Merge branch 'develop' of github.com:PaddlePaddle/PaddleSpeech into add_vctk_hifigan

pull/1587/head
TianYuan 3 years ago
commit 9497c93fb0

5
.gitignore vendored

@ -14,6 +14,7 @@
*.whl
*.egg-info
build
*output/
docs/build/
docs/topic/ctc/warp-ctc/
@ -33,6 +34,4 @@ tools/activate_python.sh
tools/miniconda.sh
tools/CRF++-0.58/
speechx/fc_patch/
*output/
speechx/fc_patch/

@ -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).*(\.cpp|\.cc|\.h|\.py)$
exclude: (?=speechx/speechx/kaldi|speechx/patch).*(\.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).*(\.cpp|\.cc|\.h|\.py)$
exclude: (?=third_party|pypinyin|speechx/speechx/kaldi|speechx/patch).*(\.cpp|\.cc|\.h|\.py)$
- repo: https://github.com/asottile/reorder_python_imports
rev: v2.4.0
hooks:

@ -1,4 +1,13 @@
# Changelog
Date: 2022-3-08, Author: yt605155624.
Add features to: T2S:
- Add aishell3 hifigan egs.
- PRLink: https://github.com/PaddlePaddle/PaddleSpeech/pull/1545
Date: 2022-3-08, Author: yt605155624.
Add features to: T2S:
- Add vctk hifigan egs.
- PRLink: https://github.com/PaddlePaddle/PaddleSpeech/pull/1544
Date: 2022-1-29, Author: yt605155624.
Add features to: T2S:

@ -178,7 +178,7 @@ Via the easy-to-use, efficient, flexible and scalable implementation, our vision
<!---
2021.12.14: We would like to have an online courses to introduce basics and research of speech, as well as code practice with `paddlespeech`. Please pay attention to our [Calendar](https://www.paddlepaddle.org.cn/live).
--->
- 🤗 2021.12.14: Our PaddleSpeech [ASR](https://huggingface.co/spaces/KPatrick/PaddleSpeechASR) and [TTS](https://huggingface.co/spaces/akhaliq/paddlespeech) Demos on Hugging Face Spaces are available!
- 🤗 2021.12.14: Our PaddleSpeech [ASR](https://huggingface.co/spaces/KPatrick/PaddleSpeechASR) and [TTS](https://huggingface.co/spaces/KPatrick/PaddleSpeechTTS) Demos on Hugging Face Spaces are available!
- 👏🏻 2021.12.10: PaddleSpeech CLI is available for Audio Classification, Automatic Speech Recognition, Speech Translation (English to Chinese) and Text-to-Speech.
### Community
@ -207,6 +207,7 @@ paddlespeech cls --input input.wav
```shell
paddlespeech asr --lang zh --input input_16k.wav
```
- web demo for Automatic Speech Recognition is integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See Demo: [ASR Demo](https://huggingface.co/spaces/KPatrick/PaddleSpeechASR)
**Speech Translation** (English to Chinese)
(not support for Mac and Windows now)
@ -218,7 +219,7 @@ paddlespeech st --input input_16k.wav
```shell
paddlespeech tts --input "你好,欢迎使用飞桨深度学习框架!" --output output.wav
```
- web demo for Text to Speech is integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See Demo: [TTS Demo](https://huggingface.co/spaces/akhaliq/paddlespeech)
- web demo for Text to Speech is integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See Demo: [TTS Demo](https://huggingface.co/spaces/KPatrick/PaddleSpeechTTS)
**Text Postprocessing**
- Punctuation Restoration
@ -397,9 +398,9 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
</tr>
<tr>
<td >HiFiGAN</td>
<td >CSMSC</td>
<td >LJSpeech / VCTK / CSMSC / AISHELL-3</td>
<td>
<a href = "./examples/csmsc/voc5">HiFiGAN-csmsc</a>
<a href = "./examples/ljspeech/voc5">HiFiGAN-ljspeech</a> / <a href = "./examples/vctk/voc5">HiFiGAN-vctk</a> / <a href = "./examples/csmsc/voc5">HiFiGAN-csmsc</a> / <a href = "./examples/aishell3/voc5">HiFiGAN-aishell3</a>
</td>
</tr>
<tr>
@ -573,7 +574,6 @@ You are warmly welcome to submit questions in [discussions](https://github.com/P
- Many thanks to [yeyupiaoling](https://github.com/yeyupiaoling)/[PPASR](https://github.com/yeyupiaoling/PPASR)/[PaddlePaddle-DeepSpeech](https://github.com/yeyupiaoling/PaddlePaddle-DeepSpeech)/[VoiceprintRecognition-PaddlePaddle](https://github.com/yeyupiaoling/VoiceprintRecognition-PaddlePaddle)/[AudioClassification-PaddlePaddle](https://github.com/yeyupiaoling/AudioClassification-PaddlePaddle) for years of attention, constructive advice and great help.
