_am_inference( + am=args.am, + am_config=am_config, + am_ckpt=args.am_ckpt, + am_stat=args.am_stat, + phones_dict=args.phones_dict) # vocoder - voc_inference = get_voc_inference(args, voc_config) + voc_inference = get_voc_inference( + voc=args.voc, + voc_config=voc_config, + voc_ckpt=args.voc_ckpt, + voc_stat=args.voc_stat) output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) diff --git a/paddlespeech/t2s/exps/wavernn/synthesize.py b/paddlespeech/t2s/exps/wavernn/synthesize.py index d23e9cb7e..ea48a6178 100644 --- a/paddlespeech/t2s/exps/wavernn/synthesize.py +++ b/paddlespeech/t2s/exps/wavernn/synthesize.py @@ -58,8 +58,7 @@ def main(): else: print("ngpu should >= 0 !") - model = WaveRNN( - hop_length=config.n_shift, sample_rate=config.fs, **config["model"]) + model = WaveRNN(**config["model"]) state_dict = paddle.load(args.checkpoint) model.set_state_dict(state_dict["main_params"]) diff --git a/paddlespeech/t2s/models/fastspeech2/fastspeech2.py b/paddlespeech/t2s/models/fastspeech2/fastspeech2.py index c2f1e218f..48595bb25 100644 --- a/paddlespeech/t2s/models/fastspeech2/fastspeech2.py +++ b/paddlespeech/t2s/models/fastspeech2/fastspeech2.py @@ -590,15 +590,17 @@ class FastSpeech2(nn.Layer): h_masks = self._source_mask(olens_in) else: h_masks = None - if return_after_enc: return hs, h_masks - # (B, Lmax, adim) - zs, _ = self.decoder(hs, h_masks) - # (B, Lmax, odim) + if self.decoder_type == 'cnndecoder': + # remove output masks for dygraph to static graph + zs = self.decoder(hs, h_masks) before_outs = zs else: + # (B, Lmax, adim) + zs, _ = self.decoder(hs, h_masks) + # (B, Lmax, odim) before_outs = self.feat_out(zs).reshape( (paddle.shape(zs)[0], -1, self.odim)) @@ -633,7 +635,8 @@ class FastSpeech2(nn.Layer): tone_id = tone_id.unsqueeze(0) # (1, L, odim) - hs, h_masks = self._forward( + # use *_ to avoid bug in dygraph to static graph + hs, *_ = self._forward( xs, ilens, is_inference=True, @@ -642,7 +645,7 @@ class FastSpeech2(nn.Layer): spk_emb=spk_emb, spk_id=spk_id, tone_id=tone_id) - return hs, h_masks + return hs def inference( self, @@ -667,8 +670,8 @@ class FastSpeech2(nn.Layer): use_teacher_forcing(bool, optional): Whether to use teacher forcing. If true, groundtruth of duration, pitch and energy will be used. spk_emb(Tensor, optional, optional): peaker embedding vector (spk_embed_dim,). (Default value = None) - spk_id(Tensor, optional(int64), optional): Batch of padded spk ids (1,). (Default value = None) - tone_id(Tensor, optional(int64), optional): Batch of padded tone ids (T,). (Default value = None) + spk_id(Tensor, optional(int64), optional): spk ids (1,). (Default value = None) + tone_id(Tensor, optional(int64), optional): tone ids (T,). (Default value = None) Returns: @@ -751,7 +754,6 @@ class FastSpeech2(nn.Layer): Returns: - """ if self.tone_embed_integration_type == "add": # apply projection and then add to hidden states diff --git a/paddlespeech/t2s/models/speedyspeech/speedyspeech.py b/paddlespeech/t2s/models/speedyspeech/speedyspeech.py index 44ccfc60f..ed7c0b7e4 100644 --- a/paddlespeech/t2s/models/speedyspeech/speedyspeech.py +++ b/paddlespeech/t2s/models/speedyspeech/speedyspeech.py @@ -11,17 +11,35 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from typing import List + import paddle from paddle import nn from paddlespeech.t2s.modules.nets_utils import initialize -from paddlespeech.t2s.modules.positional_encoding import sinusoid_position_encoding from paddlespeech.t2s.modules.predictor.length_regulator import LengthRegulator +from paddlespeech.t2s.modules.transformer.embedding import ScaledPositionalEncoding class ResidualBlock(nn.Layer): - def __init__(self, channels, kernel_size, dilation, n=2): + def __init__(self, + channels: int=128, + kernel_size: int=3, + dilation: int=3, + n: int=2): + """SpeedySpeech encoder module. + Args: + channels (int, optional): Feature size of the residual output(and also the input). + kernel_size (int, optional): Kernel size of the 1D convolution. + dilation (int, optional): Dilation of the 1D convolution. + n (int): Number of blocks. + """ + super().__init__() + total_pad = (dilation * (kernel_size - 1)) + begin = total_pad // 2 + end = total_pad - begin + # remove padding='same' here, cause onnx don't support dilation + 'same' padding blocks = [ nn.Sequential( nn.Conv1D( @@ -29,14 +47,20 @@ class ResidualBlock(nn.Layer): channels, kernel_size, dilation=dilation, - padding="same", - data_format="NLC"), + # make sure output T == input T + padding=((0, 0), (0, 0), (begin, end))), nn.ReLU(), - nn.BatchNorm1D(channels, data_format="NLC"), ) for _ in range(n) + nn.BatchNorm1D(channels), ) for _ in range(n) ] self.blocks = nn.Sequential(*blocks) - def forward(self, x): + def forward(self, x: paddle.Tensor): + """Calculate forward propagation. + Args: + x(Tensor): Batch of input sequences (B, hidden_size, Tmax). + Returns: + Tensor: The residual output (B, hidden_size, Tmax). + """ return x + self.blocks(x) @@ -62,7 +86,15 @@ class TextEmbedding(nn.Layer): tone_vocab_size, tone_embedding_size, tone_padding_idx) self.concat = concat - def forward(self, text, tone=None): + def forward(self, text: paddle.Tensor, tone: paddle.Tensor=None): + """Calculate forward propagation. + Args: + text(Tensor(int64)): Batch of padded token ids (B, Tmax). + tones(Tensor, optional(int64)): Batch of padded tone ids (B, Tmax). + Returns: + Tensor: The residual output (B, Tmax, embedding_size). + """ + text_embed = self.text_embedding(text) if tone is None: return text_embed @@ -75,13 +107,24 @@ class TextEmbedding(nn.Layer): class SpeedySpeechEncoder(nn.Layer): + """SpeedySpeech encoder module. + Args: + vocab_size (int): Dimension of the inputs. + tone_size (Optional[int]): Number of tones. + hidden_size (int): Number of encoder hidden units. + kernel_size (int): Kernel size of encoder. + dilations (List[int]): Dilations of encoder. + spk_num (Optional[int]): Number of speakers. + """ + def __init__(self, - vocab_size, - tone_size, - hidden_size, - kernel_size, - dilations, + vocab_size: int, + tone_size: int, + hidden_size: int=128, + kernel_size: int=3, + dilations: List[int]=[1, 3, 9, 27, 1, 3, 9, 27, 1, 1], spk_num=None): + super().__init__() self.embedding = TextEmbedding( vocab_size, @@ -109,34 +152,71 @@ class SpeedySpeechEncoder(nn.Layer): self.postnet1 = nn.Sequential(nn.Linear(hidden_size, hidden_size)) self.postnet2 = nn.Sequential( nn.ReLU(), - nn.BatchNorm1D(hidden_size, data_format="NLC"), - nn.Linear(hidden_size, hidden_size), ) - - def forward(self, text, tones, spk_id=None): + nn.BatchNorm1D(hidden_size), ) + self.linear = nn.Linear(hidden_size, hidden_size) + + def forward(self, + text: paddle.Tensor, + tones: paddle.Tensor, + spk_id: paddle.Tensor=None): + """Encoder input sequence. + Args: + text(Tensor(int64)): Batch of padded token ids (B, Tmax). + tones(Tensor, optional(int64)): Batch of padded tone ids (B, Tmax). + spk_id(Tnesor, optional(int64)): Batch of speaker ids (B,) + + Returns: + Tensor: Output tensor (B, Tmax, hidden_size). + """ embedding = self.embedding(text, tones) if self.spk_emb: embedding += self.spk_emb(spk_id).unsqueeze(1) embedding = self.prenet(embedding) - x = self.res_blocks(embedding) + x = self.res_blocks(embedding.transpose([0, 2, 1])).transpose([0, 2, 1]) + # (B, T, dim) x = embedding + self.postnet1(x) - x = self.postnet2(x) + x = self.postnet2(x.transpose([0, 2, 1])).transpose([0, 2, 1]) + x = self.linear(x) return x class DurationPredictor(nn.Layer): - def __init__(self, hidden_size): + def __init__(self, hidden_size: int=128): super().__init__() self.layers = nn.Sequential( ResidualBlock(hidden_size, 4, 1, n=1), ResidualBlock(hidden_size, 3, 1, n=1), - ResidualBlock(hidden_size, 1, 1, n=1), nn.Linear(hidden_size, 1)) + ResidualBlock(hidden_size, 1, 1, n=1), ) + self.linear = nn.Linear(hidden_size, 1) - def forward(self, x): - return paddle.squeeze(self.layers(x), -1) + def forward(self, x: paddle.Tensor): + """Calculate forward propagation. + Args: + x(Tensor): Batch of input sequences (B, Tmax, hidden_size). + + Returns: + Tensor: Batch of predicted durations in log domain (B, Tmax). + """ + x = self.layers(x.transpose([0, 2, 1])).transpose([0, 2, 1]) + x = self.linear(x) + return paddle.squeeze(x, -1) class SpeedySpeechDecoder(nn.Layer): - def __init__(self, hidden_size, output_size, kernel_size, dilations): + def __init__(self, + hidden_size: int=128, + output_size: int=80, + kernel_size: int=3, + dilations: List[int]=[ + 1, 3, 9, 27, 1, 3, 9, 27, 1, 3, 9, 27, 1, 3, 9, 27, 1, 1 + ]): + """SpeedySpeech decoder module. + Args: + hidden_size (int): Number of decoder hidden units. + kernel_size (int): Kernel size of decoder. + output_size (int): Dimension of the outputs. + dilations (List[int]): Dilations of decoder. + """ super().__init__() res_blocks = [ ResidualBlock(hidden_size, kernel_size, d, n=2) for d in dilations @@ -144,14 +224,21 @@ class SpeedySpeechDecoder(nn.Layer): self.res_blocks = nn.Sequential(*res_blocks) self.postnet1 = nn.Sequential(nn.Linear(hidden_size, hidden_size)) - self.postnet2 = nn.Sequential( - ResidualBlock(hidden_size, kernel_size, 1, n=2), - nn.Linear(hidden_size, output_size)) + self.postnet2 = ResidualBlock(hidden_size, kernel_size, 1, n=2) + self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): - xx = self.res_blocks(x) + """Decoder input sequence. + Args: + x(Tensor): Input tensor (B, time, hidden_size). + + Returns: + Tensor: Output tensor (B, time, output_size). + """ + xx = self.res_blocks(x.transpose([0, 2, 1])).transpose([0, 2, 1]) x = x + self.postnet1(xx) - x = self.postnet2(x) + x = self.postnet2(x.transpose([0, 2, 1])).transpose([0, 2, 1]) + x = self.linear(x) return x @@ -159,17 +246,35 @@ class SpeedySpeech(nn.Layer): def __init__( self, vocab_size, - encoder_hidden_size, - encoder_kernel_size, - encoder_dilations, - duration_predictor_hidden_size, - decoder_hidden_size, - decoder_output_size, - decoder_kernel_size, - decoder_dilations, - tone_size=None, - spk_num=None, - init_type: str="xavier_uniform", ): + encoder_hidden_size: int=128, + encoder_kernel_size: int=3, + encoder_dilations: List[int]=[1, 3, 9, 27, 1, 3, 9, 27, 1, 1], + duration_predictor_hidden_size: int=128, + decoder_hidden_size: int=128, + decoder_output_size: int=80, + decoder_kernel_size: int=3, + decoder_dilations: List[ + int]=[1, 3, 9, 27, 1, 3, 9, 27, 1, 3, 9, 27, 1, 3, 9, 27, 1, 1], + tone_size: int=None, + spk_num: int=None, + init_type: str="xavier_uniform", + positional_dropout_rate: int=0.1): + """Initialize SpeedySpeech module. + Args: + vocab_size (int): Dimension of the inputs. + encoder_hidden_size (int): Number of encoder hidden units. + encoder_kernel_size (int): Kernel size of encoder. + encoder_dilations (List[int]): Dilations of encoder. + duration_predictor_hidden_size (int): Number of duration predictor hidden units. + decoder_hidden_size (int): Number of decoder hidden units. + decoder_kernel_size (int): Kernel size of decoder. + decoder_dilations (List[int]): Dilations of decoder. + decoder_output_size (int): Dimension of the outputs. + tone_size (Optional[int]): Number of tones. + spk_num (Optional[int]): Number of speakers. + init_type (str): How to initialize transformer parameters. + + """ super().__init__() # initialize parameters @@ -181,6 +286,8 @@ class SpeedySpeech(nn.Layer): duration_predictor = DurationPredictor(duration_predictor_hidden_size) decoder = SpeedySpeechDecoder(decoder_hidden_size, decoder_output_size, decoder_kernel_size, decoder_dilations) + self.position_enc = ScaledPositionalEncoding(encoder_hidden_size, + positional_dropout_rate) self.encoder = encoder self.duration_predictor = duration_predictor @@ -190,7 +297,22 @@ class SpeedySpeech(nn.Layer): nn.initializer.set_global_initializer(None) - def forward(self, text, tones, durations, spk_id: paddle.Tensor=None): + def forward(self, + text: paddle.Tensor, + tones: paddle.Tensor, + durations: paddle.Tensor, + spk_id: paddle.Tensor=None): + """Calculate forward propagation. + Args: + text(Tensor(int64)): Batch of padded token ids (B, Tmax). + durations(Tensor(int64)): Batch of padded durations (B, Tmax). + tones(Tensor, optional(int64)): Batch of padded tone ids (B, Tmax). + spk_id(Tnesor, optional(int64)): Batch of speaker ids (B,) + + Returns: + Tensor: Output tensor (B, T_frames, decoder_output_size). + Tensor: Predicted durations (B, Tmax). + """ # input of embedding must be int64 text = paddle.cast(text, 'int64') tones = paddle.cast(tones, 'int64') @@ -198,23 +320,30 @@ class SpeedySpeech(nn.Layer): spk_id = paddle.cast(spk_id, 'int64') durations = paddle.cast(durations, 'int64') encodings = self.encoder(text, tones, spk_id) - pred_durations = self.duration_predictor(encodings.detach()) - # expand encodings durations_to_expand = durations encodings = self.length_regulator(encodings, durations_to_expand) - + encodings = self.position_enc(encodings) # decode - # remove positional encoding here - _, t_dec, feature_size = encodings.shape - encodings += sinusoid_position_encoding(t_dec, feature_size) decoded = self.decoder(encodings) return decoded, pred_durations - def inference(self, text, tones=None, durations=None, spk_id=None): - # text: [T] - # tones: [T] + def inference(self, + text: paddle.Tensor, + tones: paddle.Tensor=None, + durations: paddle.Tensor=None, + spk_id: paddle.Tensor=None): + """Generate the sequence of features given the sequences of characters. + Args: + text(Tensor(int64)): Input sequence of characters (T,). + tones(Tensor, optional(int64)): Batch of padded tone ids (T, ). + durations(Tensor, optional (int64)): Groundtruth of duration (T,). + spk_id(Tensor, optional(int64), optional): spk ids (1,). (Default value = None) + + Returns: + Tensor: logmel (T, decoder_output_size). + """ # input of embedding must be int64 text = paddle.cast(text, 'int64') text = text.unsqueeze(0) @@ -233,10 +362,7 @@ class SpeedySpeech(nn.Layer): durations_to_expand = durations encodings = self.length_regulator( encodings, durations_to_expand, is_inference=True) - - shape = paddle.shape(encodings) - t_dec, feature_size = shape[1], shape[2] - encodings += sinusoid_position_encoding(t_dec, feature_size) + encodings = self.position_enc(encodings) decoded = self.decoder(encodings) return decoded[0] diff --git a/paddlespeech/t2s/modules/positional_encoding.py b/paddlespeech/t2s/modules/positional_encoding.py index 7c368c3aa..715c576f5 100644 --- a/paddlespeech/t2s/modules/positional_encoding.py +++ b/paddlespeech/t2s/modules/positional_encoding.py @@ -31,8 +31,9 @@ def sinusoid_position_encoding(num_positions: int, channel = paddle.arange(0, feature_size, 2, dtype=dtype) index = paddle.arange(start_pos, start_pos + num_positions, 1, dtype=dtype) - p = (paddle.unsqueeze(index, -1) * - omega) / (10000.0**(channel / float(feature_size))) + denominator = channel / float(feature_size) + denominator = paddle.to_tensor([10000.0], dtype='float32')**denominator + p = (paddle.unsqueeze(index, -1) * omega) / denominator encodings = paddle.zeros([num_positions, feature_size], dtype=dtype) encodings[:, 0::2] = paddle.sin(p) encodings[:, 1::2] = paddle.cos(p) diff --git a/paddlespeech/t2s/modules/predictor/length_regulator.py b/paddlespeech/t2s/modules/predictor/length_regulator.py index be788e6ed..b64aa44ad 100644 --- a/paddlespeech/t2s/modules/predictor/length_regulator.py +++ b/paddlespeech/t2s/modules/predictor/length_regulator.py @@ -86,7 +86,7 @@ class LengthRegulator(nn.Layer): M[:, i] = m - init init = m M = paddle.reshape(M, shape=[t_dec_1, batch_size, t_enc]) - M = M[1:, :, :] + M = M[1:t_dec_1, :, :] M = paddle.transpose(M, (1, 0, 2)) encodings = paddle.matmul(M, encodings) return encodings diff --git a/paddlespeech/t2s/modules/residual_block.py b/paddlespeech/t2s/modules/residual_block.py index efbfce27a..5965a7203 100644 --- a/paddlespeech/t2s/modules/residual_block.py +++ b/paddlespeech/t2s/modules/residual_block.py @@ -30,7 +30,7 @@ class WaveNetResidualBlock(nn.Layer): Args: kernel_size (int, optional): Kernel size of the 1D convolution, by default 3 - residual_channels (int, optional): Feature size of the resiaudl output(and also the input), by default 64 + residual_channels (int, optional): Feature size of the residual output(and also the input), by default 64 gate_channels (int, optional): Output feature size of the 1D convolution, by default 128 skip_channels (int, optional): Feature size of the skip output, by default 64 aux_channels (int, optional): Feature size of the auxiliary input (e.g. spectrogram), by default 80 diff --git a/paddlespeech/t2s/modules/transformer/encoder.py b/paddlespeech/t2s/modules/transformer/encoder.py index f64202824..11986360a 100644 --- a/paddlespeech/t2s/modules/transformer/encoder.py +++ b/paddlespeech/t2s/modules/transformer/encoder.py @@ -347,7 +347,7 @@ class TransformerEncoder(BaseEncoder): encoder_type="transformer") def forward(self, xs, masks): - """Encode input sequence. + """Encoder input sequence. Args: xs(Tensor): Input tensor (#batch, time, idim). @@ -355,7 +355,7 @@ class TransformerEncoder(BaseEncoder): Returns: Tensor: Output tensor (#batch, time, attention_dim). - Tensor:Mask tensor (#batch, 1, time). + Tensor: Mask tensor (#batch, 1, time). """ xs = self.embed(xs) xs, masks = self.encoders(xs, masks) @@ -602,7 +602,7 @@ class CNNDecoder(nn.Layer): if masks is not None: outputs = outputs * masks outputs = outputs.transpose([0, 2, 1]) - return outputs, masks + return outputs class CNNPostnet(nn.Layer): diff --git a/paddlespeech/vector/cluster/diarization.py b/paddlespeech/vector/cluster/diarization.py index 597aa4807..a8043c227 100644 --- a/paddlespeech/vector/cluster/diarization.py +++ b/paddlespeech/vector/cluster/diarization.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022 SpeechBrain Authors. All Rights Reserved. +# Copyright (c) 2022 PaddlePaddle and SpeechBrain Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,18 +11,21 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# Modified from speechbrain(https://github.com/speechbrain/speechbrain) """ This script contains basic functions used for speaker diarization. This script has an optional dependency on open source sklearn library. A few sklearn functions are modified in this script as per requirement. """ import argparse +import copy import warnings +from distutils.util import strtobool import numpy as np import scipy import sklearn -from distutils.util import strtobool +from scipy import linalg from scipy import sparse from scipy.sparse.csgraph import connected_components from scipy.sparse.csgraph import laplacian as csgraph_laplacian @@ -345,6 +348,8 @@ class EmbeddingMeta: --------- segset : list List of session IDs as an array of strings. + modelset : list + List of model IDs as an array of strings. stats : tensor An ndarray of float64. Each line contains embedding from the corresponding session. @@ -353,15 +358,20 @@ class EmbeddingMeta: def __init__( self, segset=None, + modelset=None, stats=None, ): if segset is None: - self.segset = numpy.empty(0, dtype="|O") - self.stats = numpy.array([], dtype=np.float64) + self.segset = np.empty(0, dtype="|O") + self.modelset = np.empty(0, dtype="|O") + self.stats = np.array([], dtype=np.float64) else: self.segset = segset + self.modelset = modelset self.stats = stats + self.stat0 = np.array([[1.0]] * self.stats.shape[0]) + def norm_stats(self): """ Divide all first-order statistics by their Euclidean norm. @@ -370,6 +380,188 @@ class EmbeddingMeta: vect_norm = np.clip(np.linalg.norm(self.stats, axis=1), 1e-08, np.inf) self.stats = (self.stats.transpose() / vect_norm).transpose() + def get_mean_stats(self): + """ + Return the mean of first order statistics. + """ + mu = np.mean(self.stats, axis=0) + return mu + + def get_total_covariance_stats(self): + """ + Compute and return the total covariance matrix of the first-order statistics. + """ + C = self.stats - self.stats.mean(axis=0) + return np.dot(C.transpose(), C) / self.stats.shape[0] + + def get_model_stat0(self, mod_id): + """Return zero-order statistics of a given model + + Arguments + --------- + mod_id : str + ID of the model which stat0 will be returned. + """ + S = self.stat0[self.modelset == mod_id, :] + return S + + def get_model_stats(self, mod_id): + """Return first-order statistics of a given model. + + Arguments + --------- + mod_id : str + ID of the model which stat1 will be returned. + """ + return self.stats[self.modelset == mod_id, :] + + def sum_stat_per_model(self): + """ + Sum the zero- and first-order statistics per model and store them + in a new EmbeddingMeta. + Returns a EmbeddingMeta object with the statistics summed per model + and a numpy array with session_per_model. + """ + + sts_per_model = EmbeddingMeta() + sts_per_model.modelset = np.unique( + self.modelset) # nd: get uniq spkr ids + sts_per_model.segset = copy.deepcopy(sts_per_model.modelset) + sts_per_model.stat0 = np.zeros( + (sts_per_model.modelset.shape[0], self.stat0.shape[1]), + dtype=np.float64, ) + sts_per_model.stats = np.zeros( + (sts_per_model.modelset.shape[0], self.stats.shape[1]), + dtype=np.float64, ) + + session_per_model = np.zeros(np.unique(self.modelset).shape[0]) + + # For each model sum the stats + for idx, model in enumerate(sts_per_model.modelset): + sts_per_model.stat0[idx, :] = self.get_model_stat0(model).sum( + axis=0) + sts_per_model.stats[idx, :] = self.get_model_stats(model).sum( + axis=0) + session_per_model[idx] += self.get_model_stats(model).shape[0] + return sts_per_model, session_per_model + + def center_stats(self, mu): + """ + Center first order statistics. + + Arguments + --------- + mu : array + Array to center on. + """ + + dim = self.stats.shape[1] / self.stat0.shape[1] + index_map = np.repeat(np.arange(self.stat0.shape[1]), dim) + self.stats = self.stats - (self.stat0[:, index_map] * + mu.astype(np.float64)) + + def rotate_stats(self, R): + """ + Rotate first-order statistics by a right-product. + + Arguments + --------- + R : ndarray + Matrix to use for right product on the first order statistics. + """ + self.stats = np.dot(self.stats, R) + + def whiten_stats(self, mu, sigma, isSqrInvSigma=False): + """ + Whiten first-order statistics + If sigma.ndim == 1, case of a diagonal covariance. + If sigma.ndim == 2, case of a single Gaussian with full covariance. + If sigma.ndim == 3, case of a full covariance UBM. + + Arguments + --------- + mu : array + Mean vector to be subtracted from the statistics. + sigma : narray + Co-variance matrix or covariance super-vector. + isSqrInvSigma : bool + True if the input Sigma matrix is the inverse of the square root of a covariance matrix. + """ + + if sigma.ndim == 1: + self.center_stats(mu) + self.stats = self.stats / np.sqrt(sigma.astype(np.float64)) + + elif sigma.ndim == 2: + # Compute the inverse square root of the co-variance matrix Sigma + sqr_inv_sigma = sigma + + if not isSqrInvSigma: + # eigen_values, eigen_vectors = scipy.linalg.eigh(sigma) + eigen_values, eigen_vectors = linalg.eigh(sigma) + ind = eigen_values.real.argsort()[::-1] + eigen_values = eigen_values.real[ind] + eigen_vectors = eigen_vectors.real[:, ind] + + sqr_inv_eval_sigma = 1 / np.sqrt(eigen_values.real) + sqr_inv_sigma = np.dot(eigen_vectors, + np.diag(sqr_inv_eval_sigma)) + else: + pass + + # Whitening of the first-order statistics + self.center_stats(mu) # CENTERING + self.rotate_stats(sqr_inv_sigma) + + elif sigma.ndim == 3: + # we assume that sigma is a 3D ndarray of size D x n x n + # where D is the number of distributions and n is the dimension of a single distribution + n = self.stats.shape[1] // self.stat0.shape[1] + sess_nb = self.stat0.shape[0] + self.center_stats(mu) + self.stats = (np.einsum("ikj,ikl->ilj", + self.stats.T.reshape(-1, n, sess_nb), sigma) + .reshape(-1, sess_nb).T) + + else: + raise Exception("Wrong dimension of Sigma, must be 1 or 2") + + def align_models(self, model_list): + """ + Align models of the current EmbeddingMeta to match a list of models + provided as input parameter. The size of the StatServer might be + reduced to match the input list of models. + + Arguments + --------- + model_list : ndarray of strings + List of models to match. + """ + indx = np.array( + [np.argwhere(self.modelset == v)[0][0] for v in model_list]) + self.segset = self.segset[indx] + self.modelset = self.modelset[indx] + self.stat0 = self.stat0[indx, :] + self.stats = self.stats[indx, :] + + def align_segments(self, segment_list): + """ + Align segments of the current EmbeddingMeta to match a list of segment + provided as input parameter. The size of the StatServer might be + reduced to match the input list of segments. + + Arguments + --------- + segment_list: ndarray of strings + list of segments to match + """ + indx = np.array( + [np.argwhere(self.segset == v)[0][0] for v in segment_list]) + self.segset = self.segset[indx] + self.modelset = self.modelset[indx] + self.stat0 = self.stat0[indx, :] + self.stats = self.stats[indx, :] + class SpecClustUnorm: """ @@ -746,6 +938,77 @@ def merge_ssegs_same_speaker(lol): return new_lol +def write_ders_file(ref_rttm, DER, out_der_file): + """Write the final DERs for individual recording. + + Arguments + --------- + ref_rttm : str + Reference RTTM file. + DER : array + Array containing DER values of each recording. + out_der_file : str + File to write the DERs. + """ + + rttm = read_rttm(ref_rttm) + spkr_info = list(filter(lambda x: x.startswith("SPKR-INFO"), rttm)) + + rec_id_list = [] + count = 0 + + with open(out_der_file, "w") as f: + for row in spkr_info: + a = row.split(" ") + rec_id = a[1] + if rec_id not in rec_id_list: + r = [rec_id, str(round(DER[count], 2))] + rec_id_list.append(rec_id) + line_str = " ".join(r) + f.write("%s\n" % line_str) + count += 1 + r = ["OVERALL ", str(round(DER[count], 2))] + line_str = " ".join(r) + f.write("%s\n" % line_str) + + +def get_oracle_num_spkrs(rec_id, spkr_info): + """ + Returns actual number of speakers in a recording from the ground-truth. + This can be used when the condition is oracle number of speakers. + + Arguments + --------- + rec_id : str + Recording ID for which the number of speakers have to be obtained. + spkr_info : list + Header of the RTTM file. Starting with `SPKR-INFO`. + + Example + ------- + >>> from speechbrain.processing import diarization as diar + >>> spkr_info = ['SPKR-INFO ES2011a 0 unknown ES2011a.A ', + ... 'SPKR-INFO ES2011a 0 unknown ES2011a.B ', + ... 'SPKR-INFO ES2011a 0 unknown ES2011a.C ', + ... 'SPKR-INFO ES2011a 0 unknown ES2011a.D ', + ... 'SPKR-INFO ES2011b 0 unknown ES2011b.A ', + ... 'SPKR-INFO ES2011b 0 unknown ES2011b.B ', + ... 'SPKR-INFO ES2011b 0 unknown ES2011b.C '] + >>> diar.get_oracle_num_spkrs('ES2011a', spkr_info) + 4 + >>> diar.get_oracle_num_spkrs('ES2011b', spkr_info) + 3 + """ + + num_spkrs = 0 + for line in spkr_info: + if rec_id in line: + # Since rec_id is prefix for each speaker + num_spkrs += 1 + + return num_spkrs + + def distribute_overlap(lol): """ Distributes the overlapped speech equally among the adjacent segments @@ -826,6 +1089,29 @@ def distribute_overlap(lol): return new_lol +def read_rttm(rttm_file_path): + """ + Reads and returns RTTM in list format. + + Arguments + --------- + rttm_file_path : str + Path to the RTTM file to be read. + + Returns + ------- + rttm : list + List containing rows of RTTM file. + """ + + rttm = [] + with open(rttm_file_path, "r") as f: + for line in f: + entry = line[:-1] + rttm.append(entry) + return rttm + + def write_rttm(segs_list, out_rttm_file): """ Writes the segment list in RTTM format (A standard NIST format). diff --git a/paddlespeech/vector/cluster/plda.py b/paddlespeech/vector/cluster/plda.py new file mode 100644 index 000000000..81def4356 --- /dev/null +++ b/paddlespeech/vector/cluster/plda.py @@ -0,0 +1,575 @@ +# Copyright (c) 2022 PaddlePaddle and SpeechBrain Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""A popular speaker recognition/diarization model (LDA and PLDA). + +Relevant Papers + - This implementation of PLDA is based on the following papers. + + - PLDA model Training + * Ye Jiang et. al, "PLDA Modeling in I-Vector and Supervector Space for Speaker Verification," in Interspeech, 2012. + * Patrick Kenny et. al, "PLDA for speaker verification with utterances of arbitrary duration," in ICASSP, 2013. + + - PLDA scoring (fast scoring) + * Daniel Garcia-Romero et. al, “Analysis of i-vector length normalization in speaker recognition systems,” in Interspeech, 2011. + * Weiwei-LIN et. al, "Fast Scoring for PLDA with Uncertainty Propagation," in Odyssey, 2016. + * Kong Aik Lee et. al, "Multi-session PLDA Scoring of I-vector for Partially Open-Set Speaker Detection," in Interspeech 2013. + +Credits + This code is adapted from: https://git-lium.univ-lemans.fr/Larcher/sidekit +""" +import copy +import pickle + +import numpy +from scipy import linalg + +from paddlespeech.vector.cluster.diarization import EmbeddingMeta + + +def ismember(list1, list2): + c = [item in list2 for item in list1] + return c + + +class Ndx: + """ + A class that encodes trial index information. It has a list of + model names and a list of test segment names and a matrix + indicating which combinations of model and test segment are + trials of interest. + + Arguments + --------- + modelset : list + List of unique models in a ndarray. + segset : list + List of unique test segments in a ndarray. + trialmask : 2D ndarray of bool. + Rows correspond to the models and columns to the test segments. True, if the trial is of interest. + """ + + def __init__(self, + ndx_file_name="", + models=numpy.array([]), + testsegs=numpy.array([])): + """ + Initialize a Ndx object by loading information from a file. + + Arguments + --------- + ndx_file_name : str + Name of the file to load. + """ + self.modelset = numpy.empty(0, dtype="|O") + self.segset = numpy.empty(0, dtype="|O") + self.trialmask = numpy.array([], dtype="bool") + + if ndx_file_name == "": + # This is needed to make sizes same + d = models.shape[0] - testsegs.shape[0] + if d != 0: + if d > 0: + last = str(testsegs[-1]) + pad = numpy.array([last] * d) + testsegs = numpy.hstack((testsegs, pad)) + # pad = testsegs[-d:] + # testsegs = numpy.concatenate((testsegs, pad), axis=1) + else: + d = abs(d) + last = str(models[-1]) + pad = numpy.array([last] * d) + models = numpy.hstack((models, pad)) + # pad = models[-d:] + # models = numpy.concatenate((models, pad), axis=1) + + modelset = numpy.unique(models) + segset = numpy.unique(testsegs) + + trialmask = numpy.zeros( + (modelset.shape[0], segset.shape[0]), dtype="bool") + for m in range(modelset.shape[0]): + segs = testsegs[numpy.array(ismember(models, modelset[m]))] + trialmask[m, ] = ismember(segset, segs) # noqa E231 + + self.modelset = modelset + self.segset = segset + self.trialmask = trialmask + assert self.validate(), "Wrong Ndx format" + + else: + ndx = Ndx.read(ndx_file_name) + self.modelset = ndx.modelset + self.segset = ndx.segset + self.trialmask = ndx.trialmask + + def save_ndx_object(self, output_file_name): + with open(output_file_name, "wb") as output: + pickle.dump(self, output, pickle.HIGHEST_PROTOCOL) + + def filter(self, modlist, seglist, keep): + """ + Removes some of the information in an Ndx. Useful for creating a + gender specific Ndx from a pooled gender Ndx. Depending on the + value of \'keep\', the two input lists indicate the strings to + retain or the strings to discard. + + Arguments + --------- + modlist : array + A cell array of strings which will be compared with the modelset of 'inndx'. + seglist : array + A cell array of strings which will be compared with the segset of 'inndx'. + keep : bool + Indicating whether modlist and seglist are the models to keep or discard. + """ + if keep: + keepmods = modlist + keepsegs = seglist + else: + keepmods = diff(self.modelset, modlist) + keepsegs = diff(self.segset, seglist) + + keepmodidx = numpy.array(ismember(self.modelset, keepmods)) + keepsegidx = numpy.array(ismember(self.segset, keepsegs)) + + outndx = Ndx() + outndx.modelset = self.modelset[keepmodidx] + outndx.segset = self.segset[keepsegidx] + tmp = self.trialmask[numpy.array(keepmodidx), :] + outndx.trialmask = tmp[:, numpy.array(keepsegidx)] + + assert outndx.validate, "Wrong Ndx format" + + if self.modelset.shape[0] > outndx.modelset.shape[0]: + print( + "Number of models reduced from %d to %d" % + self.modelset.shape[0], + outndx.modelset.shape[0], ) + if self.segset.shape[0] > outndx.segset.shape[0]: + print( + "Number of test segments reduced from %d to %d", + self.segset.shape[0], + outndx.segset.shape[0], ) + return outndx + + def validate(self): + """ + Checks that an object of type Ndx obeys certain rules that + must always be true. Returns a boolean value indicating whether the object is valid + """ + ok = isinstance(self.modelset, numpy.ndarray) + ok &= isinstance(self.segset, numpy.ndarray) + ok &= isinstance(self.trialmask, numpy.ndarray) + + ok &= self.modelset.ndim == 1 + ok &= self.segset.ndim == 1 + ok &= self.trialmask.ndim == 2 + + ok &= self.trialmask.shape == (self.modelset.shape[0], + self.segset.shape[0], ) + return ok + + +class Scores: + """ + A class for storing scores for trials. The modelset and segset + fields are lists of model and test segment names respectively. + The element i,j of scoremat and scoremask corresponds to the + trial involving model i and test segment j. + + Arguments + --------- + modelset : list + List of unique models in a ndarray. + segset : list + List of unique test segments in a ndarray. + scoremask : 2D ndarray of bool + Indicates the trials of interest, i.e., + the entry i,j in scoremat should be ignored if scoremask[i,j] is False. + scoremat : 2D ndarray + Scores matrix. + """ + + def __init__(self, scores_file_name=""): + """ + Initialize a Scores object by loading information from a file HDF5 format. + + Arguments + --------- + scores_file_name : str + Name of the file to load. + """ + self.modelset = numpy.empty(0, dtype="|O") + self.segset = numpy.empty(0, dtype="|O") + self.scoremask = numpy.array([], dtype="bool") + self.scoremat = numpy.array([]) + + if scores_file_name == "": + pass + else: + tmp = Scores.read(scores_file_name) + self.modelset = tmp.modelset + self.segset = tmp.segset + self.scoremask = tmp.scoremask + self.scoremat = tmp.scoremat + + def __repr__(self): + ch = "modelset:\n" + ch += self.modelset + "\n" + ch += "segset:\n" + ch += self.segset + "\n" + ch += "scoremask:\n" + ch += self.scoremask.