- Many thanks to [AK391](https://github.com/AK391) for TTS web demo on Huggingface Spaces using Gradio.
- Many thanks to [mymagicpower](https://github.com/mymagicpower) for the Java implementation of ASR upon [short](https://github.com/mymagicpower/AIAS/tree/main/3_audio_sdks/asr_sdk) and [long](https://github.com/mymagicpower/AIAS/tree/main/3_audio_sdks/asr_long_audio_sdk) audio files.
- Many thanks to [JiehangXie](https://github.com/JiehangXie)/[PaddleBoBo](https://github.com/JiehangXie/PaddleBoBo) for developing Virtual Uploader(VUP)/Virtual YouTuber(VTuber) with PaddleSpeech TTS function.
- Many thanks to [745165806](https://github.com/745165806)/[PaddleSpeechTask](https://github.com/745165806/PaddleSpeechTask) for contributing Punctuation Restoration model.

@ -392,9 +392,9 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块:文本前端、声
</tr>
<tr>
<td >HiFiGAN</td>
<td >CSMSC</td>
<td >LJSpeech / VCTK / CSMSC / AISHELL-3</td>
<td>
<a href = "./examples/csmsc/voc5">HiFiGAN-csmsc</a>
<a href = "./examples/ljspeech/voc5">HiFiGAN-ljspeech</a> / <a href = "./examples/vctk/voc5">HiFiGAN-vctk</a> / <a href = "./examples/csmsc/voc5">HiFiGAN-csmsc</a> / <a href = "./examples/aishell3/voc5">HiFiGAN-aishell3</a>
</td>
</tr>
<tr>

@ -0,0 +1,171 @@
([简体中文](./README_cn.md)|English)
# Audio Searching
## Introduction
As the Internet continues to evolve, unstructured data such as emails, social media photos, live videos, and customer service voice calls have become increasingly common. If we want to process the data on a computer, we need to use embedding technology to transform the data into vector and store, index, and query it
However, when there is a large amount of data, such as hundreds of millions of audio tracks, it is more difficult to do a similarity search. The exhaustive method is feasible, but very time consuming. For this scenario, this demo will introduce how to build an audio similarity retrieval system using the open source vector database Milvus
Audio retrieval (speech, music, speaker, etc.) enables querying and finding similar sounds (or the same speaker) in a large amount of audio data. The audio similarity retrieval system can be used to identify similar sound effects, minimize intellectual property infringement, quickly retrieve the voice print library, and help enterprises control fraud and identity theft. Audio retrieval also plays an important role in the classification and statistical analysis of audio data
In this demo, you will learn how to build an audio retrieval system to retrieve similar sound snippets. The uploaded audio clips are converted into vector data using paddlespeech-based pre-training models (audio classification model, speaker recognition model, etc.) and stored in Milvus. Milvus automatically generates a unique ID for each vector, then stores the ID and the corresponding audio information (audio ID, audio speaker ID, etc.) in MySQL to complete the library construction. During retrieval, users upload test audio to obtain vector, and then conduct vector similarity search in Milvus. The retrieval result returned by Milvus is vector ID, and the corresponding audio information can be queried in MySQL by ID
![Workflow of an audio searching system](./img/audio_searching.png)
Notethis demo uses the [CN-Celeb](http://openslr.org/82/) dataset of at least 650,000 audio entries and 3000 speakers to build the audio vector library, which is then retrieved using a preset distance calculation. The dataset can also use other, Adjust as needed, e.g. Librispeech, VoxCeleb, UrbanSound, GloVe, MNIST, etc
## Usage
### 1. Prepare MySQL and Milvus services by docker-compose
The audio similarity search system requires Milvus, MySQL services. We can start these containers with one click through [docker-compose.yaml](./docker-compose.yaml), so please make sure you have [installed Docker Engine](https://docs.docker.com/engine/install/) and [Docker Compose](https://docs.docker.com/compose/install/) before running. then