__repr__() + "\n" + ch += "scoremat:\n" + ch += self.scoremat.__repr__() + "\n" + + +def fa_model_loop( + batch_start, + mini_batch_indices, + factor_analyser, + stat0, + stats, + e_h, + e_hh, ): + """ + A function for PLDA estimation. + + Arguments + --------- + batch_start : int + Index to start at in the list. + mini_batch_indices : list + Indices of the elements in the list (should start at zero). + factor_analyser : instance of PLDA class + PLDA class object. + stat0 : tensor + Matrix of zero-order statistics. + stats: tensor + Matrix of first-order statistics. + e_h : tensor + An accumulator matrix. + e_hh: tensor + An accumulator matrix. + """ + rank = factor_analyser.F.shape[1] + if factor_analyser.Sigma.ndim == 2: + A = factor_analyser.F.T.dot(factor_analyser.F) + inv_lambda_unique = dict() + for sess in numpy.unique(stat0[:, 0]): + inv_lambda_unique[sess] = linalg.inv(sess * A + numpy.eye(A.shape[ + 0])) + + tmp = numpy.zeros( + (factor_analyser.F.shape[1], factor_analyser.F.shape[1]), + dtype=numpy.float64, ) + + for idx in mini_batch_indices: + if factor_analyser.Sigma.ndim == 1: + inv_lambda = linalg.inv( + numpy.eye(rank) + (factor_analyser.F.T * stat0[ + idx + batch_start, :]).dot(factor_analyser.F)) + else: + inv_lambda = inv_lambda_unique[stat0[idx + batch_start, 0]] + + aux = factor_analyser.F.T.dot(stats[idx + batch_start, :]) + numpy.dot(aux, inv_lambda, out=e_h[idx]) + e_hh[idx] = inv_lambda + numpy.outer(e_h[idx], e_h[idx], tmp) + + +def _check_missing_model(enroll, test, ndx): + # Remove missing models and test segments + clean_ndx = ndx.filter(enroll.modelset, test.segset, True) + + # Align EmbeddingMeta to match the clean_ndx + enroll.align_models(clean_ndx.modelset) + test.align_segments(clean_ndx.segset) + + return clean_ndx + + +class PLDA: + """ + A class to train PLDA model from embeddings. + + The input is in paddlespeech.vector.cluster.diarization.EmbeddingMeta format. + Trains a simplified PLDA model no within-class covariance matrix but full residual covariance matrix. + + Arguments + --------- + mean : tensor + Mean of the vectors. + F : tensor + Eigenvoice matrix. + Sigma : tensor + Residual matrix. + """ + + def __init__( + self, + mean=None, + F=None, + Sigma=None, + rank_f=100, + nb_iter=10, + scaling_factor=1.0, ): + self.mean = None + self.F = None + self.Sigma = None + self.rank_f = rank_f + self.nb_iter = nb_iter + self.scaling_factor = scaling_factor + + if mean is not None: + self.mean = mean + if F is not None: + self.F = F + if Sigma is not None: + self.Sigma = Sigma + + def plda( + self, + emb_meta=None, + output_file_name=None, ): + """ + Trains PLDA model with no within class covariance matrix but full residual covariance matrix. + + Arguments + --------- + emb_meta : paddlespeech.vector.cluster.diarization.EmbeddingMeta + Contains vectors and meta-information to perform PLDA + rank_f : int + Rank of the between-class covariance matrix. + nb_iter : int + Number of iterations to run. + scaling_factor : float + Scaling factor to downscale statistics (value between 0 and 1). + output_file_name : str + Name of the output file where to store PLDA model. + """ + + # Dimension of the vector (x-vectors stored in stats) + vect_size = emb_meta.stats.shape[1] + + # Initialize mean and residual covariance from the training data + self.mean = emb_meta.get_mean_stats() + self.Sigma = emb_meta.get_total_covariance_stats() + + # Sum stat0 and stat1 for each speaker model + model_shifted_stat, session_per_model = emb_meta.sum_stat_per_model() + + # Number of speakers (classes) in training set + class_nb = model_shifted_stat.modelset.shape[0] + + # Multiply statistics by scaling_factor + model_shifted_stat.stat0 *= self.scaling_factor + model_shifted_stat.stats *= self.scaling_factor + session_per_model *= self.scaling_factor + + # Covariance for stats + sigma_obs = emb_meta.get_total_covariance_stats() + evals, evecs = linalg.eigh(sigma_obs) + + # Initial F (eigen voice matrix) from rank + idx = numpy.argsort(evals)[::-1] + evecs = evecs.real[:, idx[:self.rank_f]] + self.F = evecs[:, :self.rank_f] + + # Estimate PLDA model by iterating the EM algorithm + for it in range(self.nb_iter): + + # E-step + + # Copy stats as they will be whitened with a different Sigma for each iteration + local_stat = copy.deepcopy(model_shifted_stat) + + # Whiten statistics (with the new mean and Sigma) + local_stat.whiten_stats(self.mean, self.Sigma) + + # Whiten the EigenVoice matrix + eigen_values, eigen_vectors = linalg.eigh(self.Sigma) + ind = eigen_values.real.argsort()[::-1] + eigen_values = eigen_values.real[ind] + eigen_vectors = eigen_vectors.real[:, ind] + sqr_inv_eval_sigma = 1 / numpy.sqrt(eigen_values.real) + sqr_inv_sigma = numpy.dot(eigen_vectors, + numpy.diag(sqr_inv_eval_sigma)) + self.F = sqr_inv_sigma.T.dot(self.F) + + # Replicate self.stat0 + index_map = numpy.zeros(vect_size, dtype=int) + _stat0 = local_stat.stat0[:, index_map] + + e_h = numpy.zeros((class_nb, self.rank_f)) + e_hh = numpy.zeros((class_nb, self.rank_f, self.rank_f)) + + # loop on model id's + fa_model_loop( + batch_start=0, + mini_batch_indices=numpy.arange(class_nb), + factor_analyser=self, + stat0=_stat0, + stats=local_stat.stats, + e_h=e_h, + e_hh=e_hh, ) + + # Accumulate for minimum divergence step + _R = numpy.sum(e_hh, axis=0) / session_per_model.shape[0] + + _C = e_h.T.dot(local_stat.stats).dot(linalg.inv(sqr_inv_sigma)) + _A = numpy.einsum("ijk,i->jk", e_hh, local_stat.stat0.squeeze()) + + # M-step + self.F = linalg.solve(_A, _C).T + + # Update the residual covariance + self.Sigma = sigma_obs - self.F.dot(_C) / session_per_model.sum() + + # Minimum Divergence step + self.F = self.F.dot(linalg.cholesky(_R)) + + def scoring( + self, + enroll, + test, + ndx, + test_uncertainty=None, + Vtrans=None, + p_known=0.0, + scaling_factor=1.0, + check_missing=True, ): + """ + Compute the PLDA scores between to sets of vectors. The list of + trials to perform is given in an Ndx object. PLDA matrices have to be + pre-computed. i-vectors/x-vectors are supposed to be whitened before. + + Arguments + --------- + enroll : paddlespeech.vector.cluster.diarization.EmbeddingMeta + A EmbeddingMeta in which stats are xvectors. + test : paddlespeech.vector.cluster.diarization.EmbeddingMeta + A EmbeddingMeta in which stats are xvectors. + ndx : paddlespeech.vector.cluster.plda.Ndx + An Ndx object defining the list of trials to perform. + p_known : float + Probability of having a known speaker for open-set + identification case (=1 for the verification task and =0 for the + closed-set case). + check_missing : bool + If True, check that all models and segments exist. + """ + + enroll_ctr = copy.deepcopy(enroll) + test_ctr = copy.deepcopy(test) + + # Remove missing models and test segments + if check_missing: + clean_ndx = _check_missing_model(enroll_ctr, test_ctr, ndx) + else: + clean_ndx = ndx + + # Center the i-vectors around the PLDA mean + enroll_ctr.center_stats(self.mean) + test_ctr.center_stats(self.mean) + + # Compute constant component of the PLDA distribution + invSigma = linalg.inv(self.Sigma) + I_spk = numpy.eye(self.F.shape[1], dtype="float") + + K = self.F.T.dot(invSigma * scaling_factor).dot(self.F) + K1 = linalg.inv(K + I_spk) + K2 = linalg.inv(2 * K + I_spk) + + # Compute the Gaussian distribution constant + alpha1 = numpy.linalg.slogdet(K1)[1] + alpha2 = numpy.linalg.slogdet(K2)[1] + plda_cst = alpha2 / 2.0 - alpha1 + + # Compute intermediate matrices + Sigma_ac = numpy.dot(self.F, self.F.T) + Sigma_tot = Sigma_ac + self.Sigma + Sigma_tot_inv = linalg.inv(Sigma_tot) + + Tmp = linalg.inv(Sigma_tot - Sigma_ac.dot(Sigma_tot_inv).dot(Sigma_ac)) + Phi = Sigma_tot_inv - Tmp + Psi = Sigma_tot_inv.dot(Sigma_ac).dot(Tmp) + + # Compute the different parts of PLDA score + model_part = 0.5 * numpy.einsum("ij, ji->i", + enroll_ctr.stats.dot(Phi), + enroll_ctr.stats.T) + seg_part = 0.5 * numpy.einsum("ij, ji->i", + test_ctr.stats.dot(Phi), test_ctr.stats.T) + + # Compute verification scores + score = Scores() # noqa F821 + score.modelset = clean_ndx.modelset + score.segset = clean_ndx.segset + score.scoremask = clean_ndx.trialmask + + score.scoremat = model_part[:, numpy.newaxis] + seg_part + plda_cst + score.scoremat += enroll_ctr.stats.dot(Psi).dot(test_ctr.stats.T) + score.scoremat *= scaling_factor + + # Case of open-set identification, we compute the log-likelihood + # by taking into account the probability of having a known impostor + # or an out-of set class + if p_known != 0: + N = score.scoremat.shape[0] + open_set_scores = numpy.empty(score.scoremat.shape) + tmp = numpy.exp(score.scoremat) + for ii in range(N): + # open-set term + open_set_scores[ii, :] = score.scoremat[ii, :] - numpy.log( + p_known * tmp[~(numpy.arange(N) == ii)].sum(axis=0) / ( + N - 1) + (1 - p_known)) + score.scoremat = open_set_scores + + return score + + +if __name__ == '__main__': + import random + + dim, N, n_spkrs = 10, 100, 10 + train_xv = numpy.random.rand(N, dim) + md = ['md' + str(random.randrange(1, n_spkrs, 1)) for i in range(N)] # spk + modelset = numpy.array(md, dtype="|O") + sg = ['sg' + str(i) for i in range(N)] # utt + segset = numpy.array(sg, dtype="|O") + stat0 = numpy.array([[1.0]] * N) + xvectors_stat = EmbeddingMeta( + modelset=modelset, segset=segset, stats=train_xv) + # Training PLDA model: M ~ (mean, F, Sigma) + plda = PLDA(rank_f=5) + plda.plda(xvectors_stat) + print(plda.mean.shape) #(10,) + print(plda.F.shape) #(10, 5) + print(plda.Sigma.shape) #(10, 10) + # Enrollment (20 utts), + en_N = 20 + en_xv = numpy.random.rand(en_N, dim) + en_sgs = ['en' + str(i) for i in range(en_N)] + en_sets = numpy.array(en_sgs, dtype="|O") + en_stat = EmbeddingMeta(modelset=en_sets, segset=en_sets, stats=en_xv) + # Test (30 utts) + te_N = 30 + te_xv = numpy.random.rand(te_N, dim) + te_sgs = ['te' + str(i) for i in range(te_N)] + te_sets = numpy.array(te_sgs, dtype="|O") + te_stat = EmbeddingMeta(modelset=te_sets, segset=te_sets, stats=te_xv) + ndx = Ndx(models=en_sets, testsegs=te_sets) # trials + # PLDA Scoring + scores_plda = plda.scoring(en_stat, te_stat, ndx) + print(scores_plda.scoremat.shape) #(20, 30) diff --git a/paddlespeech/vector/exps/ecapa_tdnn/test.py b/paddlespeech/vector/exps/ecapa_tdnn/test.py index d0de6dc51..1b38075d6 100644 --- a/paddlespeech/vector/exps/ecapa_tdnn/test.py +++ b/paddlespeech/vector/exps/ecapa_tdnn/test.py @@ -21,10 +21,11 @@ from paddle.io import DataLoader from tqdm import tqdm from yacs.config import CfgNode -from paddleaudio.datasets import VoxCeleb from paddleaudio.metric import compute_eer from paddlespeech.s2t.utils.log import Log from paddlespeech.vector.io.batch import batch_feature_normalize +from paddlespeech.vector.io.dataset import CSVDataset +from paddlespeech.vector.io.embedding_norm import InputNormalization from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn from paddlespeech.vector.modules.sid_model import SpeakerIdetification from paddlespeech.vector.training.seeding import seed_everything @@ -32,21 +33,129 @@ from paddlespeech.vector.training.seeding import seed_everything logger = Log(__name__).getlog() +def compute_dataset_embedding(data_loader, model, mean_var_norm_emb, config, + id2embedding): + """compute the dataset embeddings + + Args: + data_loader (paddle.io.Dataloader): the dataset loader to be compute the embedding + model (paddle.nn.Layer): the speaker verification model + mean_var_norm_emb : compute the embedding mean and std norm + config (yacs.config.CfgNode): the yaml config + """ + logger.info( + f'Computing embeddings on {data_loader.dataset.csv_path} dataset') + with paddle.no_grad(): + for batch_idx, batch in enumerate(tqdm(data_loader)): + + # stage 8-1: extrac the audio embedding + ids, feats, lengths = batch['ids'], batch['feats'], batch['lengths'] + embeddings = model.backbone(feats, lengths).squeeze( + -1) # (N, emb_size, 1) -> (N, emb_size) + + # Global embedding normalization. + # if we use the global embedding norm + # eer can reduece about relative 10% + if config.global_embedding_norm and mean_var_norm_emb: + lengths = paddle.ones([embeddings.shape[0]]) + embeddings = mean_var_norm_emb(embeddings, lengths) + + # Update embedding dict. + id2embedding.update(dict(zip(ids, embeddings))) + + +def compute_verification_scores(id2embedding, train_cohort, config): + """Compute the verification trial scores + + Args: + id2embedding (dict): the utterance embedding + train_cohort (paddle.tensor): the cohort dataset embedding + config (yacs.config.CfgNode): the yaml config + + Returns: + the scores and the trial labels, + 1 refers the target and 0 refers the nontarget in labels + """ + labels = [] + enroll_ids = [] + test_ids = [] + logger.info(f"read the trial from {config.verification_file}") + cos_sim_func = paddle.nn.CosineSimilarity(axis=-1) + scores = [] + with open(config.verification_file, 'r') as f: + for line in f.readlines(): + label, enroll_id, test_id = line.strip().split(' ') + enroll_id = enroll_id.split('.')[0].replace('/', '-') + test_id = test_id.split('.')[0].replace('/', '-') + labels.append(int(label)) + + enroll_emb = id2embedding[enroll_id] + test_emb = id2embedding[test_id] + score = cos_sim_func(enroll_emb, test_emb).item() + + if "score_norm" in config: + # Getting norm stats for enroll impostors + enroll_rep = paddle.tile( + enroll_emb, repeat_times=[train_cohort.shape[0], 1]) + score_e_c = cos_sim_func(enroll_rep, train_cohort) + if "cohort_size" in config: + score_e_c, _ = paddle.topk( + score_e_c, k=config.cohort_size, axis=0) + mean_e_c = paddle.mean(score_e_c, axis=0) + std_e_c = paddle.std(score_e_c, axis=0) + + # Getting norm stats for test impostors + test_rep = paddle.tile( + test_emb, repeat_times=[train_cohort.shape[0], 1]) + score_t_c = cos_sim_func(test_rep, train_cohort) + if "cohort_size" in config: + score_t_c, _ = paddle.topk( + score_t_c, k=config.cohort_size, axis=0) + mean_t_c = paddle.mean(score_t_c, axis=0) + std_t_c = paddle.std(score_t_c, axis=0) + + if config.score_norm == "s-norm": + score_e = (score - mean_e_c) / std_e_c + score_t = (score - mean_t_c) / std_t_c + + score = 0.5 * (score_e + score_t) + elif config.score_norm == "z-norm": + score = (score - mean_e_c) / std_e_c + elif config.score_norm == "t-norm": + score = (score - mean_t_c) / std_t_c + + scores.append(score) + + return scores, labels + + def main(args, config): + """The main process for test the speaker verification model + + Args: + args (argparse.Namespace): the command line args namespace + config (yacs.config.CfgNode): the yaml config + """ + # stage0: set the training device, cpu or gpu + # if set the gpu, paddlespeech will select a gpu according the env CUDA_VISIBLE_DEVICES paddle.set_device(args.device) - # set the random seed, it is a must for multiprocess training + # set the random seed, it is the necessary measures for multiprocess training seed_everything(config.seed) # stage1: build the dnn backbone model network + # we will extract the audio embedding from the backbone model ecapa_tdnn = EcapaTdnn(**config.model) # stage2: build the speaker verification eval instance with backbone model + # because the checkpoint dict name has the SpeakerIdetification prefix + # so we need to create the SpeakerIdetification instance + # but we acutally use the backbone model to extact the audio embedding model = SpeakerIdetification( backbone=ecapa_tdnn, num_class=config.num_speakers) # stage3: load the pre-trained model - # we get the last model from the epoch and save_interval + # generally, we get the last model from the epoch args.load_checkpoint = os.path.abspath( os.path.expanduser(args.load_checkpoint)) @@ -57,35 +166,33 @@ def main(args, config): logger.info(f'Checkpoint loaded from {args.load_checkpoint}') # stage4: construct the enroll and test dataloader - - enroll_dataset = VoxCeleb( - subset='enroll', - target_dir=args.data_dir, + # Now, wo think the enroll dataset is in the {args.data_dir}/vox/csv/enroll.csv, + # and the test dataset is in the {args.data_dir}/vox/csv/test.csv + enroll_dataset = CSVDataset( + os.path.join(args.data_dir, "vox/csv/enroll.csv"), feat_type='melspectrogram', random_chunk=False, n_mels=config.n_mels, window_size=config.window_size, hop_length=config.hop_size) enroll_sampler = BatchSampler( - enroll_dataset, batch_size=config.batch_size, - shuffle=True) # Shuffle to make embedding normalization more robust. - enrol_loader = DataLoader(enroll_dataset, + enroll_dataset, batch_size=config.batch_size, shuffle=False) + enroll_loader = DataLoader(enroll_dataset, batch_sampler=enroll_sampler, collate_fn=lambda x: batch_feature_normalize( - x, mean_norm=True, std_norm=False), + x, mean_norm=True, std_norm=False), num_workers=config.num_workers, return_list=True,) - test_dataset = VoxCeleb( - subset='test', - target_dir=args.data_dir, + + test_dataset = CSVDataset( + os.path.join(args.data_dir, "vox/csv/test.csv"), feat_type='melspectrogram', random_chunk=False, n_mels=config.n_mels, window_size=config.window_size, hop_length=config.hop_size) - test_sampler = BatchSampler( - test_dataset, batch_size=config.batch_size, shuffle=True) + test_dataset, batch_size=config.batch_size, shuffle=False) test_loader = DataLoader(test_dataset, batch_sampler=test_sampler, collate_fn=lambda x: batch_feature_normalize( @@ -96,76 +203,68 @@ def main(args, config): model.eval() # stage6: global embedding norm to imporve the performance + # and we create the InputNormalization instance to process the embedding mean and std norm logger.info(f"global embedding norm: {config.global_embedding_norm}") if config.global_embedding_norm: - global_embedding_mean = None - global_embedding_std = None - mean_norm_flag = config.embedding_mean_norm - std_norm_flag = config.embedding_std_norm - batch_count = 0 + mean_var_norm_emb = InputNormalization( + norm_type="global", + mean_norm=config.embedding_mean_norm, + std_norm=config.embedding_std_norm) + + # stage 7: score norm need the imposters dataset + # we select the train dataset as the idea imposters dataset + # and we select the config.n_train_snts utterance to as the final imposters dataset + if "score_norm" in config: + logger.info(f"we will do score norm: {config.score_norm}") + train_dataset = CSVDataset( + os.path.join(args.data_dir, "vox/csv/train.csv"), + feat_type='melspectrogram', + n_train_snts=config.n_train_snts, + random_chunk=False, + n_mels=config.n_mels, + window_size=config.window_size, + hop_length=config.hop_size) + train_sampler = BatchSampler( + train_dataset, batch_size=config.batch_size, shuffle=False) + train_loader = DataLoader(train_dataset, + batch_sampler=train_sampler, + collate_fn=lambda x: batch_feature_normalize( + x, mean_norm=True, std_norm=False), + num_workers=config.num_workers, + return_list=True,) - # stage7: Compute embeddings of audios in enrol and test dataset from model. + # stage 8: Compute embeddings of audios in enrol and test dataset from model. id2embedding = {} # Run multi times to make embedding normalization more stable. - for i in range(2): - for dl in [enrol_loader, test_loader]: - logger.info( - f'Loop {[i+1]}: Computing embeddings on {dl.dataset.subset} dataset' - ) - with paddle.no_grad(): - for batch_idx, batch in enumerate(tqdm(dl)): - - # stage 8-1: extrac the audio embedding - ids, feats, lengths = batch['ids'], batch['feats'], batch[ - 'lengths'] - embeddings = model.backbone(feats, lengths).squeeze( - -1).numpy() # (N, emb_size, 1) -> (N, emb_size) - - # Global embedding normalization. - # if we use the global embedding norm - # eer can reduece about relative 10% - if config.global_embedding_norm: - batch_count += 1 - current_mean = embeddings.mean( - axis=0) if mean_norm_flag else 0 - current_std = embeddings.std( - axis=0) if std_norm_flag else 1 - # Update global mean and std. - if global_embedding_mean is None and global_embedding_std is None: - global_embedding_mean, global_embedding_std = current_mean, current_std - else: - weight = 1 / batch_count # Weight decay by batches. - global_embedding_mean = ( - 1 - weight - ) * global_embedding_mean + weight * current_mean - global_embedding_std = ( - 1 - weight - ) * global_embedding_std + weight * current_std - # Apply global embedding normalization. - embeddings = (embeddings - global_embedding_mean - ) / global_embedding_std - - # Update embedding dict. - id2embedding.update(dict(zip(ids, embeddings))) - - # stage 8: Compute cosine scores. - labels = [] - enroll_ids = [] - test_ids = [] - logger.info(f"read the trial from {VoxCeleb.veri_test_file}") - with open(VoxCeleb.veri_test_file, 'r') as f: - for line in f.readlines(): - label, enroll_id, test_id = line.strip().split(' ') - labels.append(int(label)) - enroll_ids.append(enroll_id.split('.')[0].replace('/', '-')) - test_ids.append(test_id.split('.')[0].replace('/', '-')) - - cos_sim_func = paddle.nn.CosineSimilarity(axis=1) - enrol_embeddings, test_embeddings = map(lambda ids: paddle.to_tensor( - np.asarray([id2embedding[uttid] for uttid in ids], dtype='float32')), - [enroll_ids, test_ids - ]) # (N, emb_size) - scores = cos_sim_func(enrol_embeddings, test_embeddings) + logger.info("First loop for enroll and test dataset") + compute_dataset_embedding(enroll_loader, model, mean_var_norm_emb, config, + id2embedding) + compute_dataset_embedding(test_loader, model, mean_var_norm_emb, config, + id2embedding) + + logger.info("Second loop for enroll and test dataset") + compute_dataset_embedding(enroll_loader, model, mean_var_norm_emb, config, + id2embedding) + compute_dataset_embedding(test_loader, model, mean_var_norm_emb, config, + id2embedding) + mean_var_norm_emb.save( + os.path.join(args.load_checkpoint, "mean_var_norm_emb")) + + # stage 9: Compute cosine scores. + train_cohort = None + if "score_norm" in config: + train_embeddings = {} + # cohort embedding not do mean and std norm + compute_dataset_embedding(train_loader, model, None, config, + train_embeddings) + train_cohort = paddle.stack(list(train_embeddings.values())) + + # stage 10: compute the scores + scores, labels = compute_verification_scores(id2embedding, train_cohort, + config) + + # stage 11: compute the EER and threshold + scores = paddle.to_tensor(scores) EER, threshold = compute_eer(np.asarray(labels), scores.numpy()) logger.info( f'EER of verification test: {EER*100:.4f}%, score threshold: {threshold:.5f}' diff --git a/paddlespeech/vector/exps/ecapa_tdnn/train.py b/paddlespeech/vector/exps/ecapa_tdnn/train.py index 257b97abe..8855689d5 100644 --- a/paddlespeech/vector/exps/ecapa_tdnn/train.py +++ b/paddlespeech/vector/exps/ecapa_tdnn/train.py @@ -23,13 +23,13 @@ from paddle.io import DistributedBatchSampler from yacs.config import CfgNode from paddleaudio.compliance.librosa import melspectrogram -from paddleaudio.datasets.voxceleb import VoxCeleb from paddlespeech.s2t.utils.log import Log from paddlespeech.vector.io.augment import build_augment_pipeline from paddlespeech.vector.io.augment import waveform_augment from paddlespeech.vector.io.batch import batch_pad_right from paddlespeech.vector.io.batch import feature_normalize from paddlespeech.vector.io.batch import waveform_collate_fn +from paddlespeech.vector.io.dataset import CSVDataset from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn from paddlespeech.vector.modules.loss import AdditiveAngularMargin from paddlespeech.vector.modules.loss import LogSoftmaxWrapper @@ -42,6 +42,12 @@ logger = Log(__name__).getlog() def main(args, config): + """The main process for test the speaker verification model + + Args: + args (argparse.Namespace): the command line args namespace + config (yacs.config.CfgNode): the yaml config + """ # stage0: set the training device, cpu or gpu paddle.set_device(args.device) @@ -49,37 +55,45 @@ def main(args, config): paddle.distributed.init_parallel_env() nranks = paddle.distributed.get_world_size() local_rank = paddle.distributed.get_rank() - # set the random seed, it is a must for multiprocess training + # set the random seed, it is the necessary measures for multiprocess training seed_everything(config.seed) # stage2: data prepare, such vox1 and vox2 data, and augment noise data and pipline - # note: some cmd must do in rank==0, so wo will refactor the data prepare code - train_dataset = VoxCeleb('train', target_dir=args.data_dir) - dev_dataset = VoxCeleb('dev', target_dir=args.data_dir) - + # note: some operations must be done in rank==0 + train_dataset = CSVDataset( + csv_path=os.path.join(args.data_dir, "vox/csv/train.csv"), + label2id_path=os.path.join(args.data_dir, "vox/meta/label2id.txt")) + dev_dataset = CSVDataset( + csv_path=os.path.join(args.data_dir, "vox/csv/dev.csv"), + label2id_path=os.path.join(args.data_dir, "vox/meta/label2id.txt")) + + # we will build the augment pipeline process list if config.augment: augment_pipeline = build_augment_pipeline(target_dir=args.data_dir) else: augment_pipeline = [] # stage3: build the dnn backbone model network + # in speaker verification period, we use the backbone mode to extract the audio embedding ecapa_tdnn = EcapaTdnn(**config.model) # stage4: build the speaker verification train instance with backbone model model = SpeakerIdetification( - backbone=ecapa_tdnn, num_class=VoxCeleb.num_speakers) + backbone=ecapa_tdnn, num_class=config.num_speakers) # stage5: build the optimizer, we now only construct the AdamW optimizer # 140000 is single gpu steps # so, in multi-gpu mode, wo reduce the step_size to 140000//nranks to enable CyclicLRScheduler lr_schedule = CyclicLRScheduler( - base_lr=config.learning_rate, max_lr=1e-3, step_size=140000 // nranks) + base_lr=config.learning_rate, + max_lr=config.max_lr, + step_size=config.step_size // nranks) optimizer = paddle.optimizer.AdamW( learning_rate=lr_schedule, parameters=model.parameters()) # stage6: build the loss function, we now only support LogSoftmaxWrapper criterion = LogSoftmaxWrapper( - loss_fn=AdditiveAngularMargin(margin=0.2, scale=30)) + loss_fn=AdditiveAngularMargin(margin=config.margin, scale=config.scale)) # stage7: confirm training start epoch # if pre-trained model exists, start epoch confirmed by the pre-trained model @@ -193,15 +207,15 @@ def main(args, config): paddle.optimizer.lr.LRScheduler): optimizer._learning_rate.step() optimizer.clear_grad() - train_run_cost += time.time() - train_start # stage 9-8: Calculate average loss per batch - avg_loss += loss.numpy()[0] + avg_loss = loss.item() # stage 9-9: Calculate metrics, which is one-best accuracy preds = paddle.argmax(logits, axis=1) num_corrects += (preds == labels).numpy().sum() num_samples += feats.shape[0] + train_run_cost += time.time() - train_start timer.count() # step plus one in timer # stage 9-10: print the log information only on 0-rank per log-freq batchs @@ -220,8 +234,9 @@ def main(args, config): train_feat_cost / config.log_interval) print_msg += ' avg_train_cost: {:.5f} sec,'.format( train_run_cost / config.log_interval) - print_msg += ' lr={:.4E} step/sec={:.2f} | ETA {}'.format( - lr, timer.timing, timer.eta) + + print_msg += ' lr={:.4E} step/sec={:.2f} ips={:.5f}| ETA {}'.format( + lr, timer.timing, timer.ips, timer.eta) logger.info(print_msg) avg_loss = 0 diff --git a/paddlespeech/vector/io/augment.py b/paddlespeech/vector/io/augment.py index 3baace139..0aa89c6a3 100644 --- a/paddlespeech/vector/io/augment.py +++ b/paddlespeech/vector/io/augment.py @@ -14,6 +14,7 @@ # this is modified from SpeechBrain # https://github.com/speechbrain/speechbrain/blob/085be635c07f16d42cd1295045bc46c407f1e15b/speechbrain/lobes/augment.py import math +import os from typing import List import numpy as np @@ -21,8 +22,8 @@ import paddle import paddle.nn as nn import paddle.nn.functional as F -from paddleaudio.datasets.rirs_noises import OpenRIRNoise from paddlespeech.s2t.utils.log import Log +from paddlespeech.vector.io.dataset import CSVDataset from paddlespeech.vector.io.signal_processing import compute_amplitude from paddlespeech.vector.io.signal_processing import convolve1d from paddlespeech.vector.io.signal_processing import dB_to_amplitude @@ -509,7 +510,7 @@ class AddNoise(nn.Layer): assert w >= 0, f'Target length {target_length} is less than origin length {x.shape[0]}' return np.pad(x, [0, w], mode=mode, **kwargs) - ids = [item['id'] for item in batch] + ids = [item['utt_id'] for item in batch] lengths = np.asarray([item['feat'].shape[0] for item in batch]) waveforms = list( map(lambda x: pad(x, max(max_length, lengths.max().item())), @@ -589,7 +590,7 @@ class AddReverb(nn.Layer): assert w >= 0, f'Target length {target_length} is less than origin length {x.shape[0]}' return np.pad(x, [0, w], mode=mode, **kwargs) - ids = [item['id'] for item in batch] + ids = [item['utt_id'] for item in batch] lengths = np.asarray([item['feat'].shape[0] for item in batch]) waveforms = list( map(lambda x: pad(x, lengths.max().item()), @@ -839,8 +840,10 @@ def build_augment_pipeline(target_dir=None) -> List[paddle.nn.Layer]: List[paddle.nn.Layer]: all augment process """ logger.info("start to build the augment pipeline") - noise_dataset = OpenRIRNoise('noise', target_dir=target_dir) - rir_dataset = OpenRIRNoise('rir', target_dir=target_dir) + noise_dataset = CSVDataset(csv_path=os.path.join(target_dir, + "rir_noise/csv/noise.csv")) + rir_dataset = CSVDataset(csv_path=os.path.join(target_dir, + "rir_noise/csv/rir.csv")) wavedrop = TimeDomainSpecAugment( sample_rate=16000, diff --git a/paddlespeech/vector/io/batch.py b/paddlespeech/vector/io/batch.py index 92ca990cf..5049d1946 100644 --- a/paddlespeech/vector/io/batch.py +++ b/paddlespeech/vector/io/batch.py @@ -17,6 +17,17 @@ import paddle def waveform_collate_fn(batch): + """Wrap the waveform into a batch form + + Args: + batch (list): the waveform list from the dataloader + the item of data include several field + feat: the utterance waveform data + label: the utterance label encoding data + + Returns: + dict: the batch data to dataloader + """ waveforms = np.stack([item['feat'] for item in batch]) labels = np.stack([item['label'] for item in batch]) @@ -27,6 +38,18 @@ def feature_normalize(feats: paddle.Tensor, mean_norm: bool=True, std_norm: bool=True, convert_to_numpy: bool=False): + """Do one utterance feature normalization + + Args: + feats (paddle.Tensor): the original utterance feat, such as fbank, mfcc + mean_norm (bool, optional): mean norm flag. Defaults to True. + std_norm (bool, optional): std norm flag. Defaults to True. + convert_to_numpy (bool, optional): convert the paddle.tensor to numpy + and do feature norm with numpy. Defaults to False. + + Returns: + paddle.Tensor : the normalized feats + """ # Features normalization if needed # numpy.mean is a little with paddle.mean about 1e-6 if convert_to_numpy: @@ -60,7 +83,17 @@ def pad_right_2d(x, target_length, axis=-1, mode='constant', **kwargs): def batch_feature_normalize(batch, mean_norm: bool=True, std_norm: bool=True): - ids = [item['id'] for item in batch] + """Do batch utterance features normalization + + Args: + batch (list): the batch feature from dataloader + mean_norm (bool, optional): mean normalization flag. Defaults to True. + std_norm (bool, optional): std normalization flag. Defaults to True. + + Returns: + dict: the normalized batch features + """ + ids = [item['utt_id'] for item in batch] lengths = np.asarray([item['feat'].shape[1] for item in batch]) feats = list( map(lambda x: pad_right_2d(x, lengths.max()), diff --git a/paddlespeech/vector/io/dataset.py b/paddlespeech/vector/io/dataset.py new file mode 100644 index 000000000..0a1b2ba5c --- /dev/null +++ b/paddlespeech/vector/io/dataset.py @@ -0,0 +1,193 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from dataclasses import fields + +from paddle.io import Dataset + +from paddleaudio import load as load_audio +from paddleaudio.compliance.librosa import melspectrogram +from paddlespeech.s2t.utils.log import Log +logger = Log(__name__).getlog() + +# the audio meta info in the vector CSVDataset +# utt_id: the utterance segment name +# duration: utterance segment time +# wav: utterance file path +# start: start point in the original wav file +# stop: stop point in the original wav file +# label: the utterance segment's label id + + +@dataclass +class meta_info: + """the audio meta info in the vector CSVDataset + + Args: + utt_id (str): the utterance segment name + duration (float): utterance segment time + wav (str): utterance file path + start (int): start point in the original wav file + stop (int): stop point in the original wav file + lab_id (str): the utterance segment's label id + """ + utt_id: str + duration: float + wav: str + start: int + stop: int + label: str + + +# csv dataset support feature type +# raw: return the pcm data sample point +# melspectrogram: fbank feature +feat_funcs = { + 'raw': None, + 'melspectrogram': melspectrogram, +} + + +class CSVDataset(Dataset): + def __init__(self, + csv_path, + label2id_path=None, + config=None, + random_chunk=True, + feat_type: str="raw", + n_train_snts: int=-1, + **kwargs): + """Implement the CSV Dataset + + Args: + csv_path (str): csv dataset file path + label2id_path (str): the utterance label to integer id map file path + config (CfgNode): yaml config + feat_type (str): dataset feature type. if it is raw, it return pcm data. + n_train_snts (int): select the n_train_snts sample from the dataset. + if n_train_snts = -1, dataset will load all the sample. + Default value is -1. + kwargs : feature type args + """ + super().