```bash
docker-compose -f docker-compose.yaml up -d
```
Then you will see the that all containers are created:
```bash
Creating network "quick_deploy_app_net" with driver "bridge"
Creating milvus-minio ... done
Creating milvus-etcd ... done
Creating audio-mysql ... done
Creating milvus-standalone ... done
Creating audio-webclient ... done
```
And show all containers with `docker ps`, and you can use `docker logs audio-mysql` to get the logs of server container
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
b2bcf279e599 milvusdb/milvus:v2.0.1 "/tini -- milvus run…" 22 hours ago Up 22 hours 0.0.0.0:19530->19530/tcp milvus-standalone
d8ef4c84e25c mysql:5.7 "docker-entrypoint.s…" 22 hours ago Up 22 hours 0.0.0.0:3306->3306/tcp, 33060/tcp audio-mysql
8fb501edb4f3 quay.io/coreos/etcd:v3.5.0 "etcd -advertise-cli…" 22 hours ago Up 22 hours 2379-2380/tcp milvus-etcd
ffce340b3790 minio/minio:RELEASE.2020-12-03T00-03-10Z "/usr/bin/docker-ent…" 22 hours ago Up 22 hours (healthy) 9000/tcp milvus-minio
15c84a506754 iregistry.baidu-int.com/paddlespeech/audio-search-client:1.0 "/bin/bash -c '/usr/…" 22 hours ago Up 22 hours (healthy) 0.0.0.0:8068->80/tcp audio-webclient
```
### 2. Start API Server
Then to start the system server, and it provides HTTP backend services.
- Install the Python packages
```bash
pip install -r requirements.txt
```
- Set configuration
```bash
vim src/config.py
```
Modify the parameters according to your own environment. Here listing some parameters that need to be set, for more information please refer to [config.py](./src/config.py).
| **Parameter** | **Description** | **Default setting** |
| ---------------- | ----------------------------------------------------- | ------------------- |
| MILVUS_HOST | The IP address of Milvus, you can get it by ifconfig. If running everything on one machine, most likely 127.0.0.1 | 127.0.0.1 |
| MILVUS_PORT | Port of Milvus. | 19530 |
| VECTOR_DIMENSION | Dimension of the vectors. | 2048 |
| MYSQL_HOST | The IP address of Mysql. | 127.0.0.1 |
| MYSQL_PORT | Port of Milvus. | 3306 |
| DEFAULT_TABLE | The milvus and mysql default collection name. | audio_table |
- Run the code
Then start the server with Fastapi.
```bash
export PYTHONPATH=$PYTHONPATH:./src
python src/main.py
```
Then you will see the Application is started:
```bash
INFO: Started server process [3949]
2022-03-07 17:39:14,864 INFO server.py serve 75 Started server process [3949]
INFO: Waiting for application startup.
2022-03-07 17:39:14,865 INFO on.py startup 45 Waiting for application startup.
INFO: Application startup complete.
2022-03-07 17:39:14,866 INFO on.py startup 59 Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8002 (Press CTRL+C to quit)
2022-03-07 17:39:14,867 INFO server.py _log_started_message 206 Uvicorn running on http://0.0.0.0:8002 (Press CTRL+C to quit)
```
### 3. Usage
- Prepare data
```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
- scripts test (recommend!)
The internal process is downloading data, loading the Paddlespeech model, extracting embedding, storing library, retrieving and deleting library
```bash
python ./src/test_main.py
```
Output
```bash
Checkpoint path: %your model path%
Extracting feature from audio No. 1 , 20 audios in total
Extracting feature from audio No. 2 , 20 audios in total
...
2022-03-09 17:22:13,870 INFO main.py load_audios 85 Successfully loaded data, total count: 20
2022-03-09 17:22:13,898 INFO main.py count_audio 147 Successfully count the number of data!
2022-03-09 17:22:13,918 INFO main.py audio_path 57 Successfully load audio: ./example_audio/test.wav
...
2022-03-09 17:22:32,580 INFO main.py search_local_audio 131 search result http://testserver/data?audio_path=./example_audio/test.wav, distance 0.0
2022-03-09 17:22:32,580 INFO main.py search_local_audio 131 search result http://testserver/data?audio_path=./example_audio/knife_chopping.wav, distance 0.021805256605148315
2022-03-09 17:22:32,580 INFO main.py search_local_audio 131 search result http://testserver/data?audio_path=./example_audio/knife_cut_into_flesh.wav, distance 0.052762262523174286
...
2022-03-09 17:22:32,582 INFO main.py search_local_audio 135 Successfully searched similar audio!
2022-03-09 17:22:33,658 INFO main.py drop_tables 159 Successfully drop tables in Milvus and MySQL!
```
- GUI test (optional)
Navigate to 127.0.0.1:8068 in your browser to access the front-end interface
Note: If the browser and the service are not on the same machine, then the IP needs to be changed to the IP of the machine where the service is located, and the corresponding API_URL in docker-compose.yaml needs to be changed and the service can be restarted
- Insert data
Download the data and decompress it to a path named /home/speech/data. Then enter /home/speech/data in the address bar of the upload page to upload the data
![](./img/insert.png)
- Search for similar audio
Select the magnifying glass icon on the left side of the interface. Then, press the "Default Target Audio File" button and upload a .wav sound file you'd like to search. Results will be displayed
![](./img/search.png)
### 4.Result
machine configuration
- OS: CentOS release 7.6
- kernel4.17.11-1.el7.elrepo.x86_64
- CPUIntel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
- memory132G
dataset
- CN-Celeb, train size 650,000, test size 10,000, dimention 192, distance L2
recall and elapsed time statistics are shown in the following figure
![](./img/result.png)
The retrieval framework based on Milvus takes about 2.9 milliseconds to retrieve on the premise of 90% recall rate, and it takes about 500 milliseconds for feature extraction (testing audio takes about 5 seconds), that is, a single audio test takes about 503 milliseconds in total, which can meet most application scenarios
### 5.Pretrained Models
Here is a list of pretrained models released by PaddleSpeech :
| Model | Sample Rate
| :--- | :---:
| ecapa_tdnn | 16000
| panns_cnn6| 32000
| panns_cnn10| 32000
| panns_cnn14| 32000

@ -0,0 +1,172 @@
(简体中文|[English](./README.md))
# 音频相似性检索
## 介绍
随着互联网不断发展,电子邮件、社交媒体照片、直播视频、客服语音等非结构化数据已经变得越来越普遍。如果想要使用计算机来处理这些数据,需要使用 embedding 技术将这些数据转化为向量 vector然后进行存储、建索引、并查询
但是当数据量很大比如上亿条音频要做相似度搜索就比较困难了。穷举法固然可行但非常耗时。针对这种场景该demo 将介绍如何使用开源向量数据库 Milvus 搭建音频相似度检索系统
音频检索(如演讲、音乐、说话人等检索)实现了在海量音频数据中查询并找出相似声音(或相同说话人)片段。音频相似性检索系统可用于识别相似的音效、最大限度减少知识产权侵权等,还可以快速的检索声纹库、帮助企业控制欺诈和身份盗用等。在音频数据的分类和统计分析中,音频检索也发挥着重要作用
在本 demo 中,你将学会如何构建一个音频检索系统,用来检索相似的声音片段。使用基于 PaddleSpeech 预训练模型(音频分类模型,说话人识别模型等)将上传的音频片段转换为向量数据,并存储在 Milvus 中。Milvus 自动为每个向量生成唯一的 ID然后将 ID 和 相应的音频信息音频id音频的说话人id等等存储在 MySQL这样就完成建库的工作。用户在检索时上传测试音频得到向量然后在 Milvus 中进行向量相似度搜索Milvus 返回的检索结果为向量 ID通过 ID 在 MySQL 内部查询相应的音频信息即可
![音频检索流程图](./img/audio_searching.png)
注:该 demo 使用 [CN-Celeb](http://openslr.org/82/) 数据集,包括至少 650000 条音频3000 个说话人来建立音频向量库音频特征或音频说话人特征然后通过预设的距离计算方式进行音频或说话人检索这里面数据集也可以使用其他的根据需要调整如LibrispeechVoxCelebUrbanSoundGloVeMNIST等
## 使用方法
### 1. MySQL 和 Milvus 安装
音频相似度搜索系统需要用到 Milvus, MySQL 服务。 我们可以通过 [docker-compose.yaml](./docker-compose.yaml) 一键启动这些容器,所以请确保在运行之前已经安装了 [Docker Engine](https://docs.docker.com/engine/install/) 和 [Docker Compose](https://docs.docker.com/compose/install/)。 即
```bash
docker-compose -f docker-compose.yaml up -d
```
然后你会看到所有的容器都被创建:
```bash
Creating network "quick_deploy_app_net" with driver "bridge"
Creating milvus-minio ... done
Creating milvus-etcd ... done
Creating audio-mysql ... done
Creating milvus-standalone ... done
Creating audio-webclient ... done
```
可以采用'docker ps'来显示所有的容器,还可以使用'docker logs audio-mysql'来获取服务器容器的日志:
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
b2bcf279e599 milvusdb/milvus:v2.0.1 "/tini -- milvus run…" 22 hours ago Up 22 hours 0.0.0.0:19530->19530/tcp milvus-standalone
d8ef4c84e25c mysql:5.7 "docker-entrypoint.s…" 22 hours ago Up 22 hours 0.0.0.0:3306->3306/tcp, 33060/tcp audio-mysql
8fb501edb4f3 quay.io/coreos/etcd:v3.5.0 "etcd -advertise-cli…" 22 hours ago Up 22 hours 2379-2380/tcp milvus-etcd
ffce340b3790 minio/minio:RELEASE.2020-12-03T00-03-10Z "/usr/bin/docker-ent…" 22 hours ago Up 22 hours (healthy) 9000/tcp milvus-minio
15c84a506754 iregistry.baidu-int.com/paddlespeech/audio-search-client:1.0 "/bin/bash -c '/usr/…" 22 hours ago Up 22 hours (healthy) 0.0.0.0:8068->80/tcp audio-webclient
```
### 2. 配置并启动 API 服务
启动系统服务程序,它会提供基于 Http 后端服务
- 安装服务依赖的 python 基础包
```bash
pip install -r requirements.txt
```
- 修改配置
```bash
vim src/config.py
```
请根据实际环境进行修改。 这里列出了一些需要设置的参数,更多信息请参考 [config.py](./src/config.py)
| **Parameter** | **Description** | **Default setting** |
| ---------------- | ----------------------------------------------------- | ------------------- |
| MILVUS_HOST | The IP address of Milvus, you can get it by ifconfig. If running everything on one machine, most likely 127.0.0.1 | 127.0.0.1 |
| MILVUS_PORT | Port of Milvus. | 19530 |
| VECTOR_DIMENSION | Dimension of the vectors. | 2048 |
| MYSQL_HOST | The IP address of Mysql. | 127.0.0.1 |
| MYSQL_PORT | Port of Milvus. | 3306 |
| DEFAULT_TABLE | The milvus and mysql default collection name. | audio_table |
- 运行程序
启动用 Fastapi 构建的服务
```bash
export PYTHONPATH=$PYTHONPATH:./src
python src/main.py
```
然后你会看到应用程序启动:
```bash
INFO: Started server process [3949]
2022-03-07 17:39:14,864 INFO server.py serve 75 Started server process [3949]
INFO: Waiting for application startup.
2022-03-07 17:39:14,865 INFO on.py startup 45 Waiting for application startup.
INFO: Application startup complete.
2022-03-07 17:39:14,866 INFO on.py startup 59 Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8002 (Press CTRL+C to quit)
2022-03-07 17:39:14,867 INFO server.py _log_started_message 206 Uvicorn running on http://0.0.0.0:8002 (Press CTRL+C to quit)
```
### 3. 测试方法
- 准备数据
```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
- 脚本测试(推荐)
```bash
python ./src/test_main.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
输出:
```bash
Checkpoint path: %your model path%
Extracting feature from audio No. 1 , 20 audios in total
Extracting feature from audio No. 2 , 20 audios in total
...
2022-03-09 17:22:13,870 INFO main.py load_audios 85 Successfully loaded data, total count: 20
2022-03-09 17:22:13,898 INFO main.py count_audio 147 Successfully count the number of data!
2022-03-09 17:22:13,918 INFO main.py audio_path 57 Successfully load audio: ./example_audio/test.wav
...
2022-03-09 17:22:32,580 INFO main.py search_local_audio 131 search result http://testserver/data?audio_path=./example_audio/test.wav, distance 0.0
2022-03-09 17:22:32,580 INFO main.py search_local_audio 131 search result http://testserver/data?audio_path=./example_audio/knife_chopping.wav, distance 0.021805256605148315
2022-03-09 17:22:32,580 INFO main.py search_local_audio 131 search result http://testserver/data?audio_path=./example_audio/knife_cut_into_flesh.wav, distance 0.052762262523174286
...
2022-03-09 17:22:32,582 INFO main.py search_local_audio 135 Successfully searched similar audio!
2022-03-09 17:22:33,658 INFO main.py drop_tables 159 Successfully drop tables in Milvus and MySQL!
```
- 前端测试(可选)
在浏览器中输入 127.0.0.1:8068 访问前端页面
注:如果浏览器和服务不在同一台机器上,那么 IP 需要修改成服务所在的机器 IP并且docker-compose.yaml 中相应的 API_URL 也要修改,并重新起服务即可
- 上传音频
下载数据并解压到一文件夹,假设为 /home/speech/data那么在上传页面地址栏输入 /home/speech/data 进行数据上传
![](./img/insert.png)
- 检索相似音频
选择左上角放大镜,点击 “Default Target Audio File” 按钮,上传测试音频,接着你将看到检索结果
![](./img/search.png)
### 4. 结果
机器配置:
- 操作系统: CentOS release 7.6
- 内核4.17.11-1.el7.elrepo.x86_64
- 处理器Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
- 内存132G
数据集:
- CN-Celeb, 训练集 65万, 测试集 1万向量维度 192距离计算方式 L2
召回和耗时统计如下图:
![](./img/result.png)
基于 milvus 的检索框架在召回率 90% 的前提下,检索耗时约 2.9 毫秒,加上特征提取(Embedding)耗时约 500毫秒(测试音频时长约 5秒),即单条音频测试总共耗时约 503 毫秒,可以满足大多数应用场景
### 5. 预训练模型
以下是 PaddleSpeech 提供的预训练模型列表:
| 模型 | 采样率
| :--- | :---:
| ecapa_tdnn| 16000
| panns_cnn6| 32000
| panns_cnn10| 32000
| panns_cnn14| 32000

@ -0,0 +1,88 @@
version: '3.5'
services:
etcd:
container_name: milvus-etcd
image: quay.io/coreos/etcd:v3.5.0
networks:
app_net:
environment:
- ETCD_AUTO_COMPACTION_MODE=revision
- ETCD_AUTO_COMPACTION_RETENTION=1000
- ETCD_QUOTA_BACKEND_BYTES=4294967296
volumes:
- ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/etcd:/etcd
command: etcd -advertise-client-urls=http://127.0.0.1:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd
minio:
container_name: milvus-minio
image: minio/minio:RELEASE.2020-12-03T00-03-10Z
networks:
app_net:
environment:
MINIO_ACCESS_KEY: minioadmin
MINIO_SECRET_KEY: minioadmin
volumes:
- ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/minio:/minio_data
command: minio server /minio_data
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"]
interval: 30s
timeout: 20s
retries: 3
standalone:
container_name: milvus-standalone
image: milvusdb/milvus:v2.0.1
networks:
app_net:
ipv4_address: 172.16.23.10
command: ["milvus", "run", "standalone"]
environment:
ETCD_ENDPOINTS: etcd:2379
MINIO_ADDRESS: minio:9000
volumes:
- ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/milvus:/var/lib/milvus
ports:
- "19530:19530"
depends_on:
- "etcd"
- "minio"
mysql:
container_name: audio-mysql
image: mysql:5.7
networks:
app_net:
ipv4_address: 172.16.23.11
environment:
- MYSQL_ROOT_PASSWORD=123456
volumes:
- ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/mysql:/var/lib/mysql
ports:
- "3306:3306"
webclient:
container_name: audio-webclient
image: qingen1/paddlespeech-audio-search-client:2.3
networks:
app_net:
ipv4_address: 172.16.23.13
environment:
API_URL: 'http://127.0.0.1:8002'
ports:
- "8068:80"
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost/"]
interval: 30s
timeout: 20s
retries: 3
networks:
app_net:
driver: bridge
ipam:
driver: default
config:
- subnet: 172.16.23.0/24
gateway: 172.16.23.1

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@ -0,0 +1,12 @@
soundfile==0.10.3.post1
librosa==0.8.0
numpy
pymysql
fastapi
uvicorn
diskcache==5.2.1
pymilvus==2.0.1
python-multipart
typing
starlette
pydantic

@ -0,0 +1,37 @@
# 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
############### Milvus Configuration ###############
MILVUS_HOST = os.getenv("MILVUS_HOST", "127.0.0.1")
MILVUS_PORT = int(os.getenv("MILVUS_PORT", "19530"))
VECTOR_DIMENSION = int(os.getenv("VECTOR_DIMENSION", "2048"))
INDEX_FILE_SIZE = int(os.getenv("INDEX_FILE_SIZE", "1024"))
METRIC_TYPE = os.getenv("METRIC_TYPE", "L2")
DEFAULT_TABLE = os.getenv("DEFAULT_TABLE", "audio_table")
TOP_K = int(os.getenv("TOP_K", "10"))
############### MySQL Configuration ###############
MYSQL_HOST = os.getenv("MYSQL_HOST", "127.0.0.1")
MYSQL_PORT = int(os.getenv("MYSQL_PORT", "3306"))
MYSQL_USER = os.getenv("MYSQL_USER", "root")
MYSQL_PWD = os.getenv("MYSQL_PWD", "123456")
MYSQL_DB = os.getenv("MYSQL_DB", "mysql")
############### Data Path ###############
UPLOAD_PATH = os.getenv("UPLOAD_PATH", "tmp/audio-data")
############### Number of Log Files ###############
LOGS_NUM = int(os.getenv("logs_num", "0"))

@ -0,0 +1,39 @@
# 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 librosa
import numpy as np
from logs import LOGGER
def get_audio_embedding(path):
"""
Use vpr_inference to generate embedding of audio
"""
try:
RESAMPLE_RATE = 16000
audio, _ = librosa.load(path, sr=RESAMPLE_RATE, mono=True)
# TODO add infer/python interface to get embedding, now fake it by rand
# vpr = ECAPATDNN(checkpoint_path=None, device='cuda')
# embedding = vpr.inference(audio)
np.random.seed(hash(os.path.basename(path)) % 1000000)
embedding = np.random.rand(1, 2048)
embedding = embedding / np.linalg.norm(embedding)
embedding = embedding.tolist()[0]
return embedding
except Exception as e:
LOGGER.error(f"Error with embedding:{e}")
return None

@ -0,0 +1,164 @@
# 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 codecs
import datetime
import logging
import os
import re
import sys
from config import LOGS_NUM
class MultiprocessHandler(logging.FileHandler):
"""
A handler class which writes formatted logging records to disk files
"""
def __init__(self,
filename,
when='D',
backupCount=0,
encoding=None,
delay=False):
"""
Open the specified file and use it as the stream for logging
"""
self.prefix = filename
self.backupCount = backupCount
self.when = when.upper()
self.extMath = r"^\d{4}-\d{2}-\d{2}"
self.when_dict = {
'S': "%Y-%m-%d-%H-%M-%S",
'M': "%Y-%m-%d-%H-%M",
'H': "%Y-%m-%d-%H",
'D': "%Y-%m-%d"
}
self.suffix = self.when_dict.get(when)
if not self.suffix:
print('The specified date interval unit is invalid: ', self.when)
sys.exit(1)
self.filefmt = os.path.join('.', "logs",
f"{self.prefix}-{self.suffix}.log")
self.filePath = datetime.datetime.now().strftime(self.filefmt)
_dir = os.path.dirname(self.filefmt)
try:
if not os.path.exists(_dir):
os.makedirs(_dir)
except Exception as e:
print('Failed to create log file: ', e)
print("log_path" + self.filePath)
sys.exit(1)
logging.FileHandler.__init__(self, self.filePath, 'a+', encoding, delay)