__init__() + self.csv_path = csv_path + self.label2id_path = label2id_path + self.config = config + self.random_chunk = random_chunk + self.feat_type = feat_type + self.n_train_snts = n_train_snts + self.feat_config = kwargs + self.id2label = {} + self.label2id = {} + self.data = self.load_data_csv() + self.load_speaker_to_label() + + def load_data_csv(self): + """Load the csv dataset content and store them in the data property + the csv dataset's format has six fields, + that is audio_id or utt_id, audio duration, segment start point, segment stop point + and utterance label. + Note in training period, the utterance label must has a map to integer id in label2id_path + + Returns: + list: the csv data with meta_info type + """ + data = [] + + with open(self.csv_path, 'r') as rf: + for line in rf.readlines()[1:]: + audio_id, duration, wav, start, stop, spk_id = line.strip( + ).split(',') + data.append( + meta_info(audio_id, + float(duration), wav, + int(start), int(stop), spk_id)) + if self.n_train_snts > 0: + sample_num = min(self.n_train_snts, len(data)) + data = data[0:sample_num] + + return data + + def load_speaker_to_label(self): + """Load the utterance label map content. + In vector domain, we call the utterance label as speaker label. + The speaker label is real speaker label in speaker verification domain, + and in language identification is language label. + """ + if not self.label2id_path: + logger.warning("No speaker id to label file") + return + + with open(self.label2id_path, 'r') as f: + for line in f.readlines(): + label_name, label_id = line.strip().split(' ') + self.label2id[label_name] = int(label_id) + self.id2label[int(label_id)] = label_name + + def convert_to_record(self, idx: int): + """convert the dataset sample to training record the CSV Dataset + + Args: + idx (int) : the request index in all the dataset + """ + sample = self.data[idx] + + record = {} + # To show all fields in a namedtuple: `type(sample)._fields` + for field in fields(sample): + record[field.name] = getattr(sample, field.name) + + waveform, sr = load_audio(record['wav']) + + # random select a chunk audio samples from the audio + if self.config and self.config.random_chunk: + num_wav_samples = waveform.shape[0] + num_chunk_samples = int(self.config.chunk_duration * sr) + start = random.randint(0, num_wav_samples - num_chunk_samples - 1) + stop = start + num_chunk_samples + else: + start = record['start'] + stop = record['stop'] + + # we only return the waveform as feat + waveform = waveform[start:stop] + + # all availabel feature type is in feat_funcs + assert self.feat_type in feat_funcs.keys(), \ + f"Unknown feat_type: {self.feat_type}, it must be one in {list(feat_funcs.keys())}" + feat_func = feat_funcs[self.feat_type] + feat = feat_func( + waveform, sr=sr, **self.feat_config) if feat_func else waveform + + record.update({'feat': feat}) + if self.label2id: + record.update({'label': self.label2id[record['label']]}) + + return record + + def __getitem__(self, idx): + """Return the specific index sample + + Args: + idx (int) : the request index in all the dataset + """ + return self.convert_to_record(idx) + + def __len__(self): + """Return the dataset length + + Returns: + int: the length num of the dataset + """ + return len(self.data) diff --git a/paddlespeech/vector/io/dataset_from_json.py b/paddlespeech/vector/io/dataset_from_json.py new file mode 100644 index 000000000..a4d8c4524 --- /dev/null +++ b/paddlespeech/vector/io/dataset_from_json.py @@ -0,0 +1,116 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import json +from dataclasses import dataclass +from dataclasses import fields + +from paddle.io import Dataset + +from paddleaudio import load as load_audio +from paddleaudio.compliance.librosa import melspectrogram +from paddleaudio.compliance.librosa import mfcc + + +@dataclass +class meta_info: + """the audio meta info in the vector JSONDataset + Args: + utt_id (str): the segment name + duration (float): segment time + wav (str): wav file path + start (int): start point in the original wav file + stop (int): stop point in the original wav file + lab_id (str): the record id + """ + utt_id: str + duration: float + wav: str + start: int + stop: int + record_id: str + + +# json dataset support feature type +feat_funcs = { + 'raw': None, + 'melspectrogram': melspectrogram, + 'mfcc': mfcc, +} + + +class JSONDataset(Dataset): + """ + dataset from json file. + """ + + def __init__(self, json_file: str, feat_type: str='raw', **kwargs): + """ + Ags: + json_file (:obj:`str`): Data prep JSON file. + labels (:obj:`List[int]`): Labels of audio files. + feat_type (:obj:`str`, `optional`, defaults to `raw`): + It identifies the feature type that user wants to extrace of an audio file. + """ + if feat_type not in feat_funcs.keys(): + raise RuntimeError( + f"Unknown feat_type: {feat_type}, it must be one in {list(feat_funcs.keys())}" + ) + + self.json_file = json_file + self.feat_type = feat_type + self.feat_config = kwargs + self._data = self._get_data() + super(JSONDataset, self).__init__() + + def _get_data(self): + with open(self.json_file, "r") as f: + meta_data = json.load(f) + data = [] + for key in meta_data: + sub_seg = meta_data[key]["wav"] + wav = sub_seg["file"] + duration = sub_seg["duration"] + start = sub_seg["start"] + stop = sub_seg["stop"] + rec_id = str(key).rsplit("_", 2)[0] + data.append( + meta_info( + str(key), + float(duration), wav, int(start), int(stop), str(rec_id))) + return data + + def _convert_to_record(self, idx: int): + sample = self._data[idx] + + record = {} + # To show all fields in a namedtuple + for field in fields(sample): + record[field.name] = getattr(sample, field.name) + + waveform, sr = load_audio(record['wav']) + waveform = waveform[record['start']:record['stop']] + + feat_func = feat_funcs[self.feat_type] + feat = feat_func( + waveform, sr=sr, **self.feat_config) if feat_func else waveform + + record.update({'feat': feat}) + + return record + + def __getitem__(self, idx): + return self._convert_to_record(idx) + + def __len__(self): + return len(self._data) diff --git a/paddlespeech/vector/io/embedding_norm.py b/paddlespeech/vector/io/embedding_norm.py new file mode 100644 index 000000000..429bb1af1 --- /dev/null +++ b/paddlespeech/vector/io/embedding_norm.py @@ -0,0 +1,217 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Dict + +import paddle + + +class InputNormalization: + spk_dict_mean: Dict[int, paddle.Tensor] + spk_dict_std: Dict[int, paddle.Tensor] + spk_dict_count: Dict[int, int] + + def __init__( + self, + mean_norm=True, + std_norm=True, + norm_type="global", ): + """Do feature or embedding mean and std norm + + Args: + mean_norm (bool, optional): mean norm flag. Defaults to True. + std_norm (bool, optional): std norm flag. Defaults to True. + norm_type (str, optional): norm type. Defaults to "global". + """ + super().__init__() + self.training = True + self.mean_norm = mean_norm + self.std_norm = std_norm + self.norm_type = norm_type + self.glob_mean = paddle.to_tensor([0], dtype="float32") + self.glob_std = paddle.to_tensor([0], dtype="float32") + self.spk_dict_mean = {} + self.spk_dict_std = {} + self.spk_dict_count = {} + self.weight = 1.0 + self.count = 0 + self.eps = 1e-10 + + def __call__(self, + x, + lengths, + spk_ids=paddle.to_tensor([], dtype="float32")): + """Returns the tensor with the surrounding context. + Args: + x (paddle.Tensor): A batch of tensors. + lengths (paddle.Tensor): A batch of tensors containing the relative length of each + sentence (e.g, [0.7, 0.9, 1.0]). It is used to avoid + computing stats on zero-padded steps. + spk_ids (paddle.Tensor, optional): tensor containing the ids of each speaker (e.g, [0 10 6]). + It is used to perform per-speaker normalization when + norm_type='speaker'. Defaults to paddle.to_tensor([], dtype="float32"). + Returns: + paddle.Tensor: The normalized feature or embedding + """ + N_batches = x.shape[0] + + current_means = [] + current_stds = [] + + for snt_id in range(N_batches): + + # Avoiding padded time steps + # actual size is the actual time data length + actual_size = paddle.round(lengths[snt_id] * + x.shape[1]).astype("int32") + # computing actual time data statistics + # we extract the snt_id embedding from the x + # and the target paddle.Tensor will reduce an 0-axis + # so we need unsqueeze operation to recover the all axis + current_mean, current_std = self._compute_current_stats( + x[snt_id, 0:actual_size, ...].unsqueeze(0)) + current_means.append(current_mean) + current_stds.append(current_std) + + if self.norm_type == "global": + current_mean = paddle.mean(paddle.stack(current_means), axis=0) + current_std = paddle.mean(paddle.stack(current_stds), axis=0) + + if self.norm_type == "global": + + if self.training: + if self.count == 0: + self.glob_mean = current_mean + self.glob_std = current_std + + else: + self.weight = 1 / (self.count + 1) + + self.glob_mean = ( + 1 - self.weight + ) * self.glob_mean + self.weight * current_mean + + self.glob_std = ( + 1 - self.weight + ) * self.glob_std + self.weight * current_std + + self.glob_mean.detach() + self.glob_std.detach() + + self.count = self.count + 1 + x = (x - self.glob_mean) / (self.glob_std) + return x + + def _compute_current_stats(self, x): + """Returns the tensor with the surrounding context. + + Args: + x (paddle.Tensor): A batch of tensors. + + Returns: + the statistics of the data + """ + # Compute current mean + if self.mean_norm: + current_mean = paddle.mean(x, axis=0).detach() + else: + current_mean = paddle.to_tensor([0.0], dtype="float32") + + # Compute current std + if self.std_norm: + current_std = paddle.std(x, axis=0).detach() + else: + current_std = paddle.to_tensor([1.0], dtype="float32") + + # Improving numerical stability of std + current_std = paddle.maximum(current_std, + self.eps * paddle.ones_like(current_std)) + + return current_mean, current_std + + def _statistics_dict(self): + """Fills the dictionary containing the normalization statistics. + """ + state = {} + state["count"] = self.count + state["glob_mean"] = self.glob_mean + state["glob_std"] = self.glob_std + state["spk_dict_mean"] = self.spk_dict_mean + state["spk_dict_std"] = self.spk_dict_std + state["spk_dict_count"] = self.spk_dict_count + + return state + + def _load_statistics_dict(self, state): + """Loads the dictionary containing the statistics. + + Arguments + --------- + state : dict + A dictionary containing the normalization statistics. + """ + self.count = state["count"] + if isinstance(state["glob_mean"], int): + self.glob_mean = state["glob_mean"] + self.glob_std = state["glob_std"] + else: + self.glob_mean = state["glob_mean"] # .to(self.device_inp) + self.glob_std = state["glob_std"] # .to(self.device_inp) + + # Loading the spk_dict_mean in the right device + self.spk_dict_mean = {} + for spk in state["spk_dict_mean"]: + self.spk_dict_mean[spk] = state["spk_dict_mean"][spk] + + # Loading the spk_dict_std in the right device + self.spk_dict_std = {} + for spk in state["spk_dict_std"]: + self.spk_dict_std[spk] = state["spk_dict_std"][spk] + + self.spk_dict_count = state["spk_dict_count"] + + return state + + def to(self, device): + """Puts the needed tensors in the right device. + """ + self = super(InputNormalization, self).to(device) + self.glob_mean = self.glob_mean.to(device) + self.glob_std = self.glob_std.to(device) + for spk in self.spk_dict_mean: + self.spk_dict_mean[spk] = self.spk_dict_mean[spk].to(device) + self.spk_dict_std[spk] = self.spk_dict_std[spk].to(device) + return self + + def save(self, path): + """Save statistic dictionary. + + Args: + path (str): A path where to save the dictionary. + """ + stats = self._statistics_dict() + paddle.save(stats, path) + + def _load(self, path, end_of_epoch=False, device=None): + """Load statistic dictionary. + + Arguments + --------- + path : str + The path of the statistic dictionary + device : str, None + Passed to paddle.load(..., map_location=device) + """ + del end_of_epoch # Unused here. + stats = paddle.load(path, map_location=device) + self._load_statistics_dict(stats) diff --git a/paddlespeech/vector/models/ecapa_tdnn.py b/paddlespeech/vector/models/ecapa_tdnn.py index 0e7287cd3..895ff13f4 100644 --- a/paddlespeech/vector/models/ecapa_tdnn.py +++ b/paddlespeech/vector/models/ecapa_tdnn.py @@ -79,6 +79,20 @@ class Conv1d(nn.Layer): bias_attr=bias, ) def forward(self, x): + """Do conv1d forward + + Args: + x (paddle.Tensor): [N, C, L] input data, + N is the batch, + C is the data dimension, + L is the time + + Raises: + ValueError: only support the same padding type + + Returns: + paddle.Tensor: the value of conv1d + """ if self.padding == "same": x = self._manage_padding(x, self.kernel_size, self.dilation, self.stride) @@ -88,6 +102,20 @@ class Conv1d(nn.Layer): return self.conv(x) def _manage_padding(self, x, kernel_size: int, dilation: int, stride: int): + """Padding the input data + + Args: + x (paddle.Tensor): [N, C, L] input data + N is the batch, + C is the data dimension, + L is the time + kernel_size (int): 1-d convolution kernel size + dilation (int): 1-d convolution dilation + stride (int): 1-d convolution stride + + Returns: + paddle.Tensor: the padded input data + """ L_in = x.shape[-1] # Detecting input shape padding = self._get_padding_elem(L_in, stride, kernel_size, dilation) # Time padding @@ -101,6 +129,17 @@ class Conv1d(nn.Layer): stride: int, kernel_size: int, dilation: int): + """Calculate the padding value in same mode + + Args: + L_in (int): the times of the input data, + stride (int): 1-d convolution stride + kernel_size (int): 1-d convolution kernel size + dilation (int): 1-d convolution stride + + Returns: + int: return the padding value in same mode + """ if stride > 1: n_steps = math.ceil(((L_in - kernel_size * dilation) / stride) + 1) L_out = stride * (n_steps - 1) + kernel_size * dilation @@ -245,6 +284,13 @@ class SEBlock(nn.Layer): class AttentiveStatisticsPooling(nn.Layer): def __init__(self, channels, attention_channels=128, global_context=True): + """Compute the speaker verification statistics + The detail info is section 3.1 in https://arxiv.org/pdf/1709.01507.pdf + Args: + channels (int): input data channel or data dimension + attention_channels (int, optional): attention dimension. Defaults to 128. + global_context (bool, optional): If use the global context information. Defaults to True. + """ super().__init__() self.eps = 1e-12 diff --git a/paddlespeech/vector/modules/layer.py b/paddlespeech/vector/modules/layer.py new file mode 100644 index 000000000..385ca974d --- /dev/null +++ b/paddlespeech/vector/modules/layer.py @@ -0,0 +1,76 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import paddle +import paddle.nn as nn +from paddle.autograd import PyLayer + + +class GradientReversalFunction(PyLayer): + """Gradient Reversal Layer from: + Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015) + + Forward pass is the identity function. In the backward pass, + the upstream gradients are multiplied by -lambda (i.e. gradient is reversed) + """ + + @staticmethod + def forward(ctx, x, lambda_=1): + """Forward in networks + """ + ctx.save_for_backward(lambda_) + return x.clone() + + @staticmethod + def backward(ctx, grads): + """Backward in networks + """ + lambda_, = ctx.saved_tensor() + dx = -lambda_ * grads + return dx + + +class GradientReversalLayer(nn.Layer): + """Gradient Reversal Layer from: + Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015) + + Forward pass is the identity function. In the backward pass, + the upstream gradients are multiplied by -lambda (i.e. gradient is reversed) + """ + + def __init__(self, lambda_=1): + super(GradientReversalLayer, self).__init__() + self.lambda_ = lambda_ + + def forward(self, x): + """Forward in networks + """ + return GradientReversalFunction.apply(x, self.lambda_) + + +if __name__ == "__main__": + paddle.set_device("cpu") + + data = paddle.randn([2, 3], dtype="float64") + data.stop_gradient = False + grl = GradientReversalLayer(1) + out = grl(data) + out.mean().backward() + print(data.grad) + + data = paddle.randn([2, 3], dtype="float64") + data.stop_gradient = False + grl = GradientReversalLayer(-1) + out = grl(data) + out.mean().backward() + print(data.grad) diff --git a/paddlespeech/vector/modules/loss.py b/paddlespeech/vector/modules/loss.py index 1c80dda4f..9a7530c18 100644 --- a/paddlespeech/vector/modules/loss.py +++ b/paddlespeech/vector/modules/loss.py @@ -91,3 +91,199 @@ class LogSoftmaxWrapper(nn.Layer): predictions = F.log_softmax(predictions, axis=1) loss = self.criterion(predictions, targets) / targets.sum() return loss + + +class NCELoss(nn.Layer): + """Noise Contrastive Estimation loss funtion + + Noise Contrastive Estimation (NCE) is an approximation method that is used to + work around the huge computational cost of large softmax layer. + The basic idea is to convert the prediction problem into classification problem + at training stage. It has been proved that these two criterions converges to + the same minimal point as long as noise distribution is close enough to real one. + + NCE bridges the gap between generative models and discriminative models, + rather than simply speedup the softmax layer. + With NCE, you can turn almost anything into posterior with less effort (I think). + + Refs: + NCE：http://www.cs.helsinki.fi/u/ahyvarin/papers/Gutmann10AISTATS.pdf + Thanks: https://github.com/mingen-pan/easy-to-use-NCE-RNN-for-Pytorch/blob/master/nce.py + + Examples: + Q = Q_from_tokens(output_dim) + NCELoss(Q) + """ + + def __init__(self, Q, noise_ratio=100, Z_offset=9.5): + """Noise Contrastive Estimation loss funtion + + Args: + Q (tensor): prior model, uniform or guassian + noise_ratio (int, optional): noise sampling times. Defaults to 100. + Z_offset (float, optional): scale of post processing the score. Defaults to 9.5. + """ + super(NCELoss, self).__init__() + assert type(noise_ratio) is int + self.Q = paddle.to_tensor(Q, stop_gradient=False) + self.N = self.Q.shape[0] + self.K = noise_ratio + self.Z_offset = Z_offset + + def forward(self, output, target): + """Forward inference + + Args: + output (tensor): the model output, which is the input of loss function + """ + output = paddle.reshape(output, [-1, self.N]) + B = output.shape[0] + noise_idx = self.get_noise(B) + idx = self.get_combined_idx(target, noise_idx) + P_target, P_noise = self.get_prob(idx, output, sep_target=True) + Q_target, Q_noise = self.get_Q(idx) + loss = self.nce_loss(P_target, P_noise, Q_noise, Q_target) + return loss.mean() + + def get_Q(self, idx, sep_target=True): + """Get prior model of batchsize data + """ + idx_size = idx.size + prob_model = paddle.to_tensor( + self.Q.numpy()[paddle.reshape(idx, [-1]).numpy()]) + prob_model = paddle.reshape(prob_model, [idx.shape[0], idx.shape[1]]) + if sep_target: + return prob_model[:, 0], prob_model[:, 1:] + else: + return prob_model + + def get_prob(self, idx, scores, sep_target=True): + """Post processing the score of post model(output of nn) of batchsize data + """ + scores = self.get_scores(idx, scores) + scale = paddle.to_tensor([self.Z_offset], dtype='float64') + scores = paddle.add(scores, -scale) + prob = paddle.exp(scores) + if sep_target: + return prob[:, 0], prob[:, 1:] + else: + return prob + + def get_scores(self, idx, scores): + """Get the score of post model(output of nn) of batchsize data + """ + B, N = scores.shape + K = idx.shape[1] + idx_increment = paddle.to_tensor( + N * paddle.reshape(paddle.arange(B), [B, 1]) * paddle.ones([1, K]), + dtype="int64", + stop_gradient=False) + new_idx = idx_increment + idx + new_scores = paddle.index_select( + paddle.reshape(scores, [-1]), paddle.reshape(new_idx, [-1])) + + return paddle.reshape(new_scores, [B, K]) + + def get_noise(self, batch_size, uniform=True): + """Select noise sample + """ + if uniform: + noise = np.random.randint(self.N, size=self.K * batch_size) + else: + noise = np.random.choice( + self.N, self.K * batch_size, replace=True, p=self.Q.data) + noise = paddle.to_tensor(noise, dtype='int64', stop_gradient=False) + noise_idx = paddle.reshape(noise, [batch_size, self.K]) + return noise_idx + + def get_combined_idx(self, target_idx, noise_idx): + """Combined target and noise + """ + target_idx = paddle.reshape(target_idx, [-1, 1]) + return paddle.concat((target_idx, noise_idx), 1) + + def nce_loss(self, prob_model, prob_noise_in_model, prob_noise, + prob_target_in_noise): + """Combined the loss of target and noise + """ + + def safe_log(tensor): + """Safe log + """ + EPSILON = 1e-10 + return paddle.log(EPSILON + tensor) + + model_loss = safe_log(prob_model / + (prob_model + self.K * prob_target_in_noise)) + model_loss = paddle.reshape(model_loss, [-1]) + + noise_loss = paddle.sum( + safe_log((self.K * prob_noise) / + (prob_noise_in_model + self.K * prob_noise)), -1) + noise_loss = paddle.reshape(noise_loss, [-1]) + + loss = -(model_loss + noise_loss) + + return loss + + +class FocalLoss(nn.Layer): + """This criterion is a implemenation of Focal Loss, which is proposed in + Focal Loss for Dense Object Detection. + + Loss(x, class) = - \alpha (1-softmax(x)[class])^gamma \log(softmax(x)[class]) + + The losses are averaged across observations for each minibatch. + + Args: + alpha(1D Tensor, Variable) : the scalar factor for this criterion + gamma(float, double) : gamma > 0; reduces the relative loss for well-classified examples (p > .5), + putting more focus on hard, misclassified examples + size_average(bool): By default, the losses are averaged over observations for each minibatch. + However, if the field size_average is set to False, the losses are + instead summed for each minibatch. + """ + + def __init__(self, alpha=1, gamma=0, size_average=True, ignore_index=-100): + super(FocalLoss, self).__init__() + self.alpha = alpha + self.gamma = gamma + self.size_average = size_average + self.ce = nn.CrossEntropyLoss( + ignore_index=ignore_index, reduction="none") + + def forward(self, outputs, targets): + """Forword inference. + + Args: + outputs: input tensor + target: target label tensor + """ + ce_loss = self.ce(outputs, targets) + pt = paddle.exp(-ce_loss) + focal_loss = self.alpha * (1 - pt)**self.gamma * ce_loss + if self.size_average: + return focal_loss.mean() + else: + return focal_loss.sum() + + +if __name__ == "__main__": + import numpy as np + from paddlespeech.vector.utils.vector_utils import Q_from_tokens + paddle.set_device("cpu") + + input_data = paddle.uniform([5, 100], dtype="float64") + label_data = np.random.randint(0, 100, size=(5)).astype(np.int64) + + input = paddle.to_tensor(input_data) + label = paddle.to_tensor(label_data) + + loss1 = FocalLoss() + loss = loss1.forward(input, label) + print("loss: %.5f" % (loss)) + + Q = Q_from_tokens(100) + loss2 = NCELoss(Q) + loss = loss2.forward(input, label) + print("loss: %.5f" % (loss)) diff --git a/paddlespeech/vector/utils/time.py b/paddlespeech/vector/utils/time.py index 8e85b0e12..9dfbbe1f7 100644 --- a/paddlespeech/vector/utils/time.py +++ b/paddlespeech/vector/utils/time.py @@ -23,6 +23,7 @@ class Timer(object): self.last_start_step = 0 self.current_step = 0 self._is_running = True + self.cur_ips = 0 def start(self): self.last_time = time.time() @@ -43,12 +44,17 @@ class Timer(object): self.last_start_step = self.current_step time_used = time.time() - self.last_time self.last_time = time.time() + self.cur_ips = run_steps / time_used return time_used / run_steps @property def is_running(self) -> bool: return self._is_running + @property + def ips(self) -> float: + return self.cur_ips + @property def eta(self) -> str: if not self.is_running: diff --git a/paddlespeech/vector/utils/vector_utils.py b/paddlespeech/vector/utils/vector_utils.py new file mode 100644 index 000000000..d6659e3f7 --- /dev/null +++ b/paddlespeech/vector/utils/vector_utils.py @@ -0,0 +1,41 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import paddle + + +def get_chunks(seg_dur, audio_id, audio_duration): + """Get all chunk segments from a utterance + + Args: + seg_dur (float): segment chunk duration, seconds + audio_id (str): utterance name, + audio_duration (float): utterance duration, seconds + + Returns: + List: all the chunk segments + """ + num_chunks = int(audio_duration / seg_dur) # all in seconds + chunk_lst = [ + audio_id + "_" + str(i * seg_dur) + "_" + str(i * seg_dur + seg_dur) + for i in range(num_chunks) + ] + return chunk_lst + + +def Q_from_tokens(token_num): + """Get prior model, data from uniform, would support others(guassian) in future + """ + freq = [1] * token_num + Q = paddle.to_tensor(freq, dtype='float64') + return Q / Q.sum() diff --git a/setup.py b/setup.py index 82ff63412..bc466baae 100644 --- a/setup.py +++ b/setup.py @@ -42,6 +42,7 @@ base = [ "loguru", "matplotlib", "nara_wpe", + "onnxruntime", "pandas", "paddleaudio", "paddlenlp", @@ -64,6 +65,7 @@ base = [ "webrtcvad", "yacs~=0.1.8", "prettytable", + "zhon", ] server = [ @@ -90,7 +92,6 @@ requirements = { "unidecode", "yq", "pre-commit", - "zhon", ] } diff --git a/speechx/CMakeLists.txt b/speechx/CMakeLists.txt index f1330d1da..98d9e6374 100644 --- a/speechx/CMakeLists.txt +++ b/speechx/CMakeLists.txt @@ -63,7 +63,8 @@ include(libsndfile) # include(boost) # not work set(boost_SOURCE_DIR ${fc_patch}/boost-src) set(BOOST_ROOT ${boost_SOURCE_DIR}) -# #find_package(boost REQUIRED PATHS ${BOOST_ROOT}) +include_directories(${boost_SOURCE_DIR}) +link_directories(${boost_SOURCE_DIR}/stage/lib) # Eigen include(eigen) @@ -141,4 +142,4 @@ set(DEPS ${DEPS} set(SPEECHX_ROOT ${CMAKE_CURRENT_SOURCE_DIR}/speechx) add_subdirectory(speechx) -add_subdirectory(examples) \ No newline at end of file +add_subdirectory(examples) diff --git a/speechx/examples/CMakeLists.txt b/speechx/examples/CMakeLists.txt index 7f1543c25..3c274a20a 100644 --- a/speechx/examples/CMakeLists.txt +++ b/speechx/examples/CMakeLists.txt @@ -1,7 +1,4 @@ cmake_minimum_required(VERSION 3.14 FATAL_ERROR) -add_subdirectory(feat) -add_subdirectory(nnet) -add_subdirectory(decoder) - -add_subdirectory(glog) \ No newline at end of file +add_subdirectory(ds2_ol) +add_subdirectory(dev) \ No newline at end of file diff --git a/speechx/examples/README.md b/speechx/examples/README.md index 705ca2006..50f5f902f 100644 --- a/speechx/examples/README.md +++ b/speechx/examples/README.md @@ -1,17 +1,37 @@ -# Examples +# Examples for SpeechX -* glog - glog usage -* feat - mfcc, linear -* nnet - ds2 nn -* decoder - online decoder to work as offline +* ds2_ol - ds2 streaming test under `aishell-1` test dataset. + The entrypoint is `ds2_ol/aishell/run.sh` -## How to run + +## How to run `run.sh` is the entry point. -Example to play `decoder`: +Example to play `ds2_ol`: ``` -pushd decoder +pushd ds2_ol/aishell bash run.sh ``` + +## Display Model with [Netron](https://github.com/lutzroeder/netron) + +``` +pip install netron +netron exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel --port 8022 --host 10.21.55.20 +``` + +## For Developer + +> Warning: Only for developer, make sure you know what's it. + +* dev - for speechx developer, using for test. + +## Build WFST + +> Warning: Using below example when you know what's it. + +* text_lm - process text for build lm +* ngram - using to build NGram ARPA lm. +* wfst - build wfst for TLG. diff --git a/speechx/examples/decoder/CMakeLists.txt b/speechx/examples/decoder/CMakeLists.txt deleted file mode 100644 index ded423e94..000000000 --- a/speechx/examples/decoder/CMakeLists.txt +++ /dev/null @@ -1,14 +0,0 @@ -cmake_minimum_required(VERSION 3.14 FATAL_ERROR) - -add_executable(offline_decoder_sliding_chunk_main ${CMAKE_CURRENT_SOURCE_DIR}/offline_decoder_sliding_chunk_main.cc) -target_include_directories(offline_decoder_sliding_chunk_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) -target_link_libraries(offline_decoder_sliding_chunk_main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS}) - -add_executable(offline_decoder_main ${CMAKE_CURRENT_SOURCE_DIR}/offline_decoder_main.cc) -target_include_directories(offline_decoder_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) -target_link_libraries(offline_decoder_main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS}) - -add_executable(decoder_test_main ${CMAKE_CURRENT_SOURCE_DIR}/decoder_test_main.cc) -target_include_directories(decoder_test_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) -target_link_libraries(decoder_test_main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS}) - diff --git a/speechx/examples/decoder/offline_decoder_main.cc b/speechx/examples/decoder/offline_decoder_main.cc deleted file mode 100644 index 9a9c14a0c..000000000 --- a/speechx/examples/decoder/offline_decoder_main.cc +++ /dev/null @@ -1,121 +0,0 @@ -// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -// todo refactor, repalce with gtest - -#include "base/flags.h" -#include "base/log.h" -#include "decoder/ctc_beam_search_decoder.h" -#include "frontend/audio/data_cache.h" -#include "kaldi/util/table-types.h" -#include "nnet/decodable.h" -#include "nnet/paddle_nnet.h" - -DEFINE_string(feature_respecifier, "", "feature matrix rspecifier"); -DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model"); -DEFINE_string(param_path, "avg_1.jit.pdiparams", "paddle nnet model param"); -DEFINE_string(dict_file, "vocab.txt", "vocabulary of lm"); -DEFINE_string(lm_path, "lm.klm", "language model"); -DEFINE_int32(chunk_size, 35, "feat chunk size"); - - -using kaldi::BaseFloat; -using kaldi::Matrix; -using std::vector; - -// test decoder by feeding speech feature, deprecated. -int main(int argc, char* argv[]) { - gflags::ParseCommandLineFlags(&argc, &argv, false); - google::InitGoogleLogging(argv[0]); - - kaldi::SequentialBaseFloatMatrixReader feature_reader( - FLAGS_feature_respecifier); - std::string model_graph = FLAGS_model_path; - std::string model_params = FLAGS_param_path; - std::string dict_file = FLAGS_dict_file; - std::string lm_path = FLAGS_lm_path; - int32 chunk_size = FLAGS_chunk_size; - LOG(INFO) << "model path: " << model_graph; - LOG(INFO) << "model param: " << model_params; - LOG(INFO) << "dict path: " << dict_file; - LOG(INFO) << "lm path: " << lm_path; - LOG(INFO) << "chunk size (frame): " << chunk_size; - - int32 num_done = 0, num_err = 0; - - // frontend + nnet is decodable - ppspeech::ModelOptions model_opts; - model_opts.model_path = model_graph; - model_opts.params_path = model_params; - std::shared_ptr nnet( - new ppspeech::PaddleNnet(model_opts)); - std::shared_ptr raw_data(new ppspeech::DataCache()); - std::shared_ptr decodable( - new ppspeech::Decodable(nnet, raw_data)); - LOG(INFO) << "Init decodeable."; - - // init decoder - ppspeech::CTCBeamSearchOptions opts; - opts.dict_file = dict_file; - opts.lm_path = lm_path; - ppspeech::CTCBeamSearch decoder(opts); - LOG(INFO) << "Init decoder."; - - decoder.InitDecoder(); - for (; !feature_reader.Done(); feature_reader.Next()) { - string utt = feature_reader.Key(); - const kaldi::Matrix feature = feature_reader.Value(); - LOG(INFO) << "utt: " << utt; - - // feat dim - raw_data->SetDim(feature.NumCols()); - LOG(INFO) << "dim: " << raw_data->Dim(); - - int32 row_idx = 0; - int32 num_chunks = feature.NumRows() / chunk_size; - LOG(INFO) << "n chunks: " << num_chunks; - for (int chunk_idx = 0; chunk_idx < num_chunks; ++chunk_idx) { - // feat chunk - kaldi::Vector feature_chunk(chunk_size * - feature.NumCols()); - for (int row_id = 0; row_id < chunk_size; ++row_id) { - kaldi::SubVector feat_one_row(feature, - row_idx); - kaldi::SubVector f_chunk_tmp( - feature_chunk.Data() + row_id * feature.NumCols(), - feature.NumCols()); - f_chunk_tmp.CopyFromVec(feat_one_row); - row_idx++; - } - // feed to raw cache - raw_data->Accept(feature_chunk); - if (chunk_idx == num_chunks - 1) { - raw_data->SetFinished(); - } - // decode step - decoder.AdvanceDecode(decodable); - } - - std::string result; - result = decoder.GetFinalBestPath(); - KALDI_LOG << " the result of " << utt << " is " << result; - decodable->Reset(); - decoder.Reset(); - ++num_done; - } - - KALDI_LOG << "Done " << num_done << " utterances, " << num_err - << " with errors."; - return (num_done != 0 ? 0 : 1); -} diff --git a/speechx/examples/decoder/run.sh b/speechx/examples/decoder/run.sh deleted file mode 100755 index ddda89702..000000000 --- a/speechx/examples/decoder/run.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -set +x -set -e - -. path.sh - -# 1. compile -if [ ! -d ${SPEECHX_EXAMPLES} ]; then - pushd ${SPEECHX_ROOT} - bash build.sh - popd -fi - - -# 2. download model -if [ ! -d ../paddle_asr_model ]; then - wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/paddle_asr_model.tar.gz - tar xzfv paddle_asr_model.tar.gz - mv ./paddle_asr_model ../ - # produce wav scp - echo "utt1 " $PWD/../paddle_asr_model/BAC009S0764W0290.wav > ../paddle_asr_model/wav.scp -fi - -model_dir=../paddle_asr_model -feat_wspecifier=./feats.ark -cmvn=./cmvn.ark - - -export GLOG_logtostderr=1 - -# 3. gen linear feat -linear_spectrogram_main \ - --wav_rspecifier=scp:$model_dir/wav.scp \ - --feature_wspecifier=ark,t:$feat_wspecifier \ - --cmvn_write_path=$cmvn - -# 4. run decoder -offline_decoder_main \ - --feature_respecifier=ark:$feat_wspecifier \ - --model_path=$model_dir/avg_1.jit.pdmodel \ - --param_path=$model_dir/avg_1.jit.pdparams \ - --dict_file=$model_dir/vocab.txt \ - --lm_path=$model_dir/avg_1.jit.klm diff --git a/speechx/examples/dev/CMakeLists.txt b/speechx/examples/dev/CMakeLists.txt new file mode 100644 index 000000000..c8445fb82 --- /dev/null +++ b/speechx/examples/dev/CMakeLists.txt @@ -0,0 +1,3 @@ +cmake_minimum_required(VERSION 3.14 FATAL_ERROR) + +add_subdirectory(glog) diff --git a/speechx/examples/glog/CMakeLists.txt b/speechx/examples/dev/glog/CMakeLists.txt similarity index 100% rename from speechx/examples/glog/CMakeLists.txt rename to speechx/examples/dev/glog/CMakeLists.txt diff --git a/speechx/examples/glog/README.md b/speechx/examples/dev/glog/README.md similarity index 100% rename from speechx/examples/glog/README.md rename to speechx/examples/dev/glog/README.md diff --git a/speechx/examples/glog/glog_logtostderr_test.cc b/speechx/examples/dev/glog/glog_logtostderr_test.cc similarity index 100% rename from speechx/examples/glog/glog_logtostderr_test.cc rename to speechx/examples/dev/glog/glog_logtostderr_test.cc diff --git a/speechx/examples/glog/glog_test.cc b/speechx/examples/dev/glog/glog_test.cc similarity index 100% rename from speechx/examples/glog/glog_test.cc rename to speechx/examples/dev/glog/glog_test.cc diff --git a/speechx/examples/glog/path.sh b/speechx/examples/dev/glog/path.sh similarity index 85% rename from speechx/examples/glog/path.sh rename to speechx/examples/dev/glog/path.sh index e2c7b2fcf..1a96a861a 100644 --- a/speechx/examples/glog/path.sh +++ b/speechx/examples/dev/glog/path.sh @@ -1,14 +1,15 @@ # This contains the locations of binarys build required for running the examples. -SPEECHX_ROOT=$PWD/../.. -SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples +SPEECHX_ROOT=$PWD/../../../ SPEECHX_TOOLS=$SPEECHX_ROOT/tools TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin -[ -d $SPEECHX_EXAMPLES ] || { echo "Error: 'build/examples' directory not found. please ensure that the project build successfully"; } -export LC_AL=C +SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples +[ -d $SPEECHX_EXAMPLES ] || { echo "Error: 'build/examples' directory not found. please ensure that the project build successfully"; } -SPEECHX_BIN=$SPEECHX_EXAMPLES/glog +SPEECHX_BIN=$SPEECHX_EXAMPLES/dev/glog export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN + +export LC_AL=C diff --git a/speechx/examples/glog/run.sh b/speechx/examples/dev/glog/run.sh similarity index 100% rename from speechx/examples/glog/run.sh rename to speechx/examples/dev/glog/run.sh diff --git a/speechx/examples/ds2_ol/CMakeLists.txt b/speechx/examples/ds2_ol/CMakeLists.txt new file mode 100644 index 000000000..08c194846 --- /dev/null +++ b/speechx/examples/ds2_ol/CMakeLists.txt @@ -0,0 +1,6 @@ +cmake_minimum_required(VERSION 3.14 FATAL_ERROR) + +add_subdirectory(feat) +add_subdirectory(nnet) +add_subdirectory(decoder) +add_subdirectory(websocket) diff --git a/speechx/examples/ds2_ol/README.md b/speechx/examples/ds2_ol/README.md new file mode 100644 index 000000000..3fabd3e7a --- /dev/null +++ b/speechx/examples/ds2_ol/README.md @@ -0,0 +1,11 @@ +# Deepspeech2 Streaming + +Please go to `aishell` to test it. + +* aishell +Deepspeech2 Streaming Decoding under aishell dataset. + +The below is for developing and offline testing: +* nnet +* feat +* decoder diff --git a/speechx/examples/ds2_ol/aishell/.gitignore b/speechx/examples/ds2_ol/aishell/.gitignore new file mode 100644 index 000000000..68f993b47 --- /dev/null +++ b/speechx/examples/ds2_ol/aishell/.gitignore @@ -0,0 +1,3 @@ +data +exp +aishell_* diff --git a/speechx/examples/ds2_ol/aishell/README.md b/speechx/examples/ds2_ol/aishell/README.md new file mode 100644 index 000000000..f4a815162 --- /dev/null +++ b/speechx/examples/ds2_ol/aishell/README.md @@ -0,0 +1,27 @@ +# Aishell - Deepspeech2 Streaming + +## CTC Prefix Beam Search w/o LM + +``` +Overall -> 16.14 % N=104612 C=88190 S=16110 D=312 I=465 +Mandarin -> 16.14 % N=104612 C=88190 S=16110 D=312 I=465 +Other -> 0.00 % N=0 C=0 S=0 D=0 I=0 +``` + +## CTC Prefix Beam Search w LM + +LM: zh_giga.no_cna_cmn.prune01244.klm +``` +Overall -> 7.86 % N=104768 C=96865 S=7573 D=330 I=327 +Mandarin -> 7.86 % N=104768 C=96865 S=7573 D=330 I=327 +Other -> 0.00 % N=0 C=0 S=0 D=0 I=0 +``` + +## CTC WFST + +LM: aishell train +``` +Overall -> 11.14 % N=103017 C=93363 S=9583 D=71 I=1819 +Mandarin -> 11.14 % N=103017 C=93363 S=9583 D=71 I=1818 +Other -> 0.00 % N=0 C=0 S=0 D=0 I=1 +``` diff --git a/speechx/examples/ds2_ol/aishell/local/split_data.sh b/speechx/examples/ds2_ol/aishell/local/split_data.sh new file mode 100755 index 000000000..2af6fc5ab --- /dev/null +++ b/speechx/examples/ds2_ol/aishell/local/split_data.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash + +set -eo pipefail + +data=$1 +scp=$2 +split_name=$3 +numsplit=$4 + +# save in $data/split{n} +# $scp to split +# + +if [[ ! $numsplit -gt 0 ]]; then + echo "Invalid num-split argument"; + exit 1; +fi + +directories=$(for n in `seq $numsplit`; do echo $data/split${numsplit}/$n; done) +scp_splits=$(for n in `seq $numsplit`; do echo $data/split${numsplit}/$n/${split_name}; done) + +# if this mkdir fails due to argument-list being too long, iterate. +if ! mkdir -p $directories >&/dev/null; then + for n in `seq $numsplit`; do + mkdir -p $data/split${numsplit}/$n + done +fi + +echo "utils/split_scp.pl $scp $scp_splits" +utils/split_scp.pl $scp $scp_splits diff --git a/speechx/examples/ds2_ol/aishell/path.sh b/speechx/examples/ds2_ol/aishell/path.sh new file mode 100644 index 000000000..520129eaf --- /dev/null +++ b/speechx/examples/ds2_ol/aishell/path.sh @@ -0,0 +1,14 @@ +# This contains the locations of binarys build required for running the examples. + +SPEECHX_ROOT=$PWD/../../.. +SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples + +SPEECHX_TOOLS=$SPEECHX_ROOT/tools +TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin + +[ -d $SPEECHX_EXAMPLES ] || { echo "Error: 'build/examples' directory not found. please ensure that the project build successfully"; } + +export LC_AL=C + +SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/decoder:$SPEECHX_EXAMPLES/ds2_ol/feat:$SPEECHX_EXAMPLES/ds2_ol/websocket +export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN diff --git a/speechx/examples/ds2_ol/aishell/run.sh b/speechx/examples/ds2_ol/aishell/run.sh new file mode 100755 index 000000000..779123d5a --- /dev/null +++ b/speechx/examples/ds2_ol/aishell/run.sh @@ -0,0 +1,141 @@ +#!/bin/bash +set +x +set -e + +. path.sh + +nj=40 +stage=0 +stop_stage=100 + +. utils/parse_options.sh + +# 1. compile +if [ ! -d ${SPEECHX_EXAMPLES} ]; then + pushd ${SPEECHX_ROOT} + bash build.sh + popd +fi + +# input +mkdir -p data +data=$PWD/data + +ckpt_dir=$data/model +model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/ +vocb_dir=$ckpt_dir/data/lang_char/ + +# output +mkdir -p exp +exp=$PWD/exp + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ];then + aishell_wav_scp=aishell_test.scp + if [ ! -d $data/test ]; then + pushd $data + wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip + unzip aishell_test.zip + popd + + realpath $data/test/*/*.wav > $data/wavlist + awk -F '/' '{ print $(NF) }' $data/wavlist | awk -F '.' '{ print $1 }' > $data/utt_id + paste $data/utt_id $data/wavlist > $data/$aishell_wav_scp + fi + + if [ ! -f $ckpt_dir/data/mean_std.json ]; then + mkdir -p $ckpt_dir + pushd $ckpt_dir + wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz + tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz + popd + fi + + lm=$data/zh_giga.no_cna_cmn.prune01244.klm + if [ ! -f $lm ]; then + pushd $data + wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm + popd + fi +fi + +# 3. make feature +text=$data/test/text +label_file=./aishell_result +wer=./aishell_wer + +export GLOG_logtostderr=1 + + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + # 3. gen linear feat + cmvn=$data/cmvn.ark + cmvn-json2kaldi --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn + + ./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj + + utils/run.pl JOB=1:$nj $data/split${nj}/JOB/feat.log \ + linear-spectrogram-wo-db-norm-ol \ + --wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \ + --feature_wspecifier=ark,scp:$data/split${nj}/JOB/feat.ark,$data/split${nj}/JOB/feat.scp \ + --cmvn_file=$cmvn \ + --streaming_chunk=0.36 +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + # recognizer + utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wolm.log \ + ctc-prefix-beam-search-decoder-ol \ + --feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \ + --model_path=$model_dir/avg_1.jit.pdmodel \ + --params_path=$model_dir/avg_1.jit.pdiparams \ + --model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \ + --dict_file=$vocb_dir/vocab.txt \ + --result_wspecifier=ark,t:$data/split${nj}/JOB/result + + cat $data/split${nj}/*/result > $exp/${label_file} + utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file} > $exp/${wer} +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + # decode with lm + utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.lm.log \ + ctc-prefix-beam-search-decoder-ol \ + --feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \ + --model_path=$model_dir/avg_1.jit.pdmodel \ + --params_path=$model_dir/avg_1.jit.pdiparams \ + --model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \ + --dict_file=$vocb_dir/vocab.txt \ + --lm_path=$lm \ + --result_wspecifier=ark,t:$data/split${nj}/JOB/result_lm + + cat $data/split${nj}/*/result_lm > $exp/${label_file}_lm + utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file}_lm > $exp/${wer}.lm +fi + +if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then + wfst=$data/wfst/ + mkdir -p $wfst + if [ ! -f $wfst/aishell_graph.zip ]; then + pushd $wfst + wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph.zip + unzip aishell_graph.zip + popd + fi + + graph_dir=$wfst/aishell_graph + + # TLG decoder + utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wfst.log \ + wfst-decoder-ol \ + --feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \ + --model_path=$model_dir/avg_1.jit.pdmodel \ + --params_path=$model_dir/avg_1.jit.pdiparams \ + --word_symbol_table=$graph_dir/words.txt \ + --model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \ + --graph_path=$graph_dir/TLG.fst --max_active=7500 \ + --acoustic_scale=1.2 \ + --result_wspecifier=ark,t:$data/split${nj}/JOB/result_tlg + + cat $data/split${nj}/*/result_tlg > $exp/${label_file}_tlg + utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file}_tlg > $exp/${wer}.tlg +fi diff --git a/speechx/examples/ds2_ol/aishell/utils b/speechx/examples/ds2_ol/aishell/utils new file mode 120000 index 000000000..c2519a9dd --- /dev/null +++ b/speechx/examples/ds2_ol/aishell/utils @@ -0,0 +1 @@ +../../../../utils/ \ No newline at end of file diff --git a/speechx/examples/ds2_ol/aishell/websocket_client.sh b/speechx/examples/ds2_ol/aishell/websocket_client.sh new file mode 100644 index 000000000..3c6b4e911 --- /dev/null +++ b/speechx/examples/ds2_ol/aishell/websocket_client.sh @@ -0,0 +1,37 @@ +#!/bin/bash +set +x +set -e + +. path.sh + +# 1. compile +if [ ! -d ${SPEECHX_EXAMPLES} ]; then + pushd ${SPEECHX_ROOT} + bash build.sh + popd +fi + +# input +mkdir -p data +data=$PWD/data +ckpt_dir=$data/model +model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/ +vocb_dir=$ckpt_dir/data/lang_char +# output +aishell_wav_scp=aishell_test.scp +if [ ! -d $data/test ]; then + pushd $data + wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip + unzip aishell_test.zip + popd + + realpath $data/test/*/*.wav > $data/wavlist + awk -F '/' '{ print $(NF) }' $data/wavlist | awk -F '.' '{ print $1 }' > $data/utt_id + paste $data/utt_id $data/wavlist > $data/$aishell_wav_scp +fi + +export GLOG_logtostderr=1 + +# websocket client +websocket_client_main \ + --wav_rspecifier=scp:$data/$aishell_wav_scp --streaming_chunk=0.36 diff --git a/speechx/examples/ds2_ol/aishell/websocket_server.sh b/speechx/examples/ds2_ol/aishell/websocket_server.sh new file mode 100644 index 000000000..ea619d544 --- /dev/null +++ b/speechx/examples/ds2_ol/aishell/websocket_server.sh @@ -0,0 +1,66 @@ +#!/bin/bash +set +x +set -e + +. path.sh + + +# 1. compile +if [ ! -d ${SPEECHX_EXAMPLES} ]; then + pushd ${SPEECHX_ROOT} + bash build.sh + popd +fi + +# input +mkdir -p data +data=$PWD/data +ckpt_dir=$data/model +model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/ +vocb_dir=$ckpt_dir/data/lang_char/ + +# output +aishell_wav_scp=aishell_test.scp +if [ ! -d $data/test ]; then + pushd $data + wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip + unzip aishell_test.zip + popd + + realpath $data/test/*/*.wav > $data/wavlist + awk -F '/' '{ print $(NF) }' $data/wavlist | awk -F '.' '{ print $1 }' > $data/utt_id + paste $data/utt_id $data/wavlist > $data/$aishell_wav_scp +fi + + +if [ ! -d $ckpt_dir ]; then + mkdir -p $ckpt_dir + wget -P $ckpt_dir -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz + tar xzfv $ckpt_dir/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz -C $ckpt_dir +fi + + +export GLOG_logtostderr=1 + +# 3. gen cmvn +cmvn=$PWD/cmvn.ark +cmvn-json2kaldi --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn + +text=$data/test/text +graph_dir=./aishell_graph +if [ ! -d $graph_dir ]; then + wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph.zip + unzip aishell_graph.zip +fi + +# 5. test websocket server +websocket_server_main \ + --cmvn_file=$cmvn \ + --model_path=$model_dir/avg_1.jit.pdmodel \ + --streaming_chunk=0.1 \ + --convert2PCM32=true \ + --params_path=$model_dir/avg_1.jit.pdiparams \ + --word_symbol_table=$graph_dir/words.txt \ + --model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \ + --graph_path=$graph_dir/TLG.fst --max_active=7500 \ + --acoustic_scale=1.2 diff --git a/speechx/examples/ds2_ol/decoder/.gitignore b/speechx/examples/ds2_ol/decoder/.gitignore new file mode 100644 index 000000000..bbd86a25b --- /dev/null +++ b/speechx/examples/ds2_ol/decoder/.gitignore @@ -0,0 +1,2 @@ +data +exp diff --git a/speechx/examples/ds2_ol/decoder/CMakeLists.txt b/speechx/examples/ds2_ol/decoder/CMakeLists.txt new file mode 100644 index 000000000..62dd6862e --- /dev/null +++ b/speechx/examples/ds2_ol/decoder/CMakeLists.txt @@ -0,0 +1,22 @@ +cmake_minimum_required(VERSION 3.14 FATAL_ERROR) + +set(bin_name ctc-prefix-beam-search-decoder-ol) +add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc) +target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) +target_link_libraries(${bin_name} PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS}) + + +set(bin_name wfst-decoder-ol) +add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc) +target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) +target_link_libraries(${bin_name} PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder ${DEPS}) + + +set(bin_name nnet-logprob-decoder-test) +add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc) +target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) +target_link_libraries(${bin_name} PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS}) + +add_executable(recognizer_test_main ${CMAKE_CURRENT_SOURCE_DIR}/recognizer_test_main.cc) +target_include_directories(recognizer_test_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) +target_link_libraries(recognizer_test_main PUBLIC frontend kaldi-feat-common nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder ${DEPS}) diff --git a/speechx/examples/ds2_ol/decoder/README.md b/speechx/examples/ds2_ol/decoder/README.md new file mode 100644 index 000000000..ead3b8e13 --- /dev/null +++ b/speechx/examples/ds2_ol/decoder/README.md @@ -0,0 +1,12 @@ +# ASR Decoder + +ASR Decoder test bins. We using theses bins to test CTC BeamSearch decoder and WFST decoder. + +* decoder_test_main.cc +feed nnet output logprob, and only test decoder + +* offline_decoder_sliding_chunk_main.cc +feed streaming audio feature, decode as streaming manner. + +* offline_wfst_decoder_main.cc +feed streaming audio feature, decode using WFST as streaming manner. diff --git a/speechx/examples/decoder/offline_decoder_sliding_chunk_main.cc b/speechx/examples/ds2_ol/decoder/ctc-prefix-beam-search-decoder-ol.cc similarity index 81% rename from speechx/examples/decoder/offline_decoder_sliding_chunk_main.cc rename to speechx/examples/ds2_ol/decoder/ctc-prefix-beam-search-decoder-ol.cc index 7f6c572ca..e145f6ee1 100644 --- a/speechx/examples/decoder/offline_decoder_sliding_chunk_main.cc +++ b/speechx/examples/ds2_ol/decoder/ctc-prefix-beam-search-decoder-ol.cc @@ -22,40 +22,49 @@ #include "nnet/decodable.h" #include "nnet/paddle_nnet.h" -DEFINE_string(feature_respecifier, "", "test feature rspecifier"); +DEFINE_string(feature_rspecifier, "", "test feature rspecifier"); +DEFINE_string(result_wspecifier, "", "test result wspecifier"); DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model"); DEFINE_string(param_path, "avg_1.jit.pdiparams", "paddle nnet model param"); DEFINE_string(dict_file, "vocab.txt", "vocabulary of lm"); -DEFINE_string(lm_path, "lm.klm", "language model"); +DEFINE_string(lm_path, "", "language model"); DEFINE_int32(receptive_field_length, 7, "receptive field of two CNN(kernel=5) downsampling module."); DEFINE_int32(downsampling_rate, 4, "two CNN(kernel=5) module downsampling rate."); +DEFINE_string(model_output_names, + "save_infer_model/scale_0.tmp_1,save_infer_model/" + "scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/" + "scale_3.tmp_1", + "model output names"); +DEFINE_string(model_cache_names, "5-1-1024,5-1-1024", "model cache names"); using kaldi::BaseFloat; using kaldi::Matrix; using std::vector; - // test ds2 online decoder by feeding speech feature int main(int argc, char* argv[]) { gflags::ParseCommandLineFlags(&argc, &argv, false); google::InitGoogleLogging(argv[0]); + CHECK(FLAGS_result_wspecifier != ""); + CHECK(FLAGS_feature_rspecifier != ""); + kaldi::SequentialBaseFloatMatrixReader feature_reader( - FLAGS_feature_respecifier); - std::string model_graph = FLAGS_model_path; + FLAGS_feature_rspecifier); + kaldi::TokenWriter result_writer(FLAGS_result_wspecifier); + std::string model_path = FLAGS_model_path; std::string model_params = FLAGS_param_path; std::string dict_file = FLAGS_dict_file; std::string lm_path = FLAGS_lm_path; - LOG(INFO) << "model path: " << model_graph; + LOG(INFO) << "model path: " << model_path; LOG(INFO) << "model param: " << model_params; LOG(INFO) << "dict path: " << dict_file; LOG(INFO) << "lm path: " << lm_path; - int32 num_done = 0, num_err = 0; ppspeech::CTCBeamSearchOptions opts; @@ -64,9 +73,10 @@ int main(int argc, char* argv[]) { ppspeech::CTCBeamSearch decoder(opts); ppspeech::ModelOptions model_opts; - model_opts.model_path = model_graph; + model_opts.model_path = model_path; model_opts.params_path = model_params; - model_opts.cache_shape = "5-1-1024,5-1-1024"; + model_opts.cache_shape = FLAGS_model_cache_names; + model_opts.output_names = FLAGS_model_output_names; std::shared_ptr nnet( new ppspeech::PaddleNnet(model_opts)); std::shared_ptr raw_data(new ppspeech::DataCache()); @@ -111,7 +121,6 @@ int main(int argc, char* argv[]) { if (feature_chunk_size < receptive_field_length) break; int32 start = chunk_idx * chunk_stride; - int32 end = start + chunk_size; for (int row_id = 0; row_id < chunk_size; ++row_id) { kaldi::SubVector tmp(feature, start); @@ -129,9 +138,16 @@ int main(int argc, char* argv[]) { } std::string result; result = decoder.GetFinalBestPath(); - KALDI_LOG << " the result of " << utt << " is " << result; decodable->Reset(); decoder.Reset(); + if (result.empty()) { + // the TokenWriter can not write empty string. + ++num_err; + KALDI_LOG << " the result of " << utt << " is empty"; + continue; + } + KALDI_LOG << " the result of " << utt << " is " << result; + result_writer.Write(utt, result); ++num_done; } diff --git a/speechx/examples/decoder/local/model.sh b/speechx/examples/ds2_ol/decoder/local/model.sh similarity index 100% rename from speechx/examples/decoder/local/model.sh rename to speechx/examples/ds2_ol/decoder/local/model.sh diff --git a/speechx/examples/decoder/decoder_test_main.cc b/speechx/examples/ds2_ol/decoder/nnet-logprob-decoder-test.cc similarity index 100% rename from speechx/examples/decoder/decoder_test_main.cc rename to speechx/examples/ds2_ol/decoder/nnet-logprob-decoder-test.cc diff --git a/speechx/examples/decoder/path.sh b/speechx/examples/ds2_ol/decoder/path.sh similarity index 79% rename from speechx/examples/decoder/path.sh rename to speechx/examples/ds2_ol/decoder/path.sh index a0e7c9aed..8e26e6e7e 100644 --- a/speechx/examples/decoder/path.sh +++ b/speechx/examples/ds2_ol/decoder/path.sh @@ -1,6 +1,6 @@ # This contains the locations of binarys build required for running the examples. -SPEECHX_ROOT=$PWD/../.. +SPEECHX_ROOT=$PWD/../../../ SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples SPEECHX_TOOLS=$SPEECHX_ROOT/tools @@ -10,5 +10,5 @@ TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin export LC_AL=C -SPEECHX_BIN=$SPEECHX_EXAMPLES/decoder:$SPEECHX_EXAMPLES/feat +SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/decoder:$SPEECHX_EXAMPLES/ds2_ol/feat export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN diff --git a/speechx/examples/ds2_ol/decoder/recognizer_test_main.cc b/speechx/examples/ds2_ol/decoder/recognizer_test_main.cc new file mode 100644 index 000000000..198a8ec2c --- /dev/null +++ b/speechx/examples/ds2_ol/decoder/recognizer_test_main.cc @@ -0,0 +1,85 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "decoder/recognizer.h" +#include "decoder/param.h" +#include "kaldi/feat/wave-reader.h" +#include "kaldi/util/table-types.h" + +DEFINE_string(wav_rspecifier, "", "test feature rspecifier"); +DEFINE_string(result_wspecifier, "", "test result wspecifier"); + +int main(int argc, char* argv[]) { + gflags::ParseCommandLineFlags(&argc, &argv, false); + google::InitGoogleLogging(argv[0]); + + ppspeech::RecognizerResource resource = ppspeech::InitRecognizerResoure(); + ppspeech::Recognizer recognizer(resource); + + kaldi::SequentialTableReader wav_reader( + FLAGS_wav_rspecifier); + kaldi::TokenWriter result_writer(FLAGS_result_wspecifier); + int sample_rate = 16000; + float streaming_chunk = FLAGS_streaming_chunk; + int chunk_sample_size = streaming_chunk * sample_rate; + LOG(INFO) << "sr: " << sample_rate; + LOG(INFO) << "chunk size (s): " << streaming_chunk; + LOG(INFO) << "chunk size (sample): " << chunk_sample_size; + + int32 num_done = 0, num_err = 0; + + for (; !wav_reader.Done(); wav_reader.Next()) { + std::string utt = wav_reader.Key(); + const kaldi::WaveData& wave_data = wav_reader.Value(); + + int32 this_channel = 0; + kaldi::SubVector waveform(wave_data.Data(), + this_channel); + int tot_samples = waveform.Dim(); + LOG(INFO) << "wav len (sample): " << tot_samples; + + int sample_offset = 0; + std::vector> feats; + int feature_rows = 0; + while (sample_offset < tot_samples) { + int cur_chunk_size = + std::min(chunk_sample_size, tot_samples - sample_offset); + + kaldi::Vector wav_chunk(cur_chunk_size); + for (int i = 0; i < cur_chunk_size; ++i) { + wav_chunk(i) = waveform(sample_offset + i); + } + + recognizer.Accept(wav_chunk); + if (cur_chunk_size < chunk_sample_size) { + recognizer.SetFinished(); + } + recognizer.Decode(); + + sample_offset += cur_chunk_size; + } + std::string result; + result = recognizer.GetFinalResult(); + recognizer.Reset(); + if (result.empty()) { + // the TokenWriter can not write empty string. + ++num_err; + KALDI_LOG << " the result of " << utt << " is empty"; + continue; + } + KALDI_LOG << " the result of " << utt << " is " << result; + result_writer.Write(utt, result); + ++num_done; + } +} \ No newline at end of file diff --git a/speechx/examples/ds2_ol/decoder/run.sh b/speechx/examples/ds2_ol/decoder/run.sh new file mode 100755 index 000000000..de76e9796 --- /dev/null +++ b/speechx/examples/ds2_ol/decoder/run.sh @@ -0,0 +1,79 @@ +#!/bin/bash +set +x +set -e + +. path.sh + +# 1. compile +if [ ! -d ${SPEECHX_EXAMPLES} ]; then + pushd ${SPEECHX_ROOT} + bash build.sh + popd +fi + +# input +mkdir -p data +data=$PWD/data +ckpt_dir=$data/model +model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/ +vocb_dir=$ckpt_dir/data/lang_char/ + +lm=$data/zh_giga.no_cna_cmn.prune01244.klm + +# output +exp_dir=./exp +mkdir -p $exp_dir + +# 2. download model +if [[ ! -f data/model/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz ]]; then + mkdir -p data/model + pushd data/model + wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz + tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz + popd +fi + +# produce wav scp +if [ ! -f data/wav.scp ]; then + pushd data + wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav + echo "utt1 " $PWD/zh.wav > wav.scp + popd +fi + +# download lm +if [ ! -f $lm ]; then + pushd data + wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm + popd +fi + + +feat_wspecifier=$exp_dir/feats.ark +cmvn=$exp_dir/cmvn.ark + +export GLOG_logtostderr=1 + +# dump json cmvn to kaldi +cmvn-json2kaldi \ + --json_file $ckpt_dir/data/mean_std.json \ + --cmvn_write_path $exp_dir/cmvn.ark \ + --binary=false +echo "convert json cmvn to kaldi ark." + + +# generate linear feature as streaming +linear-spectrogram-wo-db-norm-ol \ + --wav_rspecifier=scp:$data/wav.scp \ + --feature_wspecifier=ark,t:$feat_wspecifier \ + --cmvn_file=$exp_dir/cmvn.ark +echo "compute linear spectrogram feature." + +# run ctc beam search decoder as streaming +ctc-prefix-beam-search-decoder-ol \ + --result_wspecifier=ark,t:$exp_dir/result.txt \ + --feature_rspecifier=ark:$feat_wspecifier \ + --model_path=$model_dir/avg_1.jit.pdmodel \ + --param_path=$model_dir/avg_1.jit.pdiparams \ + --dict_file=$vocb_dir/vocab.txt \ + --lm_path=$lm \ No newline at end of file diff --git a/speechx/examples/decoder/valgrind.sh b/speechx/examples/ds2_ol/decoder/valgrind.sh similarity index 100% rename from speechx/examples/decoder/valgrind.sh rename to speechx/examples/ds2_ol/decoder/valgrind.sh diff --git a/speechx/examples/ds2_ol/decoder/wfst-decoder-ol.cc b/speechx/examples/ds2_ol/decoder/wfst-decoder-ol.cc new file mode 100644 index 000000000..dc4030b68 --- /dev/null +++ b/speechx/examples/ds2_ol/decoder/wfst-decoder-ol.cc @@ -0,0 +1,159 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// todo refactor, repalce with gtest + +#include "base/flags.h" +#include "base/log.h" +#include "decoder/ctc_tlg_decoder.h" +#include "frontend/audio/data_cache.h" +#include "kaldi/util/table-types.h" +#include "nnet/decodable.h" +#include "nnet/paddle_nnet.h" + +DEFINE_string(feature_rspecifier, "", "test feature rspecifier"); +DEFINE_string(result_wspecifier, "", "test result wspecifier"); +DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model"); +DEFINE_string(param_path, "avg_1.jit.pdiparams", "paddle nnet model param"); +DEFINE_string(word_symbol_table, "words.txt", "word symbol table"); +DEFINE_string(graph_path, "TLG", "decoder graph"); + +DEFINE_double(acoustic_scale, 1.0, "acoustic scale"); +DEFINE_int32(max_active, 7500, "decoder graph"); +DEFINE_int32(receptive_field_length, + 7, + "receptive field of two CNN(kernel=5) downsampling module."); +DEFINE_int32(downsampling_rate, + 4, + "two CNN(kernel=5) module downsampling rate."); +DEFINE_string(model_output_names, + "save_infer_model/scale_0.tmp_1,save_infer_model/" + "scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/" + "scale_3.tmp_1", + "model output names"); +DEFINE_string(model_cache_names, "5-1-1024,5-1-1024", "model cache names"); + +using kaldi::BaseFloat; +using kaldi::Matrix; +using std::vector; + +// test TLG decoder by feeding speech feature. +int main(int argc, char* argv[]) { + gflags::ParseCommandLineFlags(&argc, &argv, false); + google::InitGoogleLogging(argv[0]); + + kaldi::SequentialBaseFloatMatrixReader feature_reader( + FLAGS_feature_rspecifier); + kaldi::TokenWriter result_writer(FLAGS_result_wspecifier); + std::string model_graph = FLAGS_model_path; + std::string model_params = FLAGS_param_path; + std::string word_symbol_table = FLAGS_word_symbol_table; + std::string graph_path = FLAGS_graph_path; + LOG(INFO) << "model path: " << model_graph; + LOG(INFO) << "model param: " << model_params; + LOG(INFO) << "word symbol path: " << word_symbol_table; + LOG(INFO) << "graph path: " << graph_path; + + int32 num_done = 0, num_err = 0; + + ppspeech::TLGDecoderOptions opts; + opts.word_symbol_table = word_symbol_table; + opts.fst_path = graph_path; + opts.opts.max_active = FLAGS_max_active; + opts.opts.beam = 15.0; + opts.opts.lattice_beam = 7.5; + ppspeech::TLGDecoder decoder(opts); + + ppspeech::ModelOptions model_opts; + model_opts.model_path = model_graph; + model_opts.params_path = model_params; + model_opts.cache_shape = FLAGS_model_cache_names; + model_opts.output_names = FLAGS_model_output_names; + std::shared_ptr nnet( + new ppspeech::PaddleNnet(model_opts)); + std::shared_ptr raw_data(new ppspeech::DataCache()); + std::shared_ptr decodable( + new ppspeech::Decodable(nnet, raw_data, FLAGS_acoustic_scale)); + + int32 chunk_size = FLAGS_receptive_field_length; + int32 chunk_stride = FLAGS_downsampling_rate; + int32 receptive_field_length = FLAGS_receptive_field_length; + LOG(INFO) << "chunk size (frame): " << chunk_size; + LOG(INFO) << "chunk stride (frame): " << chunk_stride; + LOG(INFO) << "receptive field (frame): " << receptive_field_length; + decoder.InitDecoder(); + + for (; !feature_reader.Done(); feature_reader.Next()) { + string utt = feature_reader.Key(); + kaldi::Matrix feature = feature_reader.Value(); + raw_data->SetDim(feature.NumCols()); + LOG(INFO) << "process utt: " << utt; + LOG(INFO) << "rows: " << feature.NumRows(); + LOG(INFO) << "cols: " << feature.NumCols(); + + int32 row_idx = 0; + int32 padding_len = 0; + int32 ori_feature_len = feature.NumRows(); + if ((feature.NumRows() - chunk_size) % chunk_stride != 0) { + padding_len = + chunk_stride - (feature.NumRows() - chunk_size) % chunk_stride; + feature.Resize(feature.NumRows() + padding_len, + feature.NumCols(), + kaldi::kCopyData); + } + int32 num_chunks = (feature.NumRows() - chunk_size) / chunk_stride + 1; + for (int chunk_idx = 0; chunk_idx < num_chunks; ++chunk_idx) { + kaldi::Vector feature_chunk(chunk_size * + feature.NumCols()); + int32 feature_chunk_size = 0; + if (ori_feature_len > chunk_idx * chunk_stride) { + feature_chunk_size = std::min( + ori_feature_len - chunk_idx * chunk_stride, chunk_size); + } + if (feature_chunk_size < receptive_field_length) break; + + int32 start = chunk_idx * chunk_stride; + for (int row_id = 0; row_id < chunk_size; ++row_id) { + kaldi::SubVector tmp(feature, start); + kaldi::SubVector f_chunk_tmp( + feature_chunk.Data() + row_id * feature.NumCols(), + feature.NumCols()); + f_chunk_tmp.CopyFromVec(tmp); + ++start; + } + raw_data->Accept(feature_chunk); + if (chunk_idx == num_chunks - 1) { + raw_data->SetFinished(); + } + decoder.AdvanceDecode(decodable); + } + std::string result; + result = decoder.GetFinalBestPath(); + decodable->Reset(); + decoder.Reset(); + if (result.empty()) { + // the TokenWriter can not write empty string. + ++num_err; + KALDI_LOG << " the result of " << utt << " is empty"; + continue; + } + KALDI_LOG << " the result of " << utt << " is " << result; + result_writer.Write(utt, result); + ++num_done; + } + + KALDI_LOG << "Done " << num_done << " utterances, " << num_err + << " with errors."; + return (num_done != 0 ? 0 : 1); +} diff --git a/speechx/examples/ds2_ol/feat/.gitignore b/speechx/examples/ds2_ol/feat/.gitignore new file mode 100644 index 000000000..566f2d97b --- /dev/null +++ b/speechx/examples/ds2_ol/feat/.gitignore @@ -0,0 +1,2 @@ +exp +data diff --git a/speechx/examples/ds2_ol/feat/CMakeLists.txt b/speechx/examples/ds2_ol/feat/CMakeLists.txt new file mode 100644 index 000000000..90cb9c8e2 --- /dev/null +++ b/speechx/examples/ds2_ol/feat/CMakeLists.txt @@ -0,0 +1,12 @@ +cmake_minimum_required(VERSION 3.14 FATAL_ERROR) + +set(bin_name linear-spectrogram-wo-db-norm-ol) +add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc) +target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) +target_link_libraries(${bin_name} frontend kaldi-util kaldi-feat-common gflags glog) + + +set(bin_name cmvn-json2kaldi) +add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc) +target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) +target_link_libraries(${bin_name} utils kaldi-util kaldi-matrix gflags glog) diff --git a/speechx/examples/ds2_ol/feat/README.md b/speechx/examples/ds2_ol/feat/README.md new file mode 100644 index 000000000..89cb79eca --- /dev/null +++ b/speechx/examples/ds2_ol/feat/README.md @@ -0,0 +1,7 @@ +# Deepspeech2 Straming Audio Feature + +ASR audio feature test bins. We using theses bins to test linaer/fbank/mfcc asr feature as streaming manner. + +* linear_spectrogram_without_db_norm_main.cc + +compute linear spectrogram w/o db norm in streaming manner. diff --git a/speechx/examples/ds2_ol/feat/cmvn-json2kaldi.cc b/speechx/examples/ds2_ol/feat/cmvn-json2kaldi.cc new file mode 100644 index 000000000..0a9cfb06c --- /dev/null +++ b/speechx/examples/ds2_ol/feat/cmvn-json2kaldi.cc @@ -0,0 +1,81 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// Note: Do not print/log ondemand object. + +#include "base/flags.h" +#include "base/log.h" +#include "kaldi/matrix/kaldi-matrix.h" +#include "kaldi/util/kaldi-io.h" +#include "utils/file_utils.h" +#include "utils/simdjson.h" + +DEFINE_string(json_file, "", "cmvn json file"); +DEFINE_string(cmvn_write_path, "./cmvn.ark", "write cmvn"); +DEFINE_bool(binary, true, "write cmvn in binary (true) or text(false)"); + +using namespace simdjson; + +int main(int argc, char* argv[]) { + gflags::ParseCommandLineFlags(&argc, &argv, false); + google::InitGoogleLogging(argv[0]); + + LOG(INFO) << "cmvn josn path: " << FLAGS_json_file; + + try { + padded_string json = padded_string::load(FLAGS_json_file); + + ondemand::parser parser; + ondemand::document doc = parser.iterate(json); + ondemand::value val = doc; + + ondemand::array mean_stat = val["mean_stat"]; + std::vector mean_stat_vec; + for (double x : mean_stat) { + mean_stat_vec.push_back(x); + } + // LOG(INFO) << mean_stat; this line will casue + // simdjson::simdjson_error("Objects and arrays can only be iterated + // when + // they are first encountered") + + ondemand::array var_stat = val["var_stat"]; + std::vector var_stat_vec; + for (double x : var_stat) { + var_stat_vec.push_back(x); + } + + kaldi::int32 frame_num = uint64_t(val["frame_num"]); + LOG(INFO) << "nframe: " << frame_num; + + size_t mean_size = mean_stat_vec.size(); + kaldi::Matrix cmvn_stats(2, mean_size + 1); + for (size_t idx = 0; idx < mean_size; ++idx) { + cmvn_stats(0, idx) = mean_stat_vec[idx]; + cmvn_stats(1, idx) = var_stat_vec[idx]; + } + cmvn_stats(0, mean_size) = frame_num; + LOG(INFO) << cmvn_stats; + + kaldi::WriteKaldiObject( + cmvn_stats, FLAGS_cmvn_write_path, FLAGS_binary); + LOG(INFO) << "cmvn stats have write into: " << FLAGS_cmvn_write_path; + LOG(INFO) << "Binary: " << FLAGS_binary; + } catch (simdjson::simdjson_error& err) { + LOG(ERROR) << err.what(); + } + + + return 0; +} diff --git a/speechx/examples/ds2_ol/feat/linear-spectrogram-wo-db-norm-ol.cc b/speechx/examples/ds2_ol/feat/linear-spectrogram-wo-db-norm-ol.cc new file mode 100644 index 000000000..0d10bd306 --- /dev/null +++ b/speechx/examples/ds2_ol/feat/linear-spectrogram-wo-db-norm-ol.cc @@ -0,0 +1,140 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// todo refactor, repalce with gtest + +#include "base/flags.h" +#include "base/log.h" +#include "kaldi/feat/wave-reader.h" +#include "kaldi/util/kaldi-io.h" +#include "kaldi/util/table-types.h" + +#include "frontend/audio/audio_cache.h" +#include "frontend/audio/data_cache.h" +#include "frontend/audio/feature_cache.h" +#include "frontend/audio/frontend_itf.h" +#include "frontend/audio/linear_spectrogram.h" +#include "frontend/audio/normalizer.h" + +DEFINE_string(wav_rspecifier, "", "test wav scp path"); +DEFINE_string(feature_wspecifier, "", "output feats wspecifier"); +DEFINE_string(cmvn_file, "./cmvn.ark", "read cmvn"); +DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size"); + +int main(int argc, char* argv[]) { + gflags::ParseCommandLineFlags(&argc, &argv, false); + google::InitGoogleLogging(argv[0]); + + kaldi::SequentialTableReader wav_reader( + FLAGS_wav_rspecifier); + kaldi::BaseFloatMatrixWriter feat_writer(FLAGS_feature_wspecifier); + + int32 num_done = 0, num_err = 0; + + // feature pipeline: wave cache --> hanning + // window -->linear_spectrogram --> global cmvn -> feat cache + + std::unique_ptr data_source( + new ppspeech::AudioCache(3600 * 1600, true)); + + ppspeech::LinearSpectrogramOptions opt; + opt.frame_opts.frame_length_ms = 20; + opt.frame_opts.frame_shift_ms = 10; + opt.streaming_chunk = FLAGS_streaming_chunk; + opt.frame_opts.dither = 0.0; + opt.frame_opts.remove_dc_offset = false; + opt.frame_opts.window_type = "hanning"; + opt.frame_opts.preemph_coeff = 0.0; + LOG(INFO) << "frame length (ms): " << opt.frame_opts.frame_length_ms; + LOG(INFO) << "frame shift (ms): " << opt.frame_opts.frame_shift_ms; + + std::unique_ptr linear_spectrogram( + new ppspeech::LinearSpectrogram(opt, std::move(data_source))); + + std::unique_ptr cmvn( + new ppspeech::CMVN(FLAGS_cmvn_file, std::move(linear_spectrogram))); + + ppspeech::FeatureCacheOptions feat_cache_opts; + ppspeech::FeatureCache feature_cache(feat_cache_opts, std::move(cmvn)); + LOG(INFO) << "feat dim: " << feature_cache.Dim(); + + int sample_rate = 16000; + float streaming_chunk = FLAGS_streaming_chunk; + int chunk_sample_size = streaming_chunk * sample_rate; + LOG(INFO) << "sr: " << sample_rate; + LOG(INFO) << "chunk size (s): " << streaming_chunk; + LOG(INFO) << "chunk size (sample): " << chunk_sample_size; + + + for (; !wav_reader.Done(); wav_reader.Next()) { + std::string utt = wav_reader.Key(); + const kaldi::WaveData& wave_data = wav_reader.Value(); + LOG(INFO) << "process utt: " << utt; + + int32 this_channel = 0; + kaldi::SubVector waveform(wave_data.Data(), + this_channel); + int tot_samples = waveform.Dim(); + LOG(INFO) << "wav len (sample): " << tot_samples; + + int sample_offset = 0; + std::vector> feats; + int feature_rows = 0; + while (sample_offset < tot_samples) { + int cur_chunk_size = + std::min(chunk_sample_size, tot_samples - sample_offset); + + kaldi::Vector wav_chunk(cur_chunk_size); + for (int i = 0; i < cur_chunk_size; ++i) { + wav_chunk(i) = waveform(sample_offset + i); + } + + kaldi::Vector features; + feature_cache.Accept(wav_chunk); + if (cur_chunk_size < chunk_sample_size) { + feature_cache.SetFinished(); + } + feature_cache.Read(&features); + if (features.Dim() == 0) break; + + feats.push_back(features); + sample_offset += cur_chunk_size; + feature_rows += features.Dim() / feature_cache.Dim(); + } + + int cur_idx = 0; + kaldi::Matrix features(feature_rows, + feature_cache.Dim()); + for (auto feat : feats) { + int num_rows = feat.Dim() / feature_cache.Dim(); + for (int row_idx = 0; row_idx < num_rows; ++row_idx) { + for (size_t col_idx = 0; col_idx < feature_cache.Dim(); + ++col_idx) { + features(cur_idx, col_idx) = + feat(row_idx * feature_cache.Dim() + col_idx); + } + ++cur_idx; + } + } + feat_writer.Write(utt, features); + feature_cache.Reset(); + + if (num_done % 50 == 0 && num_done != 0) + KALDI_VLOG(2) << "Processed " << num_done << " utterances"; + num_done++; + } + KALDI_LOG << "Done " << num_done << " utterances, " << num_err + << " with errors."; + return (num_done != 0 ? 0 : 1); +} diff --git a/speechx/examples/feat/path.sh b/speechx/examples/ds2_ol/feat/path.sh similarity index 85% rename from speechx/examples/feat/path.sh rename to speechx/examples/ds2_ol/feat/path.sh index 8ab7ee299..ad2b6a4e9 100644 --- a/speechx/examples/feat/path.sh +++ b/speechx/examples/ds2_ol/feat/path.sh @@ -1,6 +1,6 @@ # This contains the locations of binarys build required for running the examples. -SPEECHX_ROOT=$PWD/../.. +SPEECHX_ROOT=$PWD/../../../ SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples SPEECHX_TOOLS=$SPEECHX_ROOT/tools @@ -10,5 +10,5 @@ TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin export LC_AL=C -SPEECHX_BIN=$SPEECHX_EXAMPLES/feat +SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/feat export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN diff --git a/speechx/examples/ds2_ol/feat/run.sh b/speechx/examples/ds2_ol/feat/run.sh new file mode 100755 index 000000000..757779275 --- /dev/null +++ b/speechx/examples/ds2_ol/feat/run.sh @@ -0,0 +1,57 @@ +#!/bin/bash +set +x +set -e + +. ./path.sh + +# 1. compile +if [ ! -d ${SPEECHX_EXAMPLES} ]; then + pushd ${SPEECHX_ROOT} + bash build.sh + popd +fi + +# 2. download model +if [ ! -e data/model/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz ]; then + mkdir -p data/model + pushd data/model + wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz + tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz + popd +fi + +# produce wav scp +if [ ! -f data/wav.scp ]; then + mkdir -p data + pushd data + wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav + echo "utt1 " $PWD/zh.wav > wav.scp + popd +fi + + +# input +data_dir=./data +exp_dir=./exp +model_dir=$data_dir/model/ + +mkdir -p $exp_dir + + +# 3. run feat +export GLOG_logtostderr=1 + +cmvn-json2kaldi \ + --json_file $model_dir/data/mean_std.json \ + --cmvn_write_path $exp_dir/cmvn.ark \ + --binary=false +echo "convert json cmvn to kaldi ark." + + +linear-spectrogram-wo-db-norm-ol \ + --wav_rspecifier=scp:$data_dir/wav.scp \ + --feature_wspecifier=ark,t:$exp_dir/feats.ark \ + --cmvn_file=$exp_dir/cmvn.ark +echo "compute linear spectrogram feature." + + diff --git a/speechx/examples/feat/valgrind.sh b/speechx/examples/ds2_ol/feat/valgrind.sh similarity index 100% rename from speechx/examples/feat/valgrind.sh rename to speechx/examples/ds2_ol/feat/valgrind.sh diff --git a/speechx/examples/ds2_ol/nnet/.gitignore b/speechx/examples/ds2_ol/nnet/.gitignore new file mode 100644 index 000000000..bbd86a25b --- /dev/null +++ b/speechx/examples/ds2_ol/nnet/.gitignore @@ -0,0 +1,2 @@ +data +exp diff --git a/speechx/examples/ds2_ol/nnet/CMakeLists.txt b/speechx/examples/ds2_ol/nnet/CMakeLists.txt new file mode 100644 index 000000000..6745a51ae --- /dev/null +++ b/speechx/examples/ds2_ol/nnet/CMakeLists.txt @@ -0,0 +1,6 @@ +cmake_minimum_required(VERSION 3.14 FATAL_ERROR) + +set(bin_name ds2-model-ol-test) +add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc) +target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) +target_link_libraries(${bin_name} PUBLIC nnet gflags glog ${DEPS}) \ No newline at end of file diff --git a/speechx/examples/ds2_ol/nnet/README.md b/speechx/examples/ds2_ol/nnet/README.md new file mode 100644 index 000000000..772a58f0e --- /dev/null +++ b/speechx/examples/ds2_ol/nnet/README.md @@ -0,0 +1,3 @@ +# Deepspeech2 Streaming NNet Test + +Using for ds2 streaming nnet inference test. diff --git a/speechx/examples/nnet/pp-model-test.cc b/speechx/examples/ds2_ol/nnet/ds2-model-ol-test.cc similarity index 90% rename from speechx/examples/nnet/pp-model-test.cc rename to speechx/examples/ds2_ol/nnet/ds2-model-ol-test.cc index 2db354a79..283466dc1 100644 --- a/speechx/examples/nnet/pp-model-test.cc +++ b/speechx/examples/ds2_ol/nnet/ds2-model-ol-test.cc @@ -12,7 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include +// deepspeech2 online model info + #include #include #include @@ -20,21 +21,26 @@ #include #include #include +#include "base/flags.h" +#include "base/log.h" #include "paddle_inference_api.h" using std::cout; using std::endl; -DEFINE_string(model_path, "avg_1.jit.pdmodel", "xxx.pdmodel"); -DEFINE_string(param_path, "avg_1.jit.pdiparams", "xxx.pdiparams"); + +DEFINE_string(model_path, "", "xxx.pdmodel"); +DEFINE_string(param_path, "", "xxx.pdiparams"); +DEFINE_int32(chunk_size, 35, "feature chunk size, unit:frame"); +DEFINE_int32(feat_dim, 161, "feature dim"); void produce_data(std::vector>* data); void model_forward_test(); void produce_data(std::vector>* data) { - int chunk_size = 35; // chunk_size in frame - int col_size = 161; // feat dim + int chunk_size = FLAGS_chunk_size; // chunk_size in frame + int col_size = FLAGS_feat_dim; // feat dim cout << "chunk size: " << chunk_size << endl; cout << "feat dim: " << col_size << endl; @@ -57,6 +63,8 @@ void model_forward_test() { ; std::string model_graph = FLAGS_model_path; std::string model_params = FLAGS_param_path; + CHECK(model_graph != ""); + CHECK(model_params != ""); cout << "model path: " << model_graph << endl; cout << "model param path : " << model_params << endl; @@ -106,7 +114,7 @@ void model_forward_test() { // state_h std::unique_ptr chunk_state_h_box = predictor->GetInputHandle(input_names[2]); - std::vector chunk_state_h_box_shape = {3, 1, 1024}; + std::vector chunk_state_h_box_shape = {5, 1, 1024}; chunk_state_h_box->Reshape(chunk_state_h_box_shape); int chunk_state_h_box_size = std::accumulate(chunk_state_h_box_shape.begin(), @@ -119,7 +127,7 @@ void model_forward_test() { // state_c std::unique_ptr chunk_state_c_box = predictor->GetInputHandle(input_names[3]); - std::vector chunk_state_c_box_shape = {3, 1, 1024}; + std::vector chunk_state_c_box_shape = {5, 1, 1024}; chunk_state_c_box->Reshape(chunk_state_c_box_shape); int chunk_state_c_box_size = std::accumulate(chunk_state_c_box_shape.begin(), @@ -187,7 +195,9 @@ void model_forward_test() { } int main(int argc, char* argv[]) { - gflags::ParseCommandLineFlags(&argc, &argv, true); + gflags::ParseCommandLineFlags(&argc, &argv, false); + google::InitGoogleLogging(argv[0]); + model_forward_test(); return 0; } diff --git a/speechx/examples/nnet/path.sh b/speechx/examples/ds2_ol/nnet/path.sh similarity index 85% rename from speechx/examples/nnet/path.sh rename to speechx/examples/ds2_ol/nnet/path.sh index f70e70eea..0ee8b4787 100644 --- a/speechx/examples/nnet/path.sh +++ b/speechx/examples/ds2_ol/nnet/path.sh @@ -1,6 +1,6 @@ # This contains the locations of binarys build required for running the examples. -SPEECHX_ROOT=$PWD/../.. +SPEECHX_ROOT=$PWD/../../../ SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples SPEECHX_TOOLS=$SPEECHX_ROOT/tools @@ -10,5 +10,5 @@ TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin export LC_AL=C -SPEECHX_BIN=$SPEECHX_EXAMPLES/nnet +SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/nnet export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN diff --git a/speechx/examples/ds2_ol/nnet/run.sh b/speechx/examples/ds2_ol/nnet/run.sh new file mode 100755 index 000000000..10029f7e8 --- /dev/null +++ b/speechx/examples/ds2_ol/nnet/run.sh @@ -0,0 +1,38 @@ +#!/bin/bash +set +x +set -e + +. path.sh + +# 1. compile +if [ ! -d ${SPEECHX_EXAMPLES} ]; then + pushd ${SPEECHX_ROOT} + bash build.sh + popd +fi + +# 2. download model +if [ ! -f data/model/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz ]; then + mkdir -p data/model + pushd data/model + wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz + tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz + popd +fi + +# produce wav scp +if [ ! -f data/wav.scp ]; then + mkdir -p data + pushd data + wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav + echo "utt1 " $PWD/zh.wav > wav.scp + popd +fi + +ckpt_dir=./data/model +model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/ + +ds2-model-ol-test \ + --model_path=$model_dir/avg_1.jit.pdmodel \ + --param_path=$model_dir/avg_1.jit.pdiparams + diff --git a/speechx/examples/nnet/valgrind.sh b/speechx/examples/ds2_ol/nnet/valgrind.sh similarity index 100% rename from speechx/examples/nnet/valgrind.sh rename to speechx/examples/ds2_ol/nnet/valgrind.sh diff --git a/speechx/examples/ds2_ol/websocket/CMakeLists.txt b/speechx/examples/ds2_ol/websocket/CMakeLists.txt new file mode 100644 index 000000000..754b528ef --- /dev/null +++ b/speechx/examples/ds2_ol/websocket/CMakeLists.txt @@ -0,0 +1,10 @@ +cmake_minimum_required(VERSION 3.14 FATAL_ERROR) + +add_executable(websocket_server_main ${CMAKE_CURRENT_SOURCE_DIR}/websocket_server_main.cc) +target_include_directories(websocket_server_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) +target_link_libraries(websocket_server_main PUBLIC frontend kaldi-feat-common nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder websocket ${DEPS}) + +add_executable(websocket_client_main ${CMAKE_CURRENT_SOURCE_DIR}/websocket_client_main.cc) +target_include_directories(websocket_client_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) +target_link_libraries(websocket_client_main PUBLIC frontend kaldi-feat-common nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder websocket ${DEPS}) + diff --git a/speechx/examples/ds2_ol/websocket/websocket_client_main.cc b/speechx/examples/ds2_ol/websocket/websocket_client_main.cc new file mode 100644 index 000000000..68ea898ad --- /dev/null +++ b/speechx/examples/ds2_ol/websocket/websocket_client_main.cc @@ -0,0 +1,82 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "websocket/websocket_client.h" +#include "kaldi/feat/wave-reader.h" +#include "kaldi/util/kaldi-io.h" +#include "kaldi/util/table-types.h" + +DEFINE_string(host, "127.0.0.1", "host of websocket server"); +DEFINE_int32(port, 201314, "port of websocket server"); +DEFINE_string(wav_rspecifier, "", "test wav scp path"); +DEFINE_double(streaming_chunk, 0.1, "streaming feature chunk size"); + +using kaldi::int16; +int main(int argc, char* argv[]) { + gflags::ParseCommandLineFlags(&argc, &argv, false); + google::InitGoogleLogging(argv[0]); + ppspeech::WebSocketClient client(FLAGS_host, FLAGS_port); + + kaldi::SequentialTableReader wav_reader( + FLAGS_wav_rspecifier); + + const int sample_rate = 16000; + const float streaming_chunk = FLAGS_streaming_chunk; + const int chunk_sample_size = streaming_chunk * sample_rate; + + for (; !wav_reader.Done(); wav_reader.Next()) { + client.SendStartSignal(); + std::string utt = wav_reader.Key(); + const kaldi::WaveData& wave_data = wav_reader.Value(); + CHECK_EQ(wave_data.SampFreq(), sample_rate); + + int32 this_channel = 0; + kaldi::SubVector waveform(wave_data.Data(), + this_channel); + const int tot_samples = waveform.Dim(); + int sample_offset = 0; + + while (sample_offset < tot_samples) { + int cur_chunk_size = + std::min(chunk_sample_size, tot_samples - sample_offset); + + std::vector wav_chunk(cur_chunk_size); + for (int i = 0; i < cur_chunk_size; ++i) { + wav_chunk[i] = static_cast(waveform(sample_offset + i)); + } + client.SendBinaryData(wav_chunk.data(), + wav_chunk.size() * sizeof(int16)); + + + sample_offset += cur_chunk_size; + LOG(INFO) << "Send " << cur_chunk_size << " samples"; + std::this_thread::sleep_for( + std::chrono::milliseconds(static_cast(1 * 1000))); + + if (cur_chunk_size < chunk_sample_size) { + client.SendEndSignal(); + } + } + + while (!client.Done()) { + } + std::string result = client.GetResult(); + LOG(INFO) << "utt: " << utt << " " << result; + + + client.Join(); + return 0; + } + return 0; +} diff --git a/speechx/examples/ds2_ol/websocket/websocket_server_main.cc b/speechx/examples/ds2_ol/websocket/websocket_server_main.cc new file mode 100644 index 000000000..43cbd6bbc --- /dev/null +++ b/speechx/examples/ds2_ol/websocket/websocket_server_main.cc @@ -0,0 +1,30 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "websocket/websocket_server.h" +#include "decoder/param.h" + +DEFINE_int32(port, 201314, "websocket listening port"); + +int main(int argc, char *argv[]) { + gflags::ParseCommandLineFlags(&argc, &argv, false); + google::InitGoogleLogging(argv[0]); + + ppspeech::RecognizerResource resource = ppspeech::InitRecognizerResoure(); + + ppspeech::WebSocketServer server(FLAGS_port, resource); + LOG(INFO) << "Listening at port " << FLAGS_port; + server.Start(); + return 0; +} diff --git a/speechx/examples/feat/CMakeLists.txt b/speechx/examples/feat/CMakeLists.txt deleted file mode 100644 index b8f516afb..000000000 --- a/speechx/examples/feat/CMakeLists.txt +++ /dev/null @@ -1,10 +0,0 @@ -cmake_minimum_required(VERSION 3.14 FATAL_ERROR) - - -add_executable(mfcc-test ${CMAKE_CURRENT_SOURCE_DIR}/feature-mfcc-test.cc) -target_include_directories(mfcc-test PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) -target_link_libraries(mfcc-test kaldi-mfcc) - -add_executable(linear_spectrogram_main ${CMAKE_CURRENT_SOURCE_DIR}/linear_spectrogram_main.cc) -target_include_directories(linear_spectrogram_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) -target_link_libraries(linear_spectrogram_main frontend kaldi-util kaldi-feat-common gflags glog) \ No newline at end of file diff --git a/speechx/examples/feat/feature-mfcc-test.cc b/speechx/examples/feat/feature-mfcc-test.cc deleted file mode 100644 index 48a9e1c29..000000000 --- a/speechx/examples/feat/feature-mfcc-test.cc +++ /dev/null @@ -1,719 +0,0 @@ -// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -// feat/feature-mfcc-test.cc - -// Copyright 2009-2011 Karel Vesely; Petr Motlicek - -// See ../../COPYING for clarification regarding multiple authors -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED -// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE, -// MERCHANTABLITY OR NON-INFRINGEMENT. -// See the Apache 2 License for the specific language governing permissions and -// limitations under the License. - - -#include - -#include "base/kaldi-math.h" -#include "feat/feature-mfcc.h" -#include "feat/wave-reader.h" -#include "matrix/kaldi-matrix-inl.h" - -using namespace kaldi; - -static void UnitTestReadWave() { - std::cout << "=== UnitTestReadWave() ===\n"; - - Vector v, v2; - - std::cout << "<<<=== Reading waveform\n"; - - { - std::ifstream is("test_data/test.wav", std::ios_base::binary); - WaveData wave; - wave.Read(is); - const Matrix data(wave.Data()); - KALDI_ASSERT(data.NumRows() == 1); - v.Resize(data.NumCols()); - v.CopyFromVec(data.Row(0)); - } - - std::cout - << "<<<=== Reading Vector waveform, prepared by matlab\n"; - std::ifstream input("test_data/test_matlab.ascii"); - KALDI_ASSERT(input.good()); - v2.Read(input, false); - input.close(); - - std::cout - << "<<<=== Comparing freshly read waveform to 'libsndfile' waveform\n"; - KALDI_ASSERT(v.Dim() == v2.Dim()); - for (int32 i = 0; i < v.Dim(); i++) { - KALDI_ASSERT(v(i) == v2(i)); - } - std::cout << "<<<=== Comparing done\n"; - - // std::cout << "== The Waveform Samples == \n"; - // std::cout << v; - - std::cout << "Test passed :)\n\n"; -} - - -/** - */ -static void UnitTestSimple() { - std::cout << "=== UnitTestSimple() ===\n"; - - Vector v(100000); - Matrix m; - - // init with noise - for (int32 i = 0; i < v.Dim(); i++) { - v(i) = (abs(i * 433024253) % 65535) - (65535 / 2); - } - - std::cout << "<<<=== Just make sure it runs... Nothing is compared\n"; - // the parametrization object - MfccOptions op; - // trying to have same opts as baseline. - op.frame_opts.dither = 0.0; - op.frame_opts.preemph_coeff = 0.0; - op.frame_opts.window_type = "rectangular"; - op.frame_opts.remove_dc_offset = false; - op.frame_opts.round_to_power_of_two = true; - op.mel_opts.low_freq = 0.0; - op.mel_opts.htk_mode = true; - op.htk_compat = true; - - Mfcc mfcc(op); - // use default parameters - - // compute mfccs. - mfcc.Compute(v, 1.0, &m); - - // possibly dump - // std::cout << "== Output features == \n" << m; - std::cout << "Test passed :)\n\n"; -} - - -static void UnitTestHTKCompare1() { - std::cout << "=== UnitTestHTKCompare1() ===\n"; - - std::ifstream is("test_data/test.wav", std::ios_base::binary); - WaveData wave; - wave.Read(is); - KALDI_ASSERT(wave.Data().NumRows() == 1); - SubVector waveform(wave.Data(), 0); - - // read the HTK features - Matrix htk_features; - { - std::ifstream is("test_data/test.wav.fea_htk.1", - std::ios::in | std::ios_base::binary); - bool ans = ReadHtk(is, &htk_features, 0); - KALDI_ASSERT(ans); - } - - // use mfcc with default configuration... - MfccOptions op; - op.frame_opts.dither = 0.0; - op.frame_opts.preemph_coeff = 0.0; - op.frame_opts.window_type = "hamming"; - op.frame_opts.remove_dc_offset = false; - op.frame_opts.round_to_power_of_two = true; - op.mel_opts.low_freq = 0.0; - op.mel_opts.htk_mode = true; - op.htk_compat = true; - op.use_energy = false; // C0 not energy. - - Mfcc mfcc(op); - - // calculate kaldi features - Matrix kaldi_raw_features; - mfcc.Compute(waveform, 1.0, &kaldi_raw_features); - - DeltaFeaturesOptions delta_opts; - Matrix kaldi_features; - ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features); - - // compare the results - bool passed = true; - int32 i_old = -1; - KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows()); - KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols()); - // Ignore ends-- we make slightly different choices than - // HTK about how to treat the deltas at the ends. - for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) { - for (int32 j = 0; j < kaldi_features.NumCols(); j++) { - BaseFloat a = kaldi_features(i, j), b = htk_features(i, j); - if ((std::abs(b - a)) > 1.0) { //<< TOLERANCE TO DIFFERENCES!!!!! - // print the non-matching data only once per-line - if (i_old != i) { - std::cout << "\n\n\n[HTK-row: " << i << "] " - << htk_features.Row(i) << "\n"; - std::cout << "[Kaldi-row: " << i << "] " - << kaldi_features.Row(i) << "\n\n\n"; - i_old = i; - } - // print indices of non-matching cells - std::cout << "[" << i << ", " << j << "]"; - passed = false; - } - } - } - if (!passed) KALDI_ERR << "Test failed"; - - // write the htk features for later inspection - HtkHeader header = { - kaldi_features.NumRows(), - 100000, // 10ms - static_cast(sizeof(float) * kaldi_features.NumCols()), - 021406 // MFCC_D_A_0 - }; - { - std::ofstream os("tmp.test.wav.fea_kaldi.1", - std::ios::out | std::ios::binary); - WriteHtk(os, kaldi_features, header); - } - - std::cout << "Test passed :)\n\n"; - - unlink("tmp.test.wav.fea_kaldi.1"); -} - - -static void UnitTestHTKCompare2() { - std::cout << "=== UnitTestHTKCompare2() ===\n"; - - std::ifstream is("test_data/test.wav", std::ios_base::binary); - WaveData wave; - wave.Read(is); - KALDI_ASSERT(wave.Data().NumRows() == 1); - SubVector waveform(wave.Data(), 0); - - // read the HTK features - Matrix htk_features; - { - std::ifstream is("test_data/test.wav.fea_htk.2", - std::ios::in | std::ios_base::binary); - bool ans = ReadHtk(is, &htk_features, 0); - KALDI_ASSERT(ans); - } - - // use mfcc with default configuration... - MfccOptions op; - op.frame_opts.dither = 0.0; - op.frame_opts.preemph_coeff = 0.0; - op.frame_opts.window_type = "hamming"; - op.frame_opts.remove_dc_offset = false; - op.frame_opts.round_to_power_of_two = true; - op.mel_opts.low_freq = 0.0; - op.mel_opts.htk_mode = true; - op.htk_compat = true; - op.use_energy = true; // Use energy. - - Mfcc mfcc(op); - - // calculate kaldi features - Matrix kaldi_raw_features; - mfcc.Compute(waveform, 1.0, &kaldi_raw_features); - - DeltaFeaturesOptions delta_opts; - Matrix kaldi_features; - ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features); - - // compare the results - bool passed = true; - int32 i_old = -1; - KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows()); - KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols()); - // Ignore ends-- we make slightly different choices than - // HTK about how to treat the deltas at the ends. - for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) { - for (int32 j = 0; j < kaldi_features.NumCols(); j++) { - BaseFloat a = kaldi_features(i, j), b = htk_features(i, j); - if ((std::abs(b - a)) > 1.0) { //<< TOLERANCE TO DIFFERENCES!!!!! - // print the non-matching data only once per-line - if (i_old != i) { - std::cout << "\n\n\n[HTK-row: " << i << "] " - << htk_features.Row(i) << "\n"; - std::cout << "[Kaldi-row: " << i << "] " - << kaldi_features.Row(i) << "\n\n\n"; - i_old = i; - } - // print indices of non-matching cells - std::cout << "[" << i << ", " << j << "]"; - passed = false; - } - } - } - if (!passed) KALDI_ERR << "Test failed"; - - // write the htk features for later inspection - HtkHeader header = { - kaldi_features.NumRows(), - 100000, // 10ms - static_cast(sizeof(float) * kaldi_features.NumCols()), - 021406 // MFCC_D_A_0 - }; - { - std::ofstream os("tmp.test.wav.fea_kaldi.2", - std::ios::out | std::ios::binary); - WriteHtk(os, kaldi_features, header); - } - - std::cout << "Test passed :)\n\n"; - - unlink("tmp.test.wav.fea_kaldi.2"); -} - - -static void UnitTestHTKCompare3() { - std::cout << "=== UnitTestHTKCompare3() ===\n"; - - std::ifstream is("test_data/test.wav", std::ios_base::binary); - WaveData wave; - wave.Read(is); - KALDI_ASSERT(wave.Data().NumRows() == 1); - SubVector waveform(wave.Data(), 0); - - // read the HTK features - Matrix htk_features; - { - std::ifstream is("test_data/test.wav.fea_htk.3", - std::ios::in | std::ios_base::binary); - bool ans = ReadHtk(is, &htk_features, 0); - KALDI_ASSERT(ans); - } - - // use mfcc with default configuration... - MfccOptions op; - op.frame_opts.dither = 0.0; - op.frame_opts.preemph_coeff = 0.0; - op.frame_opts.window_type = "hamming"; - op.frame_opts.remove_dc_offset = false; - op.frame_opts.round_to_power_of_two = true; - op.htk_compat = true; - op.use_energy = true; // Use energy. - op.mel_opts.low_freq = 20.0; - // op.mel_opts.debug_mel = true; - op.mel_opts.htk_mode = true; - - Mfcc mfcc(op); - - // calculate kaldi features - Matrix kaldi_raw_features; - mfcc.Compute(waveform, 1.0, &kaldi_raw_features); - - DeltaFeaturesOptions delta_opts; - Matrix kaldi_features; - ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features); - - // compare the results - bool passed = true; - int32 i_old = -1; - KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows()); - KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols()); - // Ignore ends-- we make slightly different choices than - // HTK about how to treat the deltas at the ends. - for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) { - for (int32 j = 0; j < kaldi_features.NumCols(); j++) { - BaseFloat a = kaldi_features(i, j), b = htk_features(i, j); - if ((std::abs(b - a)) > 1.0) { //<< TOLERANCE TO DIFFERENCES!!!!! - // print the non-matching data only once per-line - if (static_cast(i_old) != i) { - std::cout << "\n\n\n[HTK-row: " << i << "] " - << htk_features.Row(i) << "\n"; - std::cout << "[Kaldi-row: " << i << "] " - << kaldi_features.Row(i) << "\n\n\n"; - i_old = i; - } - // print indices of non-matching cells - std::cout << "[" << i << ", " << j << "]"; - passed = false; - } - } - } - if (!passed) KALDI_ERR << "Test failed"; - - // write the htk features for later inspection - HtkHeader header = { - kaldi_features.NumRows(), - 100000, // 10ms - static_cast(sizeof(float) * kaldi_features.NumCols()), - 021406 // MFCC_D_A_0 - }; - { - std::ofstream os("tmp.test.wav.fea_kaldi.3", - std::ios::out | std::ios::binary); - WriteHtk(os, kaldi_features, header); - } - - std::cout << "Test passed :)\n\n"; - - unlink("tmp.test.wav.fea_kaldi.3"); -} - - -static void UnitTestHTKCompare4() { - std::cout << "=== UnitTestHTKCompare4() ===\n"; - - std::ifstream is("test_data/test.wav", std::ios_base::binary); - WaveData wave; - wave.Read(is); - KALDI_ASSERT(wave.Data().NumRows() == 1); - SubVector waveform(wave.Data(), 0); - - // read the HTK features - Matrix htk_features; - { - std::ifstream is("test_data/test.wav.fea_htk.4", - std::ios::in | std::ios_base::binary); - bool ans = ReadHtk(is, &htk_features, 0); - KALDI_ASSERT(ans); - } - - // use mfcc with default configuration... - MfccOptions op; - op.frame_opts.dither = 0.0; - op.frame_opts.window_type = "hamming"; - op.frame_opts.remove_dc_offset = false; - op.frame_opts.round_to_power_of_two = true; - op.mel_opts.low_freq = 0.0; - op.htk_compat = true; - op.use_energy = true; // Use energy. - op.mel_opts.htk_mode = true; - - Mfcc mfcc(op); - - // calculate kaldi features - Matrix kaldi_raw_features; - mfcc.Compute(waveform, 1.0, &kaldi_raw_features); - - DeltaFeaturesOptions delta_opts; - Matrix kaldi_features; - ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features); - - // compare the results - bool passed = true; - int32 i_old = -1; - KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows()); - KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols()); - // Ignore ends-- we make slightly different choices than - // HTK about how to treat the deltas at the ends. - for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) { - for (int32 j = 0; j < kaldi_features.NumCols(); j++) { - BaseFloat a = kaldi_features(i, j), b = htk_features(i, j); - if ((std::abs(b - a)) > 1.0) { //<< TOLERANCE TO DIFFERENCES!!!!! - // print the non-matching data only once per-line - if (static_cast(i_old) != i) { - std::cout << "\n\n\n[HTK-row: " << i << "] " - << htk_features.Row(i) << "\n"; - std::cout << "[Kaldi-row: " << i << "] " - << kaldi_features.Row(i) << "\n\n\n"; - i_old = i; - } - // print indices of non-matching cells - std::cout << "[" << i << ", " << j << "]"; - passed = false; - } - } - } - if (!passed) KALDI_ERR << "Test failed"; - - // write the htk features for later inspection - HtkHeader header = { - kaldi_features.NumRows(), - 100000, // 10ms - static_cast(sizeof(float) * kaldi_features.NumCols()), - 021406 // MFCC_D_A_0 - }; - { - std::ofstream os("tmp.test.wav.fea_kaldi.4", - std::ios::out | std::ios::binary); - WriteHtk(os, kaldi_features, header); - } - - std::cout << "Test passed :)\n\n"; - - unlink("tmp.test.wav.fea_kaldi.4"); -} - - -static void UnitTestHTKCompare5() { - std::cout << "=== UnitTestHTKCompare5() ===\n"; - - std::ifstream is("test_data/test.wav", std::ios_base::binary); - WaveData wave; - wave.Read(is); - KALDI_ASSERT(wave.Data().NumRows() == 1); - SubVector waveform(wave.Data(), 0); - - // read the HTK features - Matrix htk_features; - { - std::ifstream is("test_data/test.wav.fea_htk.5", - std::ios::in | std::ios_base::binary); - bool ans = ReadHtk(is, &htk_features, 0); - KALDI_ASSERT(ans); - } - - // use mfcc with default configuration... - MfccOptions op; - op.frame_opts.dither = 0.0; - op.frame_opts.window_type = "hamming"; - op.frame_opts.remove_dc_offset = false; - op.frame_opts.round_to_power_of_two = true; - op.htk_compat = true; - op.use_energy = true; // Use energy. - op.mel_opts.low_freq = 0.0; - op.mel_opts.vtln_low = 100.0; - op.mel_opts.vtln_high = 7500.0; - op.mel_opts.htk_mode = true; - - BaseFloat vtln_warp = - 1.1; // our approach identical to htk for warp factor >1, - // differs slightly for higher mel bins if warp_factor <0.9 - - Mfcc mfcc(op); - - // calculate kaldi features - Matrix kaldi_raw_features; - mfcc.Compute(waveform, vtln_warp, &kaldi_raw_features); - - DeltaFeaturesOptions delta_opts; - Matrix kaldi_features; - ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features); - - // compare the results - bool passed = true; - int32 i_old = -1; - KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows()); - KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols()); - // Ignore ends-- we make slightly different choices than - // HTK about how to treat the deltas at the ends. - for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) { - for (int32 j = 0; j < kaldi_features.NumCols(); j++) { - BaseFloat a = kaldi_features(i, j), b = htk_features(i, j); - if ((std::abs(b - a)) > 1.0) { //<< TOLERANCE TO DIFFERENCES!!!!! - // print the non-matching data only once per-line - if (static_cast(i_old) != i) { - std::cout << "\n\n\n[HTK-row: " << i << "] " - << htk_features.Row(i) << "\n"; - std::cout << "[Kaldi-row: " << i << "] " - << kaldi_features.Row(i) << "\n\n\n"; - i_old = i; - } - // print indices of non-matching cells - std::cout << "[" << i << ", " << j << "]"; - passed = false; - } - } - } - if (!passed) KALDI_ERR << "Test failed"; - - // write the htk features for later inspection - HtkHeader header = { - kaldi_features.NumRows(), - 100000, // 10ms - static_cast(sizeof(float) * kaldi_features.NumCols()), - 021406 // MFCC_D_A_0 - }; - { - std::ofstream os("tmp.test.wav.fea_kaldi.5", - std::ios::out | std::ios::binary); - WriteHtk(os, kaldi_features, header); - } - - std::cout << "Test passed :)\n\n"; - - unlink("tmp.test.wav.fea_kaldi.5"); -} - -static void UnitTestHTKCompare6() { - std::cout << "=== UnitTestHTKCompare6() ===\n"; - - - std::ifstream is("test_data/test.wav", std::ios_base::binary); - WaveData wave; - wave.Read(is); - KALDI_ASSERT(wave.Data().NumRows() == 1); - SubVector waveform(wave.Data(), 0); - - // read the HTK features - Matrix htk_features; - { - std::ifstream is("test_data/test.wav.fea_htk.6", - std::ios::in | std::ios_base::binary); - bool ans = ReadHtk(is, &htk_features, 0); - KALDI_ASSERT(ans); - } - - // use mfcc with default configuration... - MfccOptions op; - op.frame_opts.dither = 0.0; - op.frame_opts.preemph_coeff = 0.97; - op.frame_opts.window_type = "hamming"; - op.frame_opts.remove_dc_offset = false; - op.frame_opts.round_to_power_of_two = true; - op.mel_opts.num_bins = 24; - op.mel_opts.low_freq = 125.0; - op.mel_opts.high_freq = 7800.0; - op.htk_compat = true; - op.use_energy = false; // C0 not energy. - - Mfcc mfcc(op); - - // calculate kaldi features - Matrix kaldi_raw_features; - mfcc.Compute(waveform, 1.0, &kaldi_raw_features); - - DeltaFeaturesOptions delta_opts; - Matrix kaldi_features; - ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features); - - // compare the results - bool passed = true; - int32 i_old = -1; - KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows()); - KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols()); - // Ignore ends-- we make slightly different choices than - // HTK about how to treat the deltas at the ends. - for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) { - for (int32 j = 0; j < kaldi_features.NumCols(); j++) { - BaseFloat a = kaldi_features(i, j), b = htk_features(i, j); - if ((std::abs(b - a)) > 1.0) { //<< TOLERANCE TO DIFFERENCES!!!!! - // print the non-matching data only once per-line - if (static_cast(i_old) != i) { - std::cout << "\n\n\n[HTK-row: " << i << "] " - << htk_features.Row(i) << "\n"; - std::cout << "[Kaldi-row: " << i << "] " - << kaldi_features.Row(i) << "\n\n\n"; - i_old = i; - } - // print indices of non-matching cells - std::cout << "[" << i << ", " << j << "]"; - passed = false; - } - } - } - if (!passed) KALDI_ERR << "Test failed"; - - // write the htk features for later inspection - HtkHeader header = { - kaldi_features.NumRows(), - 100000, // 10ms - static_cast(sizeof(float) * kaldi_features.NumCols()), - 021406 // MFCC_D_A_0 - }; - { - std::ofstream os("tmp.test.wav.fea_kaldi.6", - std::ios::out | std::ios::binary); - WriteHtk(os, kaldi_features, header); - } - - std::cout << "Test passed :)\n\n"; - - unlink("tmp.test.wav.fea_kaldi.6"); -} - -void UnitTestVtln() { - // Test the function VtlnWarpFreq. - BaseFloat low_freq = 10, high_freq = 7800, vtln_low_cutoff = 20, - vtln_high_cutoff = 7400; - - for (size_t i = 0; i < 100; i++) { - BaseFloat freq = 5000, warp_factor = 0.9 + RandUniform() * 0.2; - AssertEqual(MelBanks::VtlnWarpFreq(vtln_low_cutoff, - vtln_high_cutoff, - low_freq, - high_freq, - warp_factor, - freq), - freq / warp_factor); - - AssertEqual(MelBanks::VtlnWarpFreq(vtln_low_cutoff, - vtln_high_cutoff, - low_freq, - high_freq, - warp_factor, - low_freq), - low_freq); - AssertEqual(MelBanks::VtlnWarpFreq(vtln_low_cutoff, - vtln_high_cutoff, - low_freq, - high_freq, - warp_factor, - high_freq), - high_freq); - BaseFloat freq2 = low_freq + (high_freq - low_freq) * RandUniform(), - freq3 = freq2 + - (high_freq - freq2) * RandUniform(); // freq3>=freq2 - BaseFloat w2 = MelBanks::VtlnWarpFreq(vtln_low_cutoff, - vtln_high_cutoff, - low_freq, - high_freq, - warp_factor, - freq2); - BaseFloat w3 = MelBanks::VtlnWarpFreq(vtln_low_cutoff, - vtln_high_cutoff, - low_freq, - high_freq, - warp_factor, - freq3); - KALDI_ASSERT(w3 >= w2); // increasing function. - BaseFloat w3dash = MelBanks::VtlnWarpFreq( - vtln_low_cutoff, vtln_high_cutoff, low_freq, high_freq, 1.0, freq3); - AssertEqual(w3dash, freq3); - } -} - -static void UnitTestFeat() { - UnitTestVtln(); - UnitTestReadWave(); - UnitTestSimple(); - UnitTestHTKCompare1(); - UnitTestHTKCompare2(); - // commenting out this one as it doesn't compare right now I normalized - // the way the FFT bins are treated (removed offset of 0.5)... this seems - // to relate to the way frequency zero behaves. - UnitTestHTKCompare3(); - UnitTestHTKCompare4(); - UnitTestHTKCompare5(); - UnitTestHTKCompare6(); - std::cout << "Tests succeeded.\n"; -} - - -int main() { - try { - for (int i = 0; i < 5; i++) UnitTestFeat(); - std::cout << "Tests succeeded.\n"; - return 0; - } catch (const std::exception &e) { - std::cerr << e.what(); - return 1; - } -} diff --git a/speechx/examples/feat/linear_spectrogram_main.cc b/speechx/examples/feat/linear_spectrogram_main.cc deleted file mode 100644 index 2d75bb5df..000000000 --- a/speechx/examples/feat/linear_spectrogram_main.cc +++ /dev/null @@ -1,267 +0,0 @@ -// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -// todo refactor, repalce with gtest - -#include "base/flags.h" -#include "base/log.h" -#include "kaldi/feat/wave-reader.h" -#include "kaldi/util/kaldi-io.h" -#include "kaldi/util/table-types.h" - -#include "frontend/audio/audio_cache.h" -#include "frontend/audio/data_cache.h" -#include "frontend/audio/feature_cache.h" -#include "frontend/audio/frontend_itf.h" -#include "frontend/audio/linear_spectrogram.h" -#include "frontend/audio/normalizer.h" - -DEFINE_string(wav_rspecifier, "", "test wav scp path"); -DEFINE_string(feature_wspecifier, "", "output feats wspecifier"); -DEFINE_string(cmvn_write_path, "./cmvn.ark", "write cmvn"); - - -std::vector mean_{ - -13730251.531853663, -12982852.199316509, -13673844.299583456, - -13089406.559646806, -12673095.524938712, -12823859.223276224, - -13590267.158903603, -14257618.467152044, -14374605.116185192, - -14490009.21822485, -14849827.158924166, -15354435.470563512, - -15834149.206532761, -16172971.985514281, -16348740.496746974, - -16423536.699409386, -16556246.263649225, -16744088.772748645, - -16916184.08510357, -17054034.840031497, -17165612.509455364, - -17255955.470915023, -17322572.527648456, -17408943.862033736, - -17521554.799865916, -17620623.254924215, -17699792.395918526, - -17723364.411134344, -17741483.4433254, -17747426.888704527, - -17733315.928209435, -17748780.160905756, -17808336.883775543, - -17895918.671983004, -18009812.59173023, -18098188.66548325, - -18195798.958462656, -18293617.62980999, -18397432.92077201, - -18505834.787318766, -18585451.8100908, -18652438.235649142, - -18700960.306275308, -18734944.58792185, -18737426.313365128, - -18735347.165987637, -18738813.444170244, -18737086.848890636, - -18731576.2474336, -18717405.44095871, -18703089.25545657, - -18691014.546456724, -18692460.568905357, -18702119.628629155, - -18727710.621126678, -18761582.72034647, -18806745.835547544, - -18850674.8692112, -18884431.510951452, -18919999.992506847, - -18939303.799078144, -18952946.273760635, -18980289.22996379, - -19011610.17803294, -19040948.61805145, -19061021.429847397, - -19112055.53768819, -19149667.414264943, -19201127.05091321, - -19270250.82564605, -19334606.883057203, -19390513.336589377, - -19444176.259208687, -19502755.000038862, -19544333.014549147, - -19612668.183176614, -19681902.19006569, -19771969.951249883, - -19873329.723376893, -19996752.59235844, -20110031.131400537, - -20231658.612529557, -20319378.894054495, -20378534.45718066, - -20413332.089584175, -20438147.844177883, -20443710.248040095, - -20465457.02238927, -20488610.969337028, -20516295.16424432, - -20541423.795738827, -20553192.874953747, -20573605.50701977, - -20577871.61936797, -20571807.008916274, -20556242.38912231, - -20542199.30819195, -20521239.063551214, -20519150.80004532, - -20527204.80248933, -20536933.769257784, -20543470.522332076, - -20549700.089992985, -20551525.24958494, -20554873.406493705, - -20564277.65794227, -20572211.740052115, -20574305.69550465, - -20575494.450104576, -20567092.577932164, -20549302.929608088, - -20545445.11878376, -20546625.326603737, -20549190.03499401, - -20554824.947828256, -20568341.378989458, -20577582.331383612, - -20577980.519402675, -20566603.03458152, -20560131.592262644, - -20552166.469060015, -20549063.06763577, -20544490.562339947, - -20539817.82346569, -20528747.715731595, -20518026.24576161, - -20510977.844974525, -20506874.36087992, -20506731.11977665, - -20510482.133420516, -20507760.92101862, -20494644.834457114, - -20480107.89304893, -20461312.091867123, -20442941.75080173, - -20426123.02834838, -20424607.675283, -20426810.369107097, - -20434024.50097819, -20437404.75544205, -20447688.63916367, - -20460893.335563846, -20482922.735127095, -20503610.119434915, - -20527062.76448319, -20557830.035128627, -20593274.72068722, - -20632528.452965066, -20673637.471334763, -20733106.97143075, - -20842921.0447562, -21054357.83621519, -21416569.534189366, - -21978460.272811692, -22753170.052172784, -23671344.10563395, - -24613499.293358143, -25406477.12230188, -25884377.82156489, - -26049040.62791664, -26996879.104431007}; -std::vector variance_{ - 213747175.10846674, 188395815.34302503, 212706429.10966414, - 199109025.81461075, 189235901.23864496, 194901336.53253657, - 217481594.29306737, 238689869.12327808, 243977501.24115244, - 248479623.6431067, 259766741.47116545, 275516766.7790273, - 291271202.3691234, 302693239.8220509, 308627358.3997694, - 311143911.38788426, 315446105.07731867, 321705430.9341829, - 327458907.4659941, 332245072.43223983, 336251717.5935284, - 339694069.7639722, 342188204.4322228, 345587110.31313115, - 349903086.2875232, 353660214.20643026, 356700344.5270885, - 357665362.3529641, 358493352.05658793, 358857951.620328, - 358375239.52774596, 358899733.6342954, 361051818.3511561, - 364361716.05025816, 368750322.3771452, 372047800.6462831, - 375655861.1349018, 379358519.1980013, 383327605.3935181, - 387458599.282341, 390434692.3406868, 392994486.35057056, - 394874418.04603153, 396230525.79763395, 396365592.0414835, - 396334819.8242737, 396488353.19250053, 396438877.00744957, - 396197980.4459586, 395590921.6672991, 395001107.62072515, - 394528291.7318225, 394593110.424006, 395018405.59353715, - 396110577.5415993, 397506704.0371068, 399400197.4657644, - 401243568.2468382, 402687134.7805103, 404136047.2872507, - 404883170.001883, 405522253.219517, 406660365.3626476, - 407919346.0991902, 409045348.5384909, 409759588.7889818, - 411974821.8564483, 413489718.78201455, 415535392.56684107, - 418466481.97674364, 421104678.35678065, 423405392.5200779, - 425550570.40798235, 427929423.9579701, 429585274.253478, - 432368493.55181056, 435193587.13513297, 438886855.20476013, - 443058876.8633751, 448181232.5093362, 452883835.6332396, - 458056721.77926534, 461816531.22735566, 464363620.1970998, - 465886343.5057493, 466928872.0651, 467180536.42647296, - 468111848.70714295, 469138695.3071312, 470378429.6930793, - 471517958.7132626, 472109050.4262365, 473087417.0177867, - 473381322.04648733, 473220195.85483915, 472666071.8998819, - 472124669.87879956, 471298571.411737, 471251033.2902761, - 471672676.43128747, 472177147.2193172, 472572361.7711908, - 472968783.7751127, 473156295.4164052, 473398034.82676554, - 473897703.5203811, 474328271.33112127, 474452670.98002136, - 474549003.99284613, 474252887.13567275, 473557462.909069, - 473483385.85193115, 473609738.04855174, 473746944.82085115, - 474016729.91696435, 474617321.94138587, 475045097.237122, - 475125402.586558, 474664112.9824912, 474426247.5800283, - 474104075.42796475, 473978219.7273978, 473773171.7798875, - 473578534.69508696, 473102924.16904145, 472651240.5232615, - 472374383.1810912, 472209479.6956096, 472202298.8921673, - 472370090.76781124, 472220933.99374026, 471625467.37106377, - 470994646.51883453, 470182428.9637543, 469348211.5939578, - 468570387.4467277, 468540442.7225135, 468672018.90414184, - 468994346.9533251, 469138757.58201426, 469553915.95710236, - 470134523.38582784, 471082421.62055486, 471962316.51804745, - 472939745.1708408, 474250621.5944825, 475773933.43199486, - 477465399.71087736, 479218782.61382693, 481752299.7930922, - 486608947.8984568, 496119403.2067917, 512730085.5704984, - 539048915.2641417, 576285298.3548826, 621610270.2240586, - 669308196.4436442, 710656993.5957186, 736344437.3725077, - 745481288.0241544, 801121432.9925804}; -int count_ = 912592; - -void WriteMatrix() { - kaldi::Matrix cmvn_stats(2, mean_.size() + 1); - for (size_t idx = 0; idx < mean_.size(); ++idx) { - cmvn_stats(0, idx) = mean_[idx]; - cmvn_stats(1, idx) = variance_[idx]; - } - cmvn_stats(0, mean_.size()) = count_; - kaldi::WriteKaldiObject(cmvn_stats, FLAGS_cmvn_write_path, false); -} - -int main(int argc, char* argv[]) { - gflags::ParseCommandLineFlags(&argc, &argv, false); - google::InitGoogleLogging(argv[0]); - - kaldi::SequentialTableReader wav_reader( - FLAGS_wav_rspecifier); - kaldi::BaseFloatMatrixWriter feat_writer(FLAGS_feature_wspecifier); - WriteMatrix(); - - - int32 num_done = 0, num_err = 0; - - // feature pipeline: wave cache --> decibel_normalizer --> hanning - // window -->linear_spectrogram --> global cmvn -> feat cache - - // std::unique_ptr data_source(new - // ppspeech::DataCache()); - std::unique_ptr data_source( - new ppspeech::AudioCache()); - - ppspeech::DecibelNormalizerOptions db_norm_opt; - std::unique_ptr db_norm( - new ppspeech::DecibelNormalizer(db_norm_opt, std::move(data_source))); - - ppspeech::LinearSpectrogramOptions opt; - opt.frame_opts.frame_length_ms = 20; - opt.frame_opts.frame_shift_ms = 10; - opt.frame_opts.dither = 0.0; - opt.frame_opts.remove_dc_offset = false; - opt.frame_opts.window_type = "hanning"; - opt.frame_opts.preemph_coeff = 0.0; - LOG(INFO) << "frame length (ms): " << opt.frame_opts.frame_length_ms; - LOG(INFO) << "frame shift (ms): " << opt.frame_opts.frame_shift_ms; - - std::unique_ptr linear_spectrogram( - new ppspeech::LinearSpectrogram(opt, std::move(db_norm))); - - std::unique_ptr cmvn(new ppspeech::CMVN( - FLAGS_cmvn_write_path, std::move(linear_spectrogram))); - - ppspeech::FeatureCache feature_cache(kint16max, std::move(cmvn)); - LOG(INFO) << "feat dim: " << feature_cache.Dim(); - - int sample_rate = 16000; - float streaming_chunk = 0.36; - int chunk_sample_size = streaming_chunk * sample_rate; - LOG(INFO) << "sr: " << sample_rate; - LOG(INFO) << "chunk size (s): " << streaming_chunk; - LOG(INFO) << "chunk size (sample): " << chunk_sample_size; - - - for (; !wav_reader.Done(); wav_reader.Next()) { - std::string utt = wav_reader.Key(); - const kaldi::WaveData& wave_data = wav_reader.Value(); - LOG(INFO) << "process utt: " << utt; - - int32 this_channel = 0; - kaldi::SubVector waveform(wave_data.Data(), - this_channel); - int tot_samples = waveform.Dim(); - LOG(INFO) << "wav len (sample): " << tot_samples; - - int sample_offset = 0; - std::vector> feats; - int feature_rows = 0; - while (sample_offset < tot_samples) { - int cur_chunk_size = - std::min(chunk_sample_size, tot_samples - sample_offset); - - kaldi::Vector wav_chunk(cur_chunk_size); - for (int i = 0; i < cur_chunk_size; ++i) { - wav_chunk(i) = waveform(sample_offset + i); - } - - kaldi::Vector features; - feature_cache.Accept(wav_chunk); - if (cur_chunk_size < chunk_sample_size) { - feature_cache.SetFinished(); - } - feature_cache.Read(&features); - if (features.Dim() == 0) break; - - feats.push_back(features); - sample_offset += cur_chunk_size; - feature_rows += features.Dim() / feature_cache.Dim(); - } - - int cur_idx = 0; - kaldi::Matrix features(feature_rows, - feature_cache.Dim()); - for (auto feat : feats) { - int num_rows = feat.Dim() / feature_cache.Dim(); - for (int row_idx = 0; row_idx < num_rows; ++row_idx) { - for (size_t col_idx = 0; col_idx < feature_cache.Dim(); - ++col_idx) { - features(cur_idx, col_idx) = - feat(row_idx * feature_cache.Dim() + col_idx); - } - ++cur_idx; - } - } - feat_writer.Write(utt, features); - - if (num_done % 50 == 0 && num_done != 0) - KALDI_VLOG(2) << "Processed " << num_done << " utterances"; - num_done++; - } - KALDI_LOG << "Done " << num_done << " utterances, " << num_err - << " with errors."; - return (num_done != 0 ? 0 : 1); -} diff --git a/speechx/examples/feat/run.sh b/speechx/examples/feat/run.sh deleted file mode 100755 index 29c49d325..000000000 --- a/speechx/examples/feat/run.sh +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash -set +x -set -e - -. ./path.sh - -# 1. compile -if [ ! -d ${SPEECHX_EXAMPLES} ]; then - pushd ${SPEECHX_ROOT} - bash build.sh - popd -fi - -# 2. download model -if [ ! -d ../paddle_asr_model ]; then - wget https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/paddle_asr_model.tar.gz - tar xzfv paddle_asr_model.tar.gz - mv ./paddle_asr_model ../ - # produce wav scp - echo "utt1 " $PWD/../paddle_asr_model/BAC009S0764W0290.wav > ../paddle_asr_model/wav.scp -fi - -model_dir=../paddle_asr_model -feat_wspecifier=./feats.ark -cmvn=./cmvn.ark - -# 3. run feat -export GLOG_logtostderr=1 -linear_spectrogram_main \ - --wav_rspecifier=scp:$model_dir/wav.scp \ - --feature_wspecifier=ark,t:$feat_wspecifier \ - --cmvn_write_path=$cmvn diff --git a/speechx/examples/ngram/.gitignore b/speechx/examples/ngram/.gitignore new file mode 100644 index 000000000..bbd86a25b --- /dev/null +++ b/speechx/examples/ngram/.gitignore @@ -0,0 +1,2 @@ +data +exp diff --git a/speechx/examples/ngram/en/README.md b/speechx/examples/ngram/en/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/speechx/examples/ngram/zh/README.md b/speechx/examples/ngram/zh/README.md new file mode 100644 index 000000000..e11bd3439 --- /dev/null +++ b/speechx/examples/ngram/zh/README.md @@ -0,0 +1,101 @@ +# ngram train for mandarin + +Quick run: +``` +bash run.sh --stage -1 +``` + +## input + +input files: +``` +data/ +├── lexicon.txt +├── text +└── vocab.txt +``` + +``` +==> data/text <== +BAC009S0002W0122 而 对 楼市 成交 抑制 作用 最 大 的 限 购 +BAC009S0002W0123 也 成为 地方 政府 的 眼中 钉 +BAC009S0002W0124 自 六月 底 呼和浩特 市 率先 宣布 取消 限 购 后 +BAC009S0002W0125 各地 政府 便 纷纷 跟进 +BAC009S0002W0126 仅 一 个 多 月 的 时间 里 +BAC009S0002W0127 除了 北京 上海 广州 深圳 四 个 一 线 城市 和 三亚 之外 +BAC009S0002W0128 四十六 个 限 购 城市 当中 +BAC009S0002W0129 四十一 个 已 正式 取消 或 变相 放松 了 限 购 +BAC009S0002W0130 财政 金融 政策 紧随 其后 而来 +BAC009S0002W0131 显示 出 了 极 强 的 威力 + +==> data/lexicon.txt <== +SIL sil + sil +啊 aa a1 +啊 aa a2 +啊 aa a4 +啊 aa a5 +啊啊啊 aa a2 aa a2 aa a2 +啊啊啊 aa a5 aa a5 aa a5 +坐地 z uo4 d i4 +坐实 z uo4 sh ix2 +坐视 z uo4 sh ix4 +坐稳 z uo4 uu un3 +坐拥 z uo4 ii iong1 +坐诊 z uo4 zh en3 +坐庄 z uo4 zh uang1 +坐姿 z uo4 z iy1 + +==> data/vocab.txt <== + + +A +B +C +D +E +龙 +龚 +龛 + +``` + +## output + +``` +data/ +├── local +│ ├── dict +│ │ ├── lexicon.txt +│ │ └── units.txt +│ └── lm +│ ├── heldout +│ ├── lm.arpa +│ ├── text +│ ├── text.no_oov +│ ├── train +│ ├── unigram.counts +│ ├── word.counts +│ └── wordlist +``` + +``` +/workspace/srilm/bin/i686-m64/ngram-count +Namespace(bpemodel=None, in_lexicon='data/lexicon.txt', out_lexicon='data/local/dict/lexicon.txt', unit_file='data/vocab.txt') +Ignoring words 矽, which contains oov unit +Ignoring words 傩, which contains oov unit +Ignoring words 堀, which contains oov unit +Ignoring words 莼, which contains oov unit +Ignoring words 菰, which contains oov unit +Ignoring words 摭, which contains oov unit +Ignoring words 帙, which contains oov unit +Ignoring words 迨, which contains oov unit +Ignoring words 孥, which contains oov unit +Ignoring words 瑗, which contains oov unit +... +... +... +file data/local/lm/heldout: 10000 sentences, 89496 words, 0 OOVs +0 zeroprobs, logprob= -270337.9 ppl= 521.2819 ppl1= 1048.745 +build LM done. +``` diff --git a/speechx/examples/ngram/zh/local/aishell_train_lms.sh b/speechx/examples/ngram/zh/local/aishell_train_lms.sh new file mode 100755 index 000000000..544a1f59a --- /dev/null +++ b/speechx/examples/ngram/zh/local/aishell_train_lms.sh @@ -0,0 +1,71 @@ +#!/bin/bash + +# To be run from one directory above this script. +. ./path.sh + +nj=40 +text=data/local/lm/text +lexicon=data/local/dict/lexicon.txt + +for f in "$text" "$lexicon"; do + [ ! -f $x ] && echo "$0: No such file $f" && exit 1; +done + +# Check SRILM tools +if ! which ngram-count > /dev/null; then + echo "srilm tools are not found, please download it and install it from: " + echo "http://www.speech.sri.com/projects/srilm/download.html" + echo "Then add the tools to your PATH" + exit 1 +fi + +# This script takes no arguments. It assumes you have already run +# aishell_data_prep.sh. +# It takes as input the files +# data/local/lm/text +# data/local/dict/lexicon.txt +dir=data/local/lm +mkdir -p $dir + +cleantext=$dir/text.no_oov + +# oov to +# lexicon line: word char0 ... charn +# text line: utt word0 ... wordn -> line: word0 ... wordn +text_dir=$(dirname $text) +split_name=$(basename $text) +./local/split_data.sh $text_dir $text $split_name $nj + +utils/run.pl JOB=1:$nj $text_dir/split${nj}/JOB/${split_name}.no_oov.log \ + cat ${text_dir}/split${nj}/JOB/${split_name} \| awk -v lex=$lexicon 'BEGIN{while((getline0){ seen[$1]=1; } } + {for(n=1; n<=NF;n++) { if (seen[$n]) { printf("%s ", $n); } else {printf(" ");} } printf("\n");}' \ + \> ${text_dir}/split${nj}/JOB/${split_name}.no_oov || exit 1; +cat ${text_dir}/split${nj}/*/${split_name}.no_oov > $cleantext + +# compute word counts, sort in descending order +# line: count word +cat $cleantext | awk '{for(n=2;n<=NF;n++) print $n; }' | sort --parallel=`nproc` | uniq -c | \ + sort --parallel=`nproc` -nr > $dir/word.counts || exit 1; + +# Get counts from acoustic training transcripts, and add one-count +# for each word in the lexicon (but not silence, we don't want it +# in the LM-- we'll add it optionally later). +cat $cleantext | awk '{for(n=2;n<=NF;n++) print $n; }' | \ + cat - <(grep -w -v '!SIL' $lexicon | awk '{print $1}') | \ + sort --parallel=`nproc` | uniq -c | sort --parallel=`nproc` -nr > $dir/unigram.counts || exit 1; + +# word with +cat $dir/unigram.counts | awk '{print $2}' | cat - <(echo ""; echo "" ) > $dir/wordlist + +# hold out to compute ppl +heldout_sent=10000 # Don't change this if you want result to be comparable with kaldi_lm results + +mkdir -p $dir +cat $cleantext | awk '{for(n=2;n<=NF;n++){ printf $n; if(n $dir/heldout +cat $cleantext | awk '{for(n=2;n<=NF;n++){ printf $n; if(n $dir/train + +ngram-count -text $dir/train -order 3 -limit-vocab -vocab $dir/wordlist -unk \ + -map-unk "" -kndiscount -interpolate -lm $dir/lm.arpa +ngram -lm $dir/lm.arpa -ppl $dir/heldout \ No newline at end of file diff --git a/speechx/examples/ngram/zh/local/split_data.sh b/speechx/examples/ngram/zh/local/split_data.sh new file mode 100755 index 000000000..2af6fc5ab --- /dev/null +++ b/speechx/examples/ngram/zh/local/split_data.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash + +set -eo pipefail + +data=$1 +scp=$2 +split_name=$3 +numsplit=$4 + +# save in $data/split{n} +# $scp to split +# + +if [[ ! $numsplit -gt 0 ]]; then + echo "Invalid num-split argument"; + exit 1; +fi + +directories=$(for n in `seq $numsplit`; do echo $data/split${numsplit}/$n; done) +scp_splits=$(for n in `seq $numsplit`; do echo $data/split${numsplit}/$n/${split_name}; done) + +# if this mkdir fails due to argument-list being too long, iterate. +if ! mkdir -p $directories >&/dev/null; then + for n in `seq $numsplit`; do + mkdir -p $data/split${numsplit}/$n + done +fi + +echo "utils/split_scp.pl $scp $scp_splits" +utils/split_scp.pl $scp $scp_splits diff --git a/speechx/examples/ngram/zh/local/text_to_lexicon.py b/speechx/examples/ngram/zh/local/text_to_lexicon.py new file mode 100755 index 000000000..0ccd07c7b --- /dev/null +++ b/speechx/examples/ngram/zh/local/text_to_lexicon.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 +import argparse +from collections import Counter + +def main(args): + counter = Counter() + with open(args.text, 'r') as fin, open(args.lexicon, 'w') as fout: + for line in fin: + line = line.strip() + if args.has_key: + utt, text = line.split(maxsplit=1) + words = text.split() + else: + words = line.split() + + counter.update(words) + + for word in counter: + val = " ".join(list(word)) + fout.write(f"{word}\t{val}\n") + fout.flush() + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description='text(line:utt1 中国 人) to lexicon（line:中国 中 国).') + parser.add_argument( + '--has_key', + default=True, + help='text path, with utt or not') + parser.add_argument( + '--text', + required=True, + help='text path. line: utt1 中国 人 or 中国 人') + parser.add_argument( + '--lexicon', + required=True, + help='lexicon path. line:中国 中 国') + args = parser.parse_args() + print(args) + + main(args) diff --git a/speechx/examples/ngram/zh/path.sh b/speechx/examples/ngram/zh/path.sh new file mode 100644 index 000000000..a3fb3d758 --- /dev/null +++ b/speechx/examples/ngram/zh/path.sh @@ -0,0 +1,12 @@ +# This contains the locations of binarys build required for running the examples. + +MAIN_ROOT=`realpath $PWD/../../../../` +SPEECHX_ROOT=`realpath $MAIN_ROOT/speechx` + +export LC_AL=C + +# srilm +export LIBLBFGS=${MAIN_ROOT}/tools/liblbfgs-1.10 +export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:-}:${LIBLBFGS}/lib/.libs +export SRILM=${MAIN_ROOT}/tools/srilm +export PATH=${PATH}:${SRILM}/bin:${SRILM}/bin/i686-m64 diff --git a/speechx/examples/ngram/zh/run.sh b/speechx/examples/ngram/zh/run.sh new file mode 100755 index 000000000..f24ad0a7c --- /dev/null +++ b/speechx/examples/ngram/zh/run.sh @@ -0,0 +1,68 @@ +#!/bin/bash +set -eo pipefail + +. path.sh + +stage=-1 +stop_stage=100 +corpus=aishell + +unit=data/vocab.txt # vocab file, line: char/spm_pice +lexicon=data/lexicon.txt # line: word ph0 ... phn, aishell/resource_aishell/lexicon.txt +text=data/text # line: utt text, aishell/data_aishell/transcript/aishell_transcript_v0.8.txt + +. utils/parse_options.sh + +data=$PWD/data +mkdir -p $data + +if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then + if [ ! -f $data/speech.ngram.zh.tar.gz ];then + pushd $data + wget -c http://paddlespeech.bj.bcebos.com/speechx/examples/ngram/zh/speech.ngram.zh.tar.gz + tar xvzf speech.ngram.zh.tar.gz + popd + fi +fi + +if [ ! -f $unit ]; then + echo "$0: No such file $unit" + exit 1; +fi + +if ! which ngram-count; then + pushd $MAIN_ROOT/tools + make srilm.done + popd +fi + +mkdir -p data/local/dict +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + # 7.1 Prepare dict + # line: char/spm_pices + cp $unit data/local/dict/units.txt + + if [ ! -f $lexicon ];then + local/text_to_lexicon.py --has_key true --text $text --lexicon $lexicon + echo "Generate $lexicon from $text" + fi + + # filter by vocab + # line: word ph0 ... phn -> line: word char0 ... charn + utils/fst/prepare_dict.py \ + --unit_file $unit \ + --in_lexicon ${lexicon} \ + --out_lexicon data/local/dict/lexicon.txt +fi + +lm=data/local/lm +mkdir -p $lm + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + # 7.2 Train lm + cp $text $lm/text + local/aishell_train_lms.sh +fi + +echo "build LM done." +exit 0 diff --git a/speechx/examples/ngram/zh/utils b/speechx/examples/ngram/zh/utils new file mode 120000 index 000000000..c2519a9dd --- /dev/null +++ b/speechx/examples/ngram/zh/utils @@ -0,0 +1 @@ +../../../../utils/ \ No newline at end of file diff --git a/speechx/examples/nnet/CMakeLists.txt b/speechx/examples/nnet/CMakeLists.txt deleted file mode 100644 index 20f4008ce..000000000 --- a/speechx/examples/nnet/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -cmake_minimum_required(VERSION 3.14 FATAL_ERROR) - -add_executable(pp-model-test ${CMAKE_CURRENT_SOURCE_DIR}/pp-model-test.cc) -target_include_directories(pp-model-test PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi) -target_link_libraries(pp-model-test PUBLIC nnet gflags ${DEPS}) \ No newline at end of file diff --git a/speechx/examples/nnet/run.sh b/speechx/examples/nnet/run.sh deleted file mode 100755 index 4d67d1988..000000000 --- a/speechx/examples/nnet/run.sh +++ /dev/null @@ -1,29 +0,0 @@ -#!/bin/bash -set +x -set -e - -. path.sh - -# 1. compile -if [ ! -d ${SPEECHX_EXAMPLES} ]; then - pushd ${SPEECHX_ROOT} - bash build.sh - popd -fi - -# 2. download model -if [ ! -d ../paddle_asr_model ]; then - wget https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/paddle_asr_model.tar.gz - tar xzfv paddle_asr_model.tar.gz - mv ./paddle_asr_model ../ - # produce wav scp - echo "utt1 " $PWD/../paddle_asr_model/BAC009S0764W0290.wav > ../paddle_asr_model/wav.scp -fi - -model_dir=../paddle_asr_model - -# 4. run decoder -pp-model-test \ - --model_path=$model_dir/avg_1.jit.pdmodel \ - --param_path=$model_dir/avg_1.jit.pdparams - diff --git a/speechx/examples/text_lm/.gitignore b/speechx/examples/text_lm/.gitignore new file mode 100644 index 000000000..1269488f7 --- /dev/null +++ b/speechx/examples/text_lm/.gitignore @@ -0,0 +1 @@ +data diff --git a/speechx/examples/text_lm/README.md b/speechx/examples/text_lm/README.md new file mode 100644 index 000000000..b06ec3124 --- /dev/null +++ b/speechx/examples/text_lm/README.md @@ -0,0 +1,44 @@ +# Text PreProcess for building ngram LM + +## Input + +``` +data/ +|-- text +``` + +Input file is kaldi-style, which has `utt` at first column: +``` +Y0000000000_--5llN02F84_S00000 怎么样这些日子住得还习惯吧 +Y0000000000_--5llN02F84_S00002 挺好的 +Y0000000000_--5llN02F84_S00003 对了美静这段日子经常不和我们一起用餐 +Y0000000000_--5llN02F84_S00004 是不是对我回来有什么想法啊 +Y0000000000_--5llN02F84_S00005 哪有的事啊 +Y0000000000_--5llN02F84_S00006 她这两天挺累的身体也不太舒服 +Y0000000000_--5llN02F84_S00007 我让她多睡一会那就好如果要是觉得不方便 +Y0000000000_--5llN02F84_S00009 我就搬出去住 +Y0000000000_--5llN02F84_S00010 你看你这个人你就是疑心太重 +Y0000000000_--5llN02F84_S00011 你现在多好一切都井然有序的 +``` + + +## Output + +``` +data/ +`-- text.tn +``` + +Output file like this: + +``` +BAC009S0002W0122 而 对 楼市 成交 抑制 作用 最 大 的 限 购 +BAC009S0002W0123 也 成为 地方 政府 的 眼中 钉 +BAC009S0002W0124 自 六月 底 呼和浩特 市 率先 宣布 取消 限 购 后 +BAC009S0002W0125 各地 政府 便 纷纷 跟进 +BAC009S0002W0126 仅 一 个 多 月 的 时间 里 +BAC009S0002W0127 除了 北京 上海 广州 深圳 四 个 一 线 城市 和 三亚 之外 +BAC009S0002W0128 四十六 个 限 购 城市 当中 +BAC009S0002W0129 四十一 个 已 正式 取消 或 变相 放松 了 限 购 +BAC009S0002W0130 财政 金融 政策 紧随 其后 而来 +``` diff --git a/speechx/examples/text_lm/local/data/chars.dic b/speechx/examples/text_lm/local/data/chars.dic new file mode 100644 index 000000000..5eed44657 --- /dev/null +++ b/speechx/examples/text_lm/local/data/chars.dic @@ -0,0 +1,12638 @@ +0 亄 +0 伒 +0 伭 +0 佒 +0 侺 +0 偋 +0 傶 +0 傸 +0 儓 +0 儢 +0 冹 +0 刌 +0 刐 +0 剆 +0 剢 +0 劀 +0 勓 +0 匎 +0 呇 +0 呠 +0 咰 +0 哸 +0 唋 +0 嗂 +0 嗋 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+烛残漏断频倚枕 +片红休扫尽从伊 +玉勒雕鞍游冶处 +玉珰缄札何由达 +玉虎牵丝汲井回 +玉阶仙仗拥千官 +玳筵急管曲复终 +画楼西畔桂堂东 +白雨跳珠乱入船 +盘心清露如铅水 +盘飧市远无兼味 +禅源诸诠集都序 +离人偏识长更苦 +离魂暗逐郎行远 +秋阴时睛渐向暝 +空床卧听南窗雨 +竹杖芒鞋轻胜马 +篱落疏疏一径深 +紫驼之峰出翠釜 +红藕香残玉簟秋 +绛帻鸡人送晓筹 +绿遍山原白满川 +绿酒初尝人易醉 +罗衣欲换更添香 +耳里如闻饥冻声 +耻令越甲鸣吾君 +肯数邺下黄须儿 +背灯和月就花阴 +自胡马窥江去后 +花迎剑佩星初落 +莫使金樽空对月 +莫听穿林打叶声 +蒸藜炊黍饷东菑 +蓝霞辽海沉过雁 +虬须虎眉仍大颡 +虹猫蓝兔七侠传 +虽九死其犹未悔 +蜡炬成灰泪始干 +衣上旧痕馀苦泪 +衣上酒痕诗里字 +被酒莫惊春睡重 +被驱不异犬与鸡 +西出都门百馀里 +解铃还是系铃人 +谢公行处苍苔没 +贾氏窥帘韩掾少 +身向榆关那畔行 +转烛飘蓬一梦归 +转益多师是汝师 +轻罗小扇扑流莹 +辞家终拟长游衍 +辞根散作九秋蓬 +运移汉祚终难复 +远水救不了近火 +道是无晴却有晴 +郴江幸自绕郴山 +醉里吴音相媚好 +采香行处蹙连钱 +野蔬充膳甘长藿 +金粟堆前木已拱 +铜簧韵脆锵寒竹 +间关莺语花底滑 +陇上羊归塞草烟 +雁声远向萧关去 +霓为衣兮风为马 +霜凄万树风入衣 +青女素娥俱耐冷 +非有想非无想处 +顺得哥情失嫂意 +风絮飘残已化萍 +飞湍瀑流争喧豗 +香尘已隔犹回面 +香销被冷残灯灭 +马毛带雪汗气蒸 +高咏楚词酬午日 +听安万善吹筚篥歌 +和马叔度游月波楼 +料不啼清泪长啼血 +明妃曲和王介甫作 +有碗话碗有碟话碟 +下有冲波逆折之回川 +埃索美拉唑镁肠溶片 +夜登小阁忆洛中旧游 +奉济驿重送严公四韵 \ No newline at end of file diff --git a/speechx/examples/text_lm/local/mmseg.py b/speechx/examples/text_lm/local/mmseg.py new file mode 100755 index 000000000..9b94ac31d --- /dev/null +++ b/speechx/examples/text_lm/local/mmseg.py @@ -0,0 +1,380 @@ +#!/usr/bin/env python3 + +# modify from https://sites.google.com/site/homepageoffuyanwei/Home/remarksandexcellentdiscussion/page-2 + +class Word: + def __init__(self,text = '',freq = 0): + self.text = text + self.freq = freq + self.length = len(text) + +class Chunk: + def __init__(self,w1,w2 = None,w3 = None): + self.words = [] + self.words.append(w1) + if w2: + self.words.append(w2) + if w3: + self.words.append(w3) + + #计算chunk的总长度 + def totalWordLength(self): + length = 0 + for word in self.words: + length += len(word.text) + return length + + #计算平均长度 + def averageWordLength(self): + return float(self.totalWordLength()) / float(len(self.words)) + + #计算标准差 + def standardDeviation(self): + average = self.averageWordLength() + sum = 0.0 + for word in self.words: + tmp = (len(word.text) - average) + sum += float(tmp) * float(tmp) + return sum + + #自由语素度 + def wordFrequency(self): + sum = 0 + for word in self.words: + sum += word.freq + return sum + +class ComplexCompare: + + def takeHightest(self, chunks, comparator): + i = 1 + for j in range(1, len(chunks)): + rlt = comparator(chunks[j], chunks[0]) + if rlt > 0: + i = 0 + if rlt >= 0: + chunks[i], chunks[j] = chunks[j], chunks[i] + i += 1 + return chunks[0:i] + + #以下四个函数是mmseg算法的四种过滤原则，核心算法 + def mmFilter(self, chunks): + def comparator(a,b): + return a.totalWordLength() - b.totalWordLength() + return self.takeHightest(chunks, comparator) + + def lawlFilter(self,chunks): + def comparator(a,b): + return a.averageWordLength() - b.averageWordLength() + return self.takeHightest(chunks,comparator) + + def svmlFilter(self,chunks): + def comparator(a,b): + return b.standardDeviation() - a.standardDeviation() + return self.takeHightest(chunks, comparator) + + def logFreqFilter(self,chunks): + def comparator(a,b): + return a.wordFrequency() - b.wordFrequency() + return self.takeHightest(chunks, comparator) + + +#加载词组字典和字符字典 +dictWord = {} +maxWordLength = 0 + +def loadDictChars(filepath): + global maxWordLength + fsock = open(filepath) + for line in fsock: + freq, word = line.split() + word = word.strip() + dictWord[word] = (len(word), int(freq)) + maxWordLength = len(word) if maxWordLength < len(word) else maxWordLength + fsock.close() + +def loadDictWords(filepath): + global maxWordLength + fsock = open(filepath) + for line in fsock.readlines(): + word = line.strip() + dictWord[word] = (len(word), 0) + maxWordLength = len(word) if maxWordLength < len(word) else maxWordLength + fsock.close() + +#判断该词word是否在字典dictWord中 +def getDictWord(word): + result = dictWord.get(word) + if result: + return Word(word, result[1]) + return None + +#开始加载字典 +def run(): + from os.path import join, dirname + loadDictChars(join(dirname(__file__), 'data', 'chars.dic')) + loadDictWords(join(dirname(__file__), 'data', 'words.dic')) + +class Analysis: + + def __init__(self, text): + self.text = text + self.cacheSize = 3 + self.pos = 0 + self.textLength = len(self.text) + self.cache = [] + self.cacheIndex = 0 + self.complexCompare = ComplexCompare() + + #简单小技巧，用到个缓存，不知道具体有没有用处 + for i in range(self.cacheSize): + self.cache.append([-1, Word()]) + + #控制字典只加载一次 + if not dictWord: + run() + + + def __iter__(self): + while True: + token = self.getNextToken() + if token == None: + raise StopIteration + yield token + + def getNextChar(self): + return self.text[self.pos] + + #判断该字符是否是中文字符（不包括中文标点） + def isChineseChar(self,charater): + return 0x4e00 <= ord(charater) < 0x9fa6 + + #判断是否是ASCII码 + def isASCIIChar(self, ch): + import string + if ch in string.whitespace: + return False + if ch in string.punctuation: + return False + return ch in string.printable + + #得到下一个切割结果 + def getNextToken(self): + while self.pos < self.textLength: + if self.isChineseChar(self.getNextChar()): + token = self.getChineseWords() + else : + token = self.getASCIIWords()+'/' + if len(token) > 0: + return token + return None + + #切割出非中文词 + def getASCIIWords(self): + # Skip pre-word whitespaces and punctuations + #跳过中英文标点和空格 + while self.pos < self.textLength: + ch = self.getNextChar() + if self.isASCIIChar(ch) or self.isChineseChar(ch): + break + self.pos += 1 + #得到英文单词的起始位置 + start = self.pos + + #找出英文单词的结束位置 + while self.pos < self.textLength: + ch = self.getNextChar() + if not self.isASCIIChar(ch): + break + self.pos += 1 + end = self.pos + + #Skip chinese word whitespaces and punctuations + #跳过中英文标点和空格 + while self.pos < self.textLength: + ch = self.getNextChar() + if self.isASCIIChar(ch) or self.isChineseChar(ch): + break + self.pos += 1 + + #返回英文单词 + return self.text[start:end] + + #切割出中文词，并且做处理，用上述4种方法 + def getChineseWords(self): + chunks = self.createChunks() + if len(chunks) > 1: + chunks = self.complexCompare.mmFilter(chunks) + if len(chunks) > 1: + chunks = self.complexCompare.lawlFilter(chunks) + if len(chunks) > 1: + chunks = self.complexCompare.svmlFilter(chunks) + if len(chunks) > 1: + chunks = self.complexCompare.logFreqFilter(chunks) + if len(chunks) == 0 : + return '' + + #最后只有一种切割方法 + word = chunks[0].words + token = "" + length = 0 + for x in word: + if x.length != -1: + token += x.text + "/" + length += len(x.text) + self.pos += length + return token + + #三重循环来枚举切割方法，这里也可以运用递归来实现 + def createChunks(self): + chunks = [] + originalPos = self.pos + words1 = self.getMatchChineseWords() + + for word1 in words1: + self.pos += len(word1.text) + if self.pos < self.textLength: + words2 = self.getMatchChineseWords() + for word2 in words2: + self.pos += len(word2.text) + if self.pos < self.textLength: + words3 = self.getMatchChineseWords() + for word3 in words3: + # print(word3.length, word3.text) + if word3.length == -1: + chunk = Chunk(word1,word2) + # print("Ture") + else : + chunk = Chunk(word1,word2,word3) + chunks.append(chunk) + elif self.pos == self.textLength: + chunks.append(Chunk(word1,word2)) + self.pos -= len(word2.text) + elif self.pos == self.textLength: + chunks.append(Chunk(word1)) + self.pos -= len(word1.text) + + self.pos = originalPos + return chunks + + #运用正向最大匹配算法结合字典来切割中文文本 + def getMatchChineseWords(self): + #use cache,check it + for i in range(self.cacheSize): + if self.cache[i][0] == self.pos: + return self.cache[i][1] + + originalPos = self.pos + words = [] + index = 0 + while self.pos < self.textLength: + if index >= maxWordLength : + break + if not self.isChineseChar(self.getNextChar()): + break + self.pos += 1 + index += 1 + + text = self.text[originalPos:self.pos] + word = getDictWord(text) + if word: + words.append(word) + + self.pos = originalPos + #没有词则放置个‘X’，将文本长度标记为-1 + if not words: + word = Word() + word.length = -1 + word.text = 'X' + words.append(word) + + self.cache[self.cacheIndex] = (self.pos,words) + self.cacheIndex += 1 + if self.cacheIndex >= self.cacheSize: + self.cacheIndex = 0 + return words + + +if __name__=="__main__": + + def cuttest(text): + #cut = Analysis(text) + tmp="" + try: + for word in iter(Analysis(text)): + tmp += word + except Exception as e: + pass + + print(tmp) + print("================================") + + cuttest(u"研究生命来源") + cuttest(u"南京市长江大桥欢迎您") + cuttest(u"请把手抬高一点儿") + cuttest(u"长春市长春节致词。") + cuttest(u"长春市长春药店。") + cuttest(u"我的和服务必在明天做好。") + cuttest(u"我发现有很多人喜欢他。") + cuttest(u"我喜欢看电视剧大长今。") + cuttest(u"半夜给拎起来陪看欧洲杯糊着两眼半晌没搞明白谁和谁踢。") + cuttest(u"李智伟高高兴兴以及王晓薇出去玩，后来智伟和晓薇又单独去玩了。") + cuttest(u"一次性交出去很多钱。 ") + cuttest(u"这是一个伸手不见五指的黑夜。我叫孙悟空，我爱北京，我爱Python和C++。") + cuttest(u"我不喜欢日本和服。") + cuttest(u"雷猴回归人间。") + cuttest(u"工信处女干事每月经过下属科室都要亲口交代24口交换机等技术性器件的安装工作") + cuttest(u"我需要廉租房") + cuttest(u"永和服装饰品有限公司") + cuttest(u"我爱北京天安门") + cuttest(u"abc") + cuttest(u"隐马尔可夫") + cuttest(u"雷猴是个好网站") + cuttest(u"“Microsoft”一词由“MICROcomputer（微型计算机）”和“SOFTware（软件）”两部分组成") + cuttest(u"草泥马和欺实马是今年的流行词汇") + cuttest(u"伊藤洋华堂总府店") + cuttest(u"中国科学院计算技术研究所") + cuttest(u"罗密欧与朱丽叶") + cuttest(u"我购买了道具和服装") + cuttest(u"PS: 我觉得开源有一个好处，就是能够敦促自己不断改进，避免敞帚自珍") + cuttest(u"湖北省石首市") + cuttest(u"总经理完成了这件事情") + cuttest(u"电脑修好了") + cuttest(u"做好了这件事情就一了百了了") + cuttest(u"人们审美的观点是不同的") + cuttest(u"我们买了一个美的空调") + cuttest(u"线程初始化时我们要注意") + cuttest(u"一个分子是由好多原子组织成的") + cuttest(u"祝你马到功成") + cuttest(u"他掉进了无底洞里") + cuttest(u"中国的首都是北京") + cuttest(u"孙君意") + cuttest(u"外交部发言人马朝旭") + cuttest(u"领导人会议和第四届东亚峰会") + cuttest(u"在过去的这五年") + cuttest(u"还需要很长的路要走") + cuttest(u"60周年首都阅兵") + cuttest(u"你好人们审美的观点是不同的") + cuttest(u"买水果然后来世博园") + cuttest(u"买水果然后去世博园") + cuttest(u"但是后来我才知道你是对的") + cuttest(u"存在即合理") + cuttest(u"的的的的的在的的的的就以和和和") + cuttest(u"I love你，不以为耻，反以为rong") + cuttest(u" ") + cuttest(u"") + cuttest(u"hello你好人们审美的观点是不同的") + cuttest(u"很好但主要是基于网页形式") + cuttest(u"hello你好人们审美的观点是不同的") + cuttest(u"为什么我不能拥有想要的生活") + cuttest(u"后来我才") + cuttest(u"此次来中国是为了") + cuttest(u"使用了它就可以解决一些问题") + cuttest(u",使用了它就可以解决一些问题") + cuttest(u"其实使用了它就可以解决一些问题") + cuttest(u"好人使用了它就可以解决一些问题") + cuttest(u"是因为和国家") + cuttest(u"老年搜索还支持") + cuttest(u"干脆就把那部蒙人的闲法给废了拉倒！RT @laoshipukong : 27日，全国人大常委会第三次审议侵权责任法草案，删除了有关医疗损害责任“举证倒置”的规定。在医患纠纷中本已处于弱势地位的消费者由此将陷入万劫不复的境地。 ") + cuttest("2022年12月30日是星期几？") + cuttest("二零二二年十二月三十日是星期几？") \ No newline at end of file diff --git a/speechx/examples/text_lm/path.sh b/speechx/examples/text_lm/path.sh new file mode 100644 index 000000000..541f852c4 --- /dev/null +++ b/speechx/examples/text_lm/path.sh @@ -0,0 +1,4 @@ +MAIN_ROOT=`realpath $PWD/../../../../` +SPEECHX_ROOT=`realpath $MAIN_ROOT/speechx` + +export LC_AL=C diff --git a/speechx/examples/text_lm/run.sh b/speechx/examples/text_lm/run.sh new file mode 100755 index 000000000..0a733b498 --- /dev/null +++ b/speechx/examples/text_lm/run.sh @@ -0,0 +1,24 @@ +#!/bin/bash +set -eo pipefail + +. path.sh + +stage=0 +stop_stage=100 +has_key=true +token_type=word + +. utils/parse_options.sh || exit -1; + +text=data/text + +if [ ! -f $text ]; then + echo "$0: Not find $1"; + exit -1; +fi + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ];then + echo "text tn & wordseg preprocess" + rm -rf ${text}.tn + python3 utils/zh_tn.py --has_key $has_key --token_type $token_type ${text} ${text}.tn +fi \ No newline at end of file diff --git a/speechx/examples/text_lm/utils b/speechx/examples/text_lm/utils new file mode 120000 index 000000000..256f914ab --- /dev/null +++ b/speechx/examples/text_lm/utils @@ -0,0 +1 @@ +../../../utils/ \ No newline at end of file diff --git a/speechx/examples/wfst/.gitignore b/speechx/examples/wfst/.gitignore new file mode 100644 index 000000000..1269488f7 --- /dev/null +++ b/speechx/examples/wfst/.gitignore @@ -0,0 +1 @@ +data diff --git a/speechx/examples/wfst/README.md b/speechx/examples/wfst/README.md new file mode 100644 index 000000000..4f4674a4f --- /dev/null +++ b/speechx/examples/wfst/README.md @@ -0,0 +1,186 @@ +# Built TLG wfst + +## Input +``` +data/local/ +├── dict +│ ├── lexicon.txt +│ └── units.txt +└── lm + ├── heldout + ├── lm.arpa + ├── text + ├── text.no_oov + ├── train + ├── unigram.counts + ├── word.counts + └── wordlist +``` + +``` +==> data/local/dict/lexicon.txt <== +啊 啊 +啊啊啊 啊 啊 啊 +阿 阿 +阿尔 阿 尔 +阿根廷 阿 根 廷 +阿九 阿 九 +阿克 阿 克 +阿拉伯数字 阿 拉 伯 数 字 +阿拉法特 阿 拉 法 特 +阿拉木图 阿 拉 木 图 + +==> data/local/dict/units.txt <== + + +A +B +C +D +E +F +G +H + +==> data/local/lm/heldout <== +而 对 楼市 成交 抑制 作用 最 大 的 限 购 +也 成为 地方 政府 的 眼中 钉 +自 六月 底 呼和浩特 市 率先 宣布 取消 限 购 后 +各地 政府 便 纷纷 跟进 +仅 一 个 多 月 的 时间 里 +除了 北京 上海 广州 深圳 四 个 一 线 城市 和 三亚 之外 +四十六 个 限 购 城市 当中 +四十一 个 已 正式 取消 或 变相 放松 了 限 购 +财政 金融 政策 紧随 其后 而来 +显示 出 了 极 强 的 威力 + +==> data/local/lm/lm.arpa <== + +\data\ +ngram 1=129356 +ngram 2=504661 +ngram 3=123455 + +\1-grams: +-1.531278 +-3.828829 -0.1600094 +-6.157292 + +==> data/local/lm/text <== +BAC009S0002W0122 而 对 楼市 成交 抑制 作用 最 大 的 限 购 +BAC009S0002W0123 也 成为 地方 政府 的 眼中 钉 +BAC009S0002W0124 自 六月 底 呼和浩特 市 率先 宣布 取消 限 购 后 +BAC009S0002W0125 各地 政府 便 纷纷 跟进 +BAC009S0002W0126 仅 一 个 多 月 的 时间 里 +BAC009S0002W0127 除了 北京 上海 广州 深圳 四 个 一 线 城市 和 三亚 之外 +BAC009S0002W0128 四十六 个 限 购 城市 当中 +BAC009S0002W0129 四十一 个 已 正式 取消 或 变相 放松 了 限 购 +BAC009S0002W0130 财政 金融 政策 紧随 其后 而来 +BAC009S0002W0131 显示 出 了 极 强 的 威力 + +==> data/local/lm/text.no_oov <== + 而 对 楼市 成交 抑制 作用 最 大 的 限 购 + 也 成为 地方 政府 的 眼中 钉 + 自 六月 底 呼和浩特 市 率先 宣布 取消 限 购 后 + 各地 政府 便 纷纷 跟进 + 仅 一 个 多 月 的 时间 里 + 除了 北京 上海 广州 深圳 四 个 一 线 城市 和 三亚 之外 + 四十六 个 限 购 城市 当中 + 四十一 个 已 正式 取消 或 变相 放松 了 限 购 + 财政 ���融 政策 紧随 其后 而来 + 显示 出 了 极 强 的 威力 + +==> data/local/lm/train <== +汉莎 不 得 不 通过 这样 的 方式 寻求 新 的 发展 点 +并 计划 朝云 计算 方面 发展 +汉莎 的 基础 设施 部门 拥有 一千四百 名 员工 +媒体 就 曾 披露 这笔 交易 +虽然 双方 已经 正式 签署 了 外包 协议 +但是 这笔 交易 还 需要 得到 反 垄断 部门 的 批准 +陈 黎明 一九八九 年 获得 美国 康乃尔 大学 硕士 学位 +并 于 二零零三 年 顺利 完成 美国 哈佛 商学 院 高级 管理 课程 +曾 在 多家 国际 公司 任职 +拥有 业务 开发 商务 及 企业 治理 + +==> data/local/lm/unigram.counts <== + 57487 的 + 13099 在 + 11862 一 + 11397 了 + 10998 不 + 9913 是 + 7952 有 + 6250 和 + 6152 个 + 5422 将 + +==> data/local/lm/word.counts <== + 57486 的 + 13098 在 + 11861 一 + 11396 了 + 10997 不 + 9912 是 + 7951 有 + 6249 和 + 6151 个 + 5421 将 + +==> data/local/lm/wordlist <== +的 +在 +一 +了 +不 +是 +有 +和 +个 +将 +``` + +## Output + +``` +fstaddselfloops 'echo 4234 |' 'echo 123660 |' +Lexicon and Token FSTs compiling succeeded +arpa2fst --read-symbol-table=data/lang_test/words.txt --keep-symbols=true - +LOG (arpa2fst[5.5.0~1-5a37]:Read():arpa-file-parser.cc:94) Reading \data\ section. +LOG (arpa2fst[5.5.0~1-5a37]:Read():arpa-file-parser.cc:149) Reading \1-grams: section. +LOG (arpa2fst[5.5.0~1-5a37]:Read():arpa-file-parser.cc:149) Reading \2-grams: section. +LOG (arpa2fst[5.5.0~1-5a37]:Read():arpa-file-parser.cc:149) Reading \3-grams: section. +Checking how stochastic G is (the first of these numbers should be small): +fstisstochastic data/lang_test/G.fst +0 -1.14386 +fsttablecompose data/lang_test/L.fst data/lang_test/G.fst +fstminimizeencoded +fstdeterminizestar --use-log=true +fsttablecompose data/lang_test/T.fst data/lang_test/LG.fst +Composing decoding graph TLG.fst succeeded +Aishell build TLG done. +``` + +``` +data/ +├── lang_test +│ ├── G.fst +│ ├── L.fst +│ ├── LG.fst +│ ├── T.fst +│ ├── TLG.fst +│ ├── tokens.txt +│ ├── units.txt +│ └── words.txt +└── local + ├── lang + │ ├── L.fst + │ ├── T.fst + │ ├── tokens.txt + │ ├── units.txt + │ └── words.txt + └── tmp + ├── disambig.list + ├── lexiconp_disambig.txt + ├── lexiconp.txt + └── units.list +``` \ No newline at end of file diff --git a/speechx/examples/wfst/path.sh b/speechx/examples/wfst/path.sh new file mode 100644 index 000000000..a07c1297d --- /dev/null +++ b/speechx/examples/wfst/path.sh @@ -0,0 +1,19 @@ +# This contains the locations of binarys build required for running the examples. + +MAIN_ROOT=`realpath $PWD/../../../` +SPEECHX_ROOT=`realpath $MAIN_ROOT/speechx` + +export LC_AL=C + +# srilm +export LIBLBFGS=${MAIN_ROOT}/tools/liblbfgs-1.10 +export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:-}:${LIBLBFGS}/lib/.libs +export SRILM=${MAIN_ROOT}/tools/srilm +export PATH=${PATH}:${SRILM}/bin:${SRILM}/bin/i686-m64 + +# Kaldi +export KALDI_ROOT=${MAIN_ROOT}/tools/kaldi +[ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh +export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH +[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present, can not using Kaldi!" +[ -f $KALDI_ROOT/tools/config/common_path.sh ] && . $KALDI_ROOT/tools/config/common_path.sh diff --git a/speechx/examples/wfst/run.sh b/speechx/examples/wfst/run.sh new file mode 100755 index 000000000..1354646af --- /dev/null +++ b/speechx/examples/wfst/run.sh @@ -0,0 +1,29 @@ +#!/bin/bash +set -eo pipefail + +. path.sh + +stage=-1 +stop_stage=100 + +. utils/parse_options.sh + +if ! which fstprint ; then + pushd $MAIN_ROOT/tools + make kaldi.done + popd +fi + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + # build T & L + # utils/fst/compile_lexicon_token_fst.sh + utils/fst/compile_lexicon_token_fst.sh \ + data/local/dict data/local/tmp data/local/lang + + # build G & LG & TLG + # utils/fst/make_tlg.sh + utils/fst/make_tlg.sh data/local/lm data/local/lang data/lang_test || exit 1; +fi + +echo "build TLG done." +exit 0 diff --git a/speechx/examples/wfst/utils b/speechx/examples/wfst/utils new file mode 120000 index 000000000..256f914ab --- /dev/null +++ b/speechx/examples/wfst/utils @@ -0,0 +1 @@ +../../../utils/ \ No newline at end of file diff --git a/speechx/speechx/CMakeLists.txt b/speechx/speechx/CMakeLists.txt index 225abee7c..b4da095d8 100644 --- a/speechx/speechx/CMakeLists.txt +++ b/speechx/speechx/CMakeLists.txt @@ -30,4 +30,10 @@ include_directories( ${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_SOURCE_DIR}/decoder ) -add_subdirectory(decoder) \ No newline at end of file +add_subdirectory(decoder) + +include_directories( +${CMAKE_CURRENT_SOURCE_DIR} +${CMAKE_CURRENT_SOURCE_DIR}/websocket +) +add_subdirectory(websocket) diff --git a/speechx/speechx/base/common.h b/speechx/speechx/base/common.h index 7502bc5eb..a9303cbbc 100644 --- a/speechx/speechx/base/common.h +++ b/speechx/speechx/base/common.h @@ -28,8 +28,10 @@ #include #include #include +#include #include #include +#include #include #include "base/basic_types.h" diff --git a/speechx/speechx/decoder/CMakeLists.txt b/speechx/speechx/decoder/CMakeLists.txt index 7cd281b66..06bf4020f 100644 --- a/speechx/speechx/decoder/CMakeLists.txt +++ b/speechx/speechx/decoder/CMakeLists.txt @@ -6,5 +6,7 @@ add_library(decoder STATIC ctc_decoders/decoder_utils.cpp ctc_decoders/path_trie.cpp ctc_decoders/scorer.cpp + ctc_tlg_decoder.cc + recognizer.cc ) -target_link_libraries(decoder PUBLIC kenlm utils fst) \ No newline at end of file +target_link_libraries(decoder PUBLIC kenlm utils fst frontend nnet kaldi-decoder) diff --git a/speechx/speechx/decoder/ctc_beam_search_decoder.cc b/speechx/speechx/decoder/ctc_beam_search_decoder.cc index 5d7a4f77a..5a12c0b50 100644 --- a/speechx/speechx/decoder/ctc_beam_search_decoder.cc +++ b/speechx/speechx/decoder/ctc_beam_search_decoder.cc @@ -92,8 +92,7 @@ void CTCBeamSearch::AdvanceDecode( while (1) { vector> likelihood; vector frame_prob; - bool flag = - decodable->FrameLogLikelihood(num_frame_decoded_, &frame_prob); + bool flag = decodable->FrameLikelihood(num_frame_decoded_, &frame_prob); if (flag == false) break; likelihood.push_back(frame_prob); AdvanceDecoding(likelihood); diff --git a/speechx/speechx/decoder/ctc_beam_search_decoder.h b/speechx/speechx/decoder/ctc_beam_search_decoder.h index 1387eee79..9d0a5d142 100644 --- a/speechx/speechx/decoder/ctc_beam_search_decoder.h +++ b/speechx/speechx/decoder/ctc_beam_search_decoder.h @@ -15,7 +15,7 @@ #include "base/common.h" #include "decoder/ctc_decoders/path_trie.h" #include "decoder/ctc_decoders/scorer.h" -#include "nnet/decodable-itf.h" +#include "kaldi/decoder/decodable-itf.h" #include "util/parse-options.h" #pragma once diff --git a/speechx/speechx/decoder/ctc_tlg_decoder.cc b/speechx/speechx/decoder/ctc_tlg_decoder.cc new file mode 100644 index 000000000..7b720e7ba --- /dev/null +++ b/speechx/speechx/decoder/ctc_tlg_decoder.cc @@ -0,0 +1,65 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "decoder/ctc_tlg_decoder.h" +namespace ppspeech { + +TLGDecoder::TLGDecoder(TLGDecoderOptions opts) { + fst_.reset(fst::Fst::Read(opts.fst_path)); + CHECK(fst_ != nullptr); + word_symbol_table_.reset( + fst::SymbolTable::ReadText(opts.word_symbol_table)); + decoder_.reset(new kaldi::LatticeFasterOnlineDecoder(*fst_, opts.opts)); + decoder_->InitDecoding(); + frame_decoded_size_ = 0; +} + +void TLGDecoder::InitDecoder() { + decoder_->InitDecoding(); + frame_decoded_size_ = 0; +} + +void TLGDecoder::AdvanceDecode( + const std::shared_ptr& decodable) { + while (!decodable->IsLastFrame(frame_decoded_size_)) { + AdvanceDecoding(decodable.get()); + } +} + +void TLGDecoder::AdvanceDecoding(kaldi::DecodableInterface* decodable) { + decoder_->AdvanceDecoding(decodable, 1); + frame_decoded_size_++; +} + +void TLGDecoder::Reset() { + InitDecoder(); + return; +} + +std::string TLGDecoder::GetFinalBestPath() { + decoder_->FinalizeDecoding(); + kaldi::Lattice lat; + kaldi::LatticeWeight weight; + std::vector alignment; + std::vector words_id; + decoder_->GetBestPath(&lat, true); + fst::GetLinearSymbolSequence(lat, &alignment, &words_id, &weight); + std::string words; + for (int32 idx = 0; idx < words_id.size(); ++idx) { + std::string word = word_symbol_table_->Find(words_id[idx]); + words += word; + } + return words; +} +} diff --git a/speechx/speechx/decoder/ctc_tlg_decoder.h b/speechx/speechx/decoder/ctc_tlg_decoder.h new file mode 100644 index 000000000..361c44af5 --- /dev/null +++ b/speechx/speechx/decoder/ctc_tlg_decoder.h @@ -0,0 +1,59 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "base/basic_types.h" +#include "kaldi/decoder/decodable-itf.h" +#include "kaldi/decoder/lattice-faster-online-decoder.h" +#include "util/parse-options.h" + +namespace ppspeech { + +struct TLGDecoderOptions { + kaldi::LatticeFasterDecoderConfig opts; + // todo remove later, add into decode resource + std::string word_symbol_table; + std::string fst_path; + + TLGDecoderOptions() : word_symbol_table(""), fst_path("") {} +}; + +class TLGDecoder { + public: + explicit TLGDecoder(TLGDecoderOptions opts); + void InitDecoder(); + void Decode(); + std::string GetBestPath(); + std::vector> GetNBestPath(); + std::string GetFinalBestPath(); + int NumFrameDecoded(); + int DecodeLikelihoods(const std::vector>& probs, + std::vector& nbest_words); + void AdvanceDecode( + const std::shared_ptr& decodable); + void Reset(); + + private: + void AdvanceDecoding(kaldi::DecodableInterface* decodable); + + std::shared_ptr decoder_; + std::shared_ptr> fst_; + std::shared_ptr word_symbol_table_; + // the frame size which have decoded starts from 0. + int32 frame_decoded_size_; +}; + + +} // namespace ppspeech \ No newline at end of file diff --git a/speechx/speechx/decoder/param.h b/speechx/speechx/decoder/param.h new file mode 100644 index 000000000..cd50ef53b --- /dev/null +++ b/speechx/speechx/decoder/param.h @@ -0,0 +1,94 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "base/common.h" + +#include "decoder/ctc_beam_search_decoder.h" +#include "decoder/ctc_tlg_decoder.h" +#include "frontend/audio/feature_pipeline.h" + +DEFINE_string(cmvn_file, "", "read cmvn"); +DEFINE_double(streaming_chunk, 0.1, "streaming feature chunk size"); +DEFINE_bool(convert2PCM32, true, "audio convert to pcm32"); +DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model"); +DEFINE_string(params_path, "avg_1.jit.pdiparams", "paddle nnet model param"); +DEFINE_string(word_symbol_table, "words.txt", "word symbol table"); +DEFINE_string(graph_path, "TLG", "decoder graph"); +DEFINE_double(acoustic_scale, 1.0, "acoustic scale"); +DEFINE_int32(max_active, 7500, "max active"); +DEFINE_double(beam, 15.0, "decoder beam"); +DEFINE_double(lattice_beam, 7.5, "decoder beam"); +DEFINE_int32(receptive_field_length, + 7, + "receptive field of two CNN(kernel=5) downsampling module."); +DEFINE_int32(downsampling_rate, + 4, + "two CNN(kernel=5) module downsampling rate."); +DEFINE_string(model_output_names, + "save_infer_model/scale_0.tmp_1,save_infer_model/" + "scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/" + "scale_3.tmp_1", + "model output names"); +DEFINE_string(model_cache_names, "5-1-1024,5-1-1024", "model cache names"); + +namespace ppspeech { +// todo refactor later +FeaturePipelineOptions InitFeaturePipelineOptions() { + FeaturePipelineOptions opts; + opts.cmvn_file = FLAGS_cmvn_file; + opts.linear_spectrogram_opts.streaming_chunk = FLAGS_streaming_chunk; + opts.convert2PCM32 = FLAGS_convert2PCM32; + kaldi::FrameExtractionOptions frame_opts; + frame_opts.frame_length_ms = 20; + frame_opts.frame_shift_ms = 10; + frame_opts.remove_dc_offset = false; + frame_opts.window_type = "hanning"; + frame_opts.preemph_coeff = 0.0; + frame_opts.dither = 0.0; + opts.linear_spectrogram_opts.frame_opts = frame_opts; + opts.feature_cache_opts.frame_chunk_size = FLAGS_receptive_field_length; + opts.feature_cache_opts.frame_chunk_stride = FLAGS_downsampling_rate; + return opts; +} + +ModelOptions InitModelOptions() { + ModelOptions model_opts; + model_opts.model_path = FLAGS_model_path; + model_opts.params_path = FLAGS_params_path; + model_opts.cache_shape = FLAGS_model_cache_names; + model_opts.output_names = FLAGS_model_output_names; + return model_opts; +} + +TLGDecoderOptions InitDecoderOptions() { + TLGDecoderOptions decoder_opts; + decoder_opts.word_symbol_table = FLAGS_word_symbol_table; + decoder_opts.fst_path = FLAGS_graph_path; + decoder_opts.opts.max_active = FLAGS_max_active; + decoder_opts.opts.beam = FLAGS_beam; + decoder_opts.opts.lattice_beam = FLAGS_lattice_beam; + return decoder_opts; +} + +RecognizerResource InitRecognizerResoure() { + RecognizerResource resource; + resource.acoustic_scale = FLAGS_acoustic_scale; + resource.feature_pipeline_opts = InitFeaturePipelineOptions(); + resource.model_opts = InitModelOptions(); + resource.tlg_opts = InitDecoderOptions(); + return resource; +} +} \ No newline at end of file diff --git a/speechx/speechx/decoder/recognizer.cc b/speechx/speechx/decoder/recognizer.cc new file mode 100644 index 000000000..2c90ada99 --- /dev/null +++ b/speechx/speechx/decoder/recognizer.cc @@ -0,0 +1,60 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "decoder/recognizer.h" + +namespace ppspeech { + +using kaldi::Vector; +using kaldi::VectorBase; +using kaldi::BaseFloat; +using std::vector; +using kaldi::SubVector; +using std::unique_ptr; + +Recognizer::Recognizer(const RecognizerResource& resource) { + // resource_ = resource; + const FeaturePipelineOptions& feature_opts = resource.feature_pipeline_opts; + feature_pipeline_.reset(new FeaturePipeline(feature_opts)); + std::shared_ptr nnet(new PaddleNnet(resource.model_opts)); + BaseFloat ac_scale = resource.acoustic_scale; + decodable_.reset(new Decodable(nnet, feature_pipeline_, ac_scale)); + decoder_.reset(new TLGDecoder(resource.tlg_opts)); + input_finished_ = false; +} + +void Recognizer::Accept(const Vector& waves) { + feature_pipeline_->Accept(waves); +} + +void Recognizer::Decode() { decoder_->AdvanceDecode(decodable_); } + +std::string Recognizer::GetFinalResult() { + return decoder_->GetFinalBestPath(); +} + +void Recognizer::SetFinished() { + feature_pipeline_->SetFinished(); + input_finished_ = true; +} + +bool Recognizer::IsFinished() { return input_finished_; } + +void Recognizer::Reset() { + feature_pipeline_->Reset(); + decodable_->Reset(); + decoder_->Reset(); +} + +} // namespace ppspeech \ No newline at end of file diff --git a/speechx/speechx/decoder/recognizer.h b/speechx/speechx/decoder/recognizer.h new file mode 100644 index 000000000..9a7e7d11e --- /dev/null +++ b/speechx/speechx/decoder/recognizer.h @@ -0,0 +1,59 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// todo refactor later (SGoat) + +#pragma once + +#include "decoder/ctc_beam_search_decoder.h" +#include "decoder/ctc_tlg_decoder.h" +#include "frontend/audio/feature_pipeline.h" +#include "nnet/decodable.h" +#include "nnet/paddle_nnet.h" + +namespace ppspeech { + +struct RecognizerResource { + FeaturePipelineOptions feature_pipeline_opts; + ModelOptions model_opts; + TLGDecoderOptions tlg_opts; + // CTCBeamSearchOptions beam_search_opts; + kaldi::BaseFloat acoustic_scale; + RecognizerResource() + : acoustic_scale(1.0), + feature_pipeline_opts(), + model_opts(), + tlg_opts() {} +}; + +class Recognizer { + public: + explicit Recognizer(const RecognizerResource& resouce); + void Accept(const kaldi::Vector& waves); + void Decode(); + std::string GetFinalResult(); + void SetFinished(); + bool IsFinished(); + void Reset(); + + private: + // std::shared_ptr resource_; + // RecognizerResource resource_; + std::shared_ptr feature_pipeline_; + std::shared_ptr decodable_; + std::unique_ptr decoder_; + bool input_finished_; +}; + +} // namespace ppspeech \ No newline at end of file diff --git a/speechx/speechx/frontend/audio/CMakeLists.txt b/speechx/speechx/frontend/audio/CMakeLists.txt index 35243b6e3..2d20edf71 100644 --- a/speechx/speechx/frontend/audio/CMakeLists.txt +++ b/speechx/speechx/frontend/audio/CMakeLists.txt @@ -6,6 +6,7 @@ add_library(frontend STATIC linear_spectrogram.cc audio_cache.cc feature_cache.cc + feature_pipeline.cc ) -target_link_libraries(frontend PUBLIC kaldi-matrix) \ No newline at end of file +target_link_libraries(frontend PUBLIC kaldi-matrix kaldi-feat-common) diff --git a/speechx/speechx/frontend/audio/audio_cache.cc b/speechx/speechx/frontend/audio/audio_cache.cc index c3233e595..e8af6668d 100644 --- a/speechx/speechx/frontend/audio/audio_cache.cc +++ b/speechx/speechx/frontend/audio/audio_cache.cc @@ -21,23 +21,30 @@ using kaldi::BaseFloat; using kaldi::VectorBase; using kaldi::Vector; -AudioCache::AudioCache(int buffer_size) +AudioCache::AudioCache(int buffer_size, bool convert2PCM32) : finished_(false), capacity_(buffer_size), size_(0), offset_(0), - timeout_(1) { + timeout_(1), + convert2PCM32_(convert2PCM32) { ring_buffer_.resize(capacity_); } +BaseFloat AudioCache::Convert2PCM32(BaseFloat val) { + return val * (1. / std::pow(2.0, 15)); +} + void AudioCache::Accept(const VectorBase& waves) { std::unique_lock lock(mutex_); while (size_ + waves.Dim() > ring_buffer_.size()) { ready_feed_condition_.wait(lock); } for (size_t idx = 0; idx < waves.Dim(); ++idx) { - int32 buffer_idx = (idx + offset_) % ring_buffer_.size(); + int32 buffer_idx = (idx + offset_ + size_) % ring_buffer_.size(); ring_buffer_[buffer_idx] = waves(idx); + if (convert2PCM32_) + ring_buffer_[buffer_idx] = Convert2PCM32(waves(idx)); } size_ += waves.Dim(); } diff --git a/speechx/speechx/frontend/audio/audio_cache.h b/speechx/speechx/frontend/audio/audio_cache.h index 17e1a8389..a681ef092 100644 --- a/speechx/speechx/frontend/audio/audio_cache.h +++ b/speechx/speechx/frontend/audio/audio_cache.h @@ -23,7 +23,8 @@ namespace ppspeech { // waves cache class AudioCache : public FrontendInterface { public: - explicit AudioCache(int buffer_size = kint16max); + explicit AudioCache(int buffer_size = 1000 * kint16max, + bool convert2PCM32 = true); virtual void Accept(const kaldi::VectorBase& waves); @@ -46,14 +47,17 @@ class AudioCache : public FrontendInterface { } private: + kaldi::BaseFloat Convert2PCM32(kaldi::BaseFloat val); + std::vector ring_buffer_; size_t offset_; // offset in ring_buffer_ size_t size_; // samples in ring_buffer_ now size_t capacity_; // capacity of ring_buffer_ bool finished_; // reach audio end - mutable std::mutex mutex_; + std::mutex mutex_; std::condition_variable ready_feed_condition_; kaldi::int32 timeout_; // millisecond + bool convert2PCM32_; DISALLOW_COPY_AND_ASSIGN(AudioCache); }; diff --git a/speechx/speechx/frontend/audio/cmvn.cc b/speechx/speechx/frontend/audio/cmvn.cc index 4c1ffd6a1..c7e446c92 100644 --- a/speechx/speechx/frontend/audio/cmvn.cc +++ b/speechx/speechx/frontend/audio/cmvn.cc @@ -120,4 +120,4 @@ void CMVN::ApplyCMVN(kaldi::MatrixBase* feats) { ApplyCmvn(stats_, var_norm_, feats); } -} // namespace ppspeech \ No newline at end of file +} // namespace ppspeech diff --git a/speechx/speechx/frontend/audio/feature_cache.cc b/speechx/speechx/frontend/audio/feature_cache.cc index 3f7f6502b..b5768460e 100644 --- a/speechx/speechx/frontend/audio/feature_cache.cc +++ b/speechx/speechx/frontend/audio/feature_cache.cc @@ -23,10 +23,13 @@ using std::vector; using kaldi::SubVector; using std::unique_ptr; -FeatureCache::FeatureCache(int max_size, +FeatureCache::FeatureCache(FeatureCacheOptions opts, unique_ptr base_extractor) { - max_size_ = max_size; + max_size_ = opts.max_size; + frame_chunk_stride_ = opts.frame_chunk_stride; + frame_chunk_size_ = opts.frame_chunk_size; base_extractor_ = std::move(base_extractor); + dim_ = base_extractor_->Dim(); } void FeatureCache::Accept(const kaldi::VectorBase& inputs) { @@ -44,13 +47,14 @@ bool FeatureCache::Read(kaldi::Vector* feats) { std::unique_lock lock(mutex_); while (cache_.empty() && base_extractor_->IsFinished() == false) { - ready_read_condition_.wait(lock); - BaseFloat elapsed = timer.Elapsed() * 1000; - // todo replace 1.0 with timeout_ - if (elapsed > 1.0) { + // todo refactor: wait + // ready_read_condition_.wait(lock); + int32 elapsed = static_cast(timer.Elapsed() * 1000); + // todo replace 1 with timeout_, 1 ms + if (elapsed > 1) { return false; } - usleep(1000); // sleep 1 ms + usleep(100); // sleep 0.1 ms } if (cache_.empty()) return false; feats->Resize(cache_.front().Dim()); @@ -63,25 +67,41 @@ bool FeatureCache::Read(kaldi::Vector* feats) { // read all data from base_feature_extractor_ into cache_ bool FeatureCache::Compute() { // compute and feed - Vector feature_chunk; - bool result = base_extractor_->Read(&feature_chunk); + Vector feature; + bool result = base_extractor_->Read(&feature); + if (result == false || feature.Dim() == 0) return false; + int32 joint_len = feature.Dim() + remained_feature_.Dim(); + int32 num_chunk = + ((joint_len / dim_) - frame_chunk_size_) / frame_chunk_stride_ + 1; - std::unique_lock lock(mutex_); - while (cache_.size() >= max_size_) { - ready_feed_condition_.wait(lock); - } + Vector joint_feature(joint_len); + joint_feature.Range(0, remained_feature_.Dim()) + .CopyFromVec(remained_feature_); + joint_feature.Range(remained_feature_.Dim(), feature.Dim()) + .CopyFromVec(feature); - // feed cache - if (feature_chunk.Dim() != 0) { + for (int chunk_idx = 0; chunk_idx < num_chunk; ++chunk_idx) { + int32 start = chunk_idx * frame_chunk_stride_ * dim_; + Vector feature_chunk(frame_chunk_size_ * dim_); + SubVector tmp(joint_feature.Data() + start, + frame_chunk_size_ * dim_); + feature_chunk.CopyFromVec(tmp); + + std::unique_lock lock(mutex_); + while (cache_.size() >= max_size_) { + ready_feed_condition_.wait(lock); + } + + // feed cache cache_.push(feature_chunk); + ready_read_condition_.notify_one(); } - ready_read_condition_.notify_one(); + int32 remained_feature_len = + joint_len - num_chunk * frame_chunk_stride_ * dim_; + remained_feature_.Resize(remained_feature_len); + remained_feature_.CopyFromVec(joint_feature.Range( + frame_chunk_stride_ * num_chunk * dim_, remained_feature_len)); return result; } -void Reset() { - // std::lock_guard lock(mutex_); - return; -} - } // namespace ppspeech \ No newline at end of file diff --git a/speechx/speechx/frontend/audio/feature_cache.h b/speechx/speechx/frontend/audio/feature_cache.h index 99961b5e2..607f72c08 100644 --- a/speechx/speechx/frontend/audio/feature_cache.h +++ b/speechx/speechx/frontend/audio/feature_cache.h @@ -19,10 +19,18 @@ namespace ppspeech { +struct FeatureCacheOptions { + int32 max_size; + int32 frame_chunk_size; + int32 frame_chunk_stride; + FeatureCacheOptions() + : max_size(kint16max), frame_chunk_size(1), frame_chunk_stride(1) {} +}; + class FeatureCache : public FrontendInterface { public: explicit FeatureCache( - int32 max_size = kint16max, + FeatureCacheOptions opts, std::unique_ptr base_extractor = NULL); // Feed feats or waves @@ -32,12 +40,15 @@ class FeatureCache : public FrontendInterface { virtual bool Read(kaldi::Vector* feats); // feat dim - virtual size_t Dim() const { return base_extractor_->Dim(); } + virtual size_t Dim() const { return dim_; } virtual void SetFinished() { + // std::unique_lock lock(mutex_); base_extractor_->SetFinished(); + LOG(INFO) << "set finished"; // read the last chunk data Compute(); + // ready_feed_condition_.notify_one(); } virtual bool IsFinished() const { return base_extractor_->IsFinished(); } @@ -52,9 +63,13 @@ class FeatureCache : public FrontendInterface { private: bool Compute(); + int32 dim_; size_t max_size_; - std::unique_ptr base_extractor_; + int32 frame_chunk_size_; + int32 frame_chunk_stride_; + kaldi::Vector remained_feature_; + std::unique_ptr base_extractor_; std::mutex mutex_; std::queue> cache_; std::condition_variable ready_feed_condition_; diff --git a/speechx/speechx/frontend/audio/feature_pipeline.cc b/speechx/speechx/frontend/audio/feature_pipeline.cc new file mode 100644 index 000000000..86eca2e05 --- /dev/null +++ b/speechx/speechx/frontend/audio/feature_pipeline.cc @@ -0,0 +1,36 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "frontend/audio/feature_pipeline.h" + +namespace ppspeech { + +using std::unique_ptr; + +FeaturePipeline::FeaturePipeline(const FeaturePipelineOptions& opts) { + unique_ptr data_source( + new ppspeech::AudioCache(1000 * kint16max, opts.convert2PCM32)); + + unique_ptr linear_spectrogram( + new ppspeech::LinearSpectrogram(opts.linear_spectrogram_opts, + std::move(data_source))); + + unique_ptr cmvn( + new ppspeech::CMVN(opts.cmvn_file, std::move(linear_spectrogram))); + + base_extractor_.reset( + new ppspeech::FeatureCache(opts.feature_cache_opts, std::move(cmvn))); +} + +} // ppspeech \ No newline at end of file diff --git a/speechx/speechx/frontend/audio/feature_pipeline.h b/speechx/speechx/frontend/audio/feature_pipeline.h new file mode 100644 index 000000000..7bd6c84f9 --- /dev/null +++ b/speechx/speechx/frontend/audio/feature_pipeline.h @@ -0,0 +1,57 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// todo refactor later (SGoat) + +#pragma once + +#include "frontend/audio/audio_cache.h" +#include "frontend/audio/data_cache.h" +#include "frontend/audio/feature_cache.h" +#include "frontend/audio/frontend_itf.h" +#include "frontend/audio/linear_spectrogram.h" +#include "frontend/audio/normalizer.h" + +namespace ppspeech { + +struct FeaturePipelineOptions { + std::string cmvn_file; + bool convert2PCM32; + LinearSpectrogramOptions linear_spectrogram_opts; + FeatureCacheOptions feature_cache_opts; + FeaturePipelineOptions() + : cmvn_file(""), + convert2PCM32(false), + linear_spectrogram_opts(), + feature_cache_opts() {} +}; + +class FeaturePipeline : public FrontendInterface { + public: + explicit FeaturePipeline(const FeaturePipelineOptions& opts); + virtual void Accept(const kaldi::VectorBase& waves) { + base_extractor_->Accept(waves); + } + virtual bool Read(kaldi::Vector* feats) { + return base_extractor_->Read(feats); + } + virtual size_t Dim() const { return base_extractor_->Dim(); } + virtual void SetFinished() { base_extractor_->SetFinished(); } + virtual bool IsFinished() const { return base_extractor_->IsFinished(); } + virtual void Reset() { base_extractor_->Reset(); } + + private: + std::unique_ptr base_extractor_; +}; +} \ No newline at end of file diff --git a/speechx/speechx/frontend/audio/linear_spectrogram.cc b/speechx/speechx/frontend/audio/linear_spectrogram.cc index d6ae3d012..9ef5e7664 100644 --- a/speechx/speechx/frontend/audio/linear_spectrogram.cc +++ b/speechx/speechx/frontend/audio/linear_spectrogram.cc @@ -52,16 +52,16 @@ bool LinearSpectrogram::Read(Vector* feats) { if (flag == false || input_feats.Dim() == 0) return false; int32 feat_len = input_feats.Dim(); - int32 left_len = reminded_wav_.Dim(); + int32 left_len = remained_wav_.Dim(); Vector waves(feat_len + left_len); - waves.Range(0, left_len).CopyFromVec(reminded_wav_); + waves.Range(0, left_len).CopyFromVec(remained_wav_); waves.Range(left_len, feat_len).CopyFromVec(input_feats); Compute(waves, feats); int32 frame_shift = opts_.frame_opts.WindowShift(); int32 num_frames = kaldi::NumFrames(waves.Dim(), opts_.frame_opts); int32 left_samples = waves.Dim() - frame_shift * num_frames; - reminded_wav_.Resize(left_samples); - reminded_wav_.CopyFromVec( + remained_wav_.Resize(left_samples); + remained_wav_.CopyFromVec( waves.Range(frame_shift * num_frames, left_samples)); return true; } diff --git a/speechx/speechx/frontend/audio/linear_spectrogram.h b/speechx/speechx/frontend/audio/linear_spectrogram.h index 896c494dd..2764b7cf4 100644 --- a/speechx/speechx/frontend/audio/linear_spectrogram.h +++ b/speechx/speechx/frontend/audio/linear_spectrogram.h @@ -25,12 +25,12 @@ struct LinearSpectrogramOptions { kaldi::FrameExtractionOptions frame_opts; kaldi::BaseFloat streaming_chunk; // second - LinearSpectrogramOptions() : streaming_chunk(0.36), frame_opts() {} + LinearSpectrogramOptions() : streaming_chunk(0.1), frame_opts() {} void Register(kaldi::OptionsItf* opts) { opts->Register("streaming-chunk", &streaming_chunk, - "streaming chunk size, default: 0.36 sec"); + "streaming chunk size, default: 0.1 sec"); frame_opts.Register(opts); } }; @@ -46,7 +46,10 @@ class LinearSpectrogram : public FrontendInterface { virtual size_t Dim() const { return dim_; } virtual void SetFinished() { base_extractor_->SetFinished(); } virtual bool IsFinished() const { return base_extractor_->IsFinished(); } - virtual void Reset() { base_extractor_->Reset(); } + virtual void Reset() { + base_extractor_->Reset(); + remained_wav_.Resize(0); + } private: bool Compute(const kaldi::Vector& waves, @@ -57,7 +60,7 @@ class LinearSpectrogram : public FrontendInterface { kaldi::BaseFloat hanning_window_energy_; LinearSpectrogramOptions opts_; std::unique_ptr base_extractor_; - kaldi::Vector reminded_wav_; + kaldi::Vector remained_wav_; int chunk_sample_size_; DISALLOW_COPY_AND_ASSIGN(LinearSpectrogram); }; diff --git a/speechx/speechx/kaldi/CMakeLists.txt b/speechx/speechx/kaldi/CMakeLists.txt index 414a6fa0c..6f7398cd1 100644 --- a/speechx/speechx/kaldi/CMakeLists.txt +++ b/speechx/speechx/kaldi/CMakeLists.txt @@ -4,3 +4,6 @@ add_subdirectory(base) add_subdirectory(util) add_subdirectory(feat) add_subdirectory(matrix) +add_subdirectory(lat) +add_subdirectory(fstext) +add_subdirectory(decoder) diff --git a/speechx/speechx/kaldi/decoder/CMakeLists.txt b/speechx/speechx/kaldi/decoder/CMakeLists.txt new file mode 100644 index 000000000..f1ee6eabb --- /dev/null +++ b/speechx/speechx/kaldi/decoder/CMakeLists.txt @@ -0,0 +1,6 @@ + +add_library(kaldi-decoder +lattice-faster-decoder.cc +lattice-faster-online-decoder.cc +) +target_link_libraries(kaldi-decoder PUBLIC kaldi-lat) diff --git a/speechx/speechx/nnet/decodable-itf.h b/speechx/speechx/kaldi/decoder/decodable-itf.h similarity index 98% rename from speechx/speechx/nnet/decodable-itf.h rename to speechx/speechx/kaldi/decoder/decodable-itf.h index 8e9a5a72a..b8ce9143e 100644 --- a/speechx/speechx/nnet/decodable-itf.h +++ b/speechx/speechx/kaldi/decoder/decodable-itf.h @@ -121,7 +121,7 @@ class DecodableInterface { /// decoding-from-matrix setting where we want to allow the last delta or /// LDA /// features to be flushed out for compatibility with the baseline setup. - virtual bool IsLastFrame(int32 frame) const = 0; + virtual bool IsLastFrame(int32 frame) = 0; /// The call NumFramesReady() will return the number of frames currently /// available @@ -143,7 +143,7 @@ class DecodableInterface { /// this is for compatibility with OpenFst). virtual int32 NumIndices() const = 0; - virtual bool FrameLogLikelihood( + virtual bool FrameLikelihood( int32 frame, std::vector* likelihood) = 0; diff --git a/speechx/speechx/kaldi/decoder/lattice-faster-decoder.cc b/speechx/speechx/kaldi/decoder/lattice-faster-decoder.cc index 42d1d2af4..ae6b71600 100644 --- a/speechx/speechx/kaldi/decoder/lattice-faster-decoder.cc +++ b/speechx/speechx/kaldi/decoder/lattice-faster-decoder.cc @@ -1007,14 +1007,10 @@ template class LatticeFasterDecoderTpl, decoder::StdToken> template class LatticeFasterDecoderTpl, decoder::StdToken >; template class LatticeFasterDecoderTpl, decoder::StdToken >; -template class LatticeFasterDecoderTpl; -template class LatticeFasterDecoderTpl; template class LatticeFasterDecoderTpl , decoder::BackpointerToken>; template class LatticeFasterDecoderTpl, decoder::BackpointerToken >; template class LatticeFasterDecoderTpl, decoder::BackpointerToken >; -template class LatticeFasterDecoderTpl; -template class LatticeFasterDecoderTpl; } // end namespace kaldi. diff --git a/speechx/speechx/kaldi/decoder/lattice-faster-decoder.h b/speechx/speechx/kaldi/decoder/lattice-faster-decoder.h index 2016ad571..d142a8c7d 100644 --- a/speechx/speechx/kaldi/decoder/lattice-faster-decoder.h +++ b/speechx/speechx/kaldi/decoder/lattice-faster-decoder.h @@ -23,11 +23,10 @@ #ifndef KALDI_DECODER_LATTICE_FASTER_DECODER_H_ #define KALDI_DECODER_LATTICE_FASTER_DECODER_H_ -#include "decoder/grammar-fst.h" #include "fst/fstlib.h" #include "fst/memory.h" #include "fstext/fstext-lib.h" -#include "itf/decodable-itf.h" +#include "decoder/decodable-itf.h" #include "lat/determinize-lattice-pruned.h" #include "lat/kaldi-lattice.h" #include "util/hash-list.h" diff --git a/speechx/speechx/kaldi/decoder/lattice-faster-online-decoder.cc b/speechx/speechx/kaldi/decoder/lattice-faster-online-decoder.cc index ebdace7e8..b5261503c 100644 --- a/speechx/speechx/kaldi/decoder/lattice-faster-online-decoder.cc +++ b/speechx/speechx/kaldi/decoder/lattice-faster-online-decoder.cc @@ -278,8 +278,8 @@ bool LatticeFasterOnlineDecoderTpl::GetRawLatticePruned( template class LatticeFasterOnlineDecoderTpl >; template class LatticeFasterOnlineDecoderTpl >; template class LatticeFasterOnlineDecoderTpl >; -template class LatticeFasterOnlineDecoderTpl; -template class LatticeFasterOnlineDecoderTpl; +//template class LatticeFasterOnlineDecoderTpl; +//template class LatticeFasterOnlineDecoderTpl; } // end namespace kaldi. diff --git a/speechx/speechx/kaldi/decoder/lattice-faster-online-decoder.h b/speechx/speechx/kaldi/decoder/lattice-faster-online-decoder.h index 8b10996fd..f57368a44 100644 --- a/speechx/speechx/kaldi/decoder/lattice-faster-online-decoder.h +++ b/speechx/speechx/kaldi/decoder/lattice-faster-online-decoder.h @@ -30,7 +30,7 @@ #include "util/stl-utils.h" #include "util/hash-list.h" #include "fst/fstlib.h" -#include "itf/decodable-itf.h" +#include "decoder/decodable-itf.h" #include "fstext/fstext-lib.h" #include "lat/determinize-lattice-pruned.h" #include "lat/kaldi-lattice.h" diff --git a/speechx/speechx/kaldi/fstext/CMakeLists.txt b/speechx/speechx/kaldi/fstext/CMakeLists.txt new file mode 100644 index 000000000..af91fd985 --- /dev/null +++ b/speechx/speechx/kaldi/fstext/CMakeLists.txt @@ -0,0 +1,5 @@ + +add_library(kaldi-fstext +kaldi-fst-io.cc +) +target_link_libraries(kaldi-fstext PUBLIC kaldi-util) diff --git a/speechx/speechx/kaldi/fstext/determinize-lattice-inl.h b/speechx/speechx/kaldi/fstext/determinize-lattice-inl.h new file mode 100644 index 000000000..0bfbc8f41 --- /dev/null +++ b/speechx/speechx/kaldi/fstext/determinize-lattice-inl.h @@ -0,0 +1,1357 @@ +// fstext/determinize-lattice-inl.h + +// Copyright 2009-2012 Microsoft Corporation +// 2012-2013 Johns Hopkins University (Author: Daniel Povey) + +// See ../../COPYING for clarification regarding multiple authors +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED +// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE, +// MERCHANTABLITY OR NON-INFRINGEMENT. +// See the Apache 2 License for the specific language governing permissions and +// limitations under the License. + +#ifndef KALDI_FSTEXT_DETERMINIZE_LATTICE_INL_H_ +#define KALDI_FSTEXT_DETERMINIZE_LATTICE_INL_H_ +// Do not include this file directly. It is included by determinize-lattice.h + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace fst { + +// This class maps back and forth from/to integer id's to sequences of strings. +// used in determinization algorithm. It is constructed in such a way that +// finding the string-id of the successor of (string, next-label) has constant +// time. + +// Note: class IntType, typically int32, is the type of the element in the +// string (typically a template argument of the CompactLatticeWeightTpl). + +template +class LatticeStringRepository { + public: + struct Entry { + const Entry *parent; // NULL for empty string. + IntType i; + inline bool operator==(const Entry &other) const { + return (parent == other.parent && i == other.i); + } + Entry() {} + Entry(const Entry &e) : parent(e.parent), i(e.i) {} + }; + // Note: all Entry* pointers returned in function calls are + // owned by the repository itself, not by the caller! + + // Interface guarantees empty string is NULL. + inline const Entry *EmptyString() { return NULL; } + + // Returns string of "parent" with i appended. Pointer + // owned by repository + const Entry *Successor(const Entry *parent, IntType i) { + new_entry_->parent = parent; + new_entry_->i = i; + + std::pair pr = set_.insert(new_entry_); + if (pr.second) { // Was successfully inserted (was not there). We need to + // replace the element we inserted, which resides on the + // stack, with one from the heap. + const Entry *ans = new_entry_; + new_entry_ = new Entry(); + return ans; + } else { // Was not inserted because an equivalent Entry already + // existed. + return *pr.first; + } + } + + const Entry *Concatenate(const Entry *a, const Entry *b) { + if (a == NULL) + return b; + else if (b == NULL) + return a; + std::vector v; + ConvertToVector(b, &v); + const Entry *ans = a; + for (size_t i = 0; i < v.size(); i++) ans = Successor(ans, v[i]); + return ans; + } + const Entry *CommonPrefix(const Entry *a, const Entry *b) { + std::vector a_vec, b_vec; + ConvertToVector(a, &a_vec); + ConvertToVector(b, &b_vec); + const Entry *ans = NULL; + for (size_t i = 0; + i < a_vec.size() && i < b_vec.size() && a_vec[i] == b_vec[i]; i++) + ans = Successor(ans, a_vec[i]); + return ans; + } + + // removes any elements from b that are not part of + // a common prefix with a. + void ReduceToCommonPrefix(const Entry *a, std::vector *b) { + size_t a_size = Size(a), b_size = b->size(); + while (a_size > b_size) { + a = a->parent; + a_size--; + } + if (b_size > a_size) b_size = a_size; + typename std::vector::iterator b_begin = b->begin(); + while (a_size != 0) { + if (a->i != *(b_begin + a_size - 1)) b_size = a_size - 1; + a = a->parent; + a_size--; + } + if (b_size != b->size()) b->resize(b_size); + } + + // removes the first n elements of a. + const Entry *RemovePrefix(const Entry *a, size_t n) { + if (n == 0) return a; + std::vector a_vec; + ConvertToVector(a, &a_vec); + assert(a_vec.size() >= n); + const Entry *ans = NULL; + for (size_t i = n; i < a_vec.size(); i++) ans = Successor(ans, a_vec[i]); + return ans; + } + + // Returns true if a is a prefix of b. If a is prefix of b, + // time taken is |b| - |a|. Else, time taken is |b|. + bool IsPrefixOf(const Entry *a, const Entry *b) const { + if (a == NULL) return true; // empty string prefix of all. + if (a == b) return true; + if (b == NULL) return false; + return IsPrefixOf(a, b->parent); + } + + inline size_t Size(const Entry *entry) const { + size_t ans = 0; + while (entry != NULL) { + ans++; + entry = entry->parent; + } + return ans; + } + + void ConvertToVector(const Entry *entry, std::vector *out) const { + size_t length = Size(entry); + out->resize(length); + if (entry != NULL) { + typename std::vector::reverse_iterator iter = out->rbegin(); + while (entry != NULL) { + *iter = entry->i; + entry = entry->parent; + ++iter; + } + } + } + + const Entry *ConvertFromVector(const std::vector &vec) { + const Entry *e = NULL; + for (size_t i = 0; i < vec.size(); i++) e = Successor(e, vec[i]); + return e; + } + + LatticeStringRepository() { new_entry_ = new Entry; } + + void Destroy() { + for (typename SetType::iterator iter = set_.begin(); iter != set_.end(); + ++iter) + delete *iter; + SetType tmp; + tmp.swap(set_); + if (new_entry_) { + delete new_entry_; + new_entry_ = NULL; + } + } + + // Rebuild will rebuild this object, guaranteeing only + // to preserve the Entry values that are in the vector pointed + // to (this list does not have to be unique). The point of + // this is to save memory. + void Rebuild(const std::vector &to_keep) { + SetType tmp_set; + for (typename std::vector::const_iterator iter = + to_keep.begin(); + iter != to_keep.end(); ++iter) + RebuildHelper(*iter, &tmp_set); + // Now delete all elems not in tmp_set. + for (typename SetType::iterator iter = set_.begin(); iter != set_.end(); + ++iter) { + if (tmp_set.count(*iter) == 0) + delete (*iter); // delete the Entry; not needed. + } + set_.swap(tmp_set); + } + + ~LatticeStringRepository() { Destroy(); } + int32 MemSize() const { + return set_.size() * sizeof(Entry) * 2; // this is a lower bound + // on the size this structure might take. + } + + private: + class EntryKey { // Hash function object. + public: + inline size_t operator()(const Entry *entry) const { + size_t prime = 49109; + return static_cast(entry->i) + + prime * reinterpret_cast(entry->parent); + } + }; + class EntryEqual { + public: + inline bool operator()(const Entry *e1, const Entry *e2) const { + return (*e1 == *e2); + } + }; + typedef std::unordered_set SetType; + + void RebuildHelper(const Entry *to_add, SetType *tmp_set) { + while (true) { + if (to_add == NULL) return; + typename SetType::iterator iter = tmp_set->find(to_add); + if (iter == tmp_set->end()) { // not in tmp_set. + tmp_set->insert(to_add); + to_add = to_add->parent; // and loop. + } else { + return; + } + } + } + + KALDI_DISALLOW_COPY_AND_ASSIGN(LatticeStringRepository); + Entry *new_entry_; // We always have a pre-allocated Entry ready to use, + // to avoid unnecessary news and deletes. + SetType set_; +}; + +// class LatticeDeterminizer is templated on the same types that +// CompactLatticeWeight is templated on: the base weight (Weight), typically +// LatticeWeightTpl etc. but could also be e.g. TropicalWeight, and the +// IntType, typically int32, used for the output symbols in the compact +// representation of strings [note: the output symbols would usually be +// p.d.f. id's in the anticipated use of this code] It has a special requirement +// on the Weight type: that there should be a Compare function on the weights +// such that Compare(w1, w2) returns -1 if w1 < w2, 0 if w1 == w2, and +1 if w1 +// > w2. This requires that there be a total order on the weights. + +template +class LatticeDeterminizer { + public: + // Output to Gallic acceptor (so the strings go on weights, and there is a 1-1 + // correspondence between our states and the states in ofst. If destroy == + // true, release memory as we go (but we cannot output again). + + typedef CompactLatticeWeightTpl CompactWeight; + typedef ArcTpl + CompactArc; // arc in compact, acceptor form of lattice + typedef ArcTpl Arc; // arc in non-compact version of lattice + + // Output to standard FST with CompactWeightTpl as its weight type + // (the weight stores the original output-symbol strings). If destroy == + // true, release memory as we go (but we cannot output again). + void Output(MutableFst *ofst, bool destroy = true) { + assert(determinized_); + typedef typename Arc::StateId StateId; + StateId nStates = static_cast(output_arcs_.size()); + if (destroy) FreeMostMemory(); + ofst->DeleteStates(); + ofst->SetStart(kNoStateId); + if (nStates == 0) { + return; + } + for (StateId s = 0; s < nStates; s++) { + OutputStateId news = ofst->AddState(); + assert(news == s); + } + ofst->SetStart(0); + // now process transitions. + for (StateId this_state = 0; this_state < nStates; this_state++) { + std::vector &this_vec(output_arcs_[this_state]); + typename std::vector::const_iterator iter = this_vec.begin(), + end = this_vec.end(); + + for (; iter != end; ++iter) { + const TempArc &temp_arc(*iter); + CompactArc new_arc; + std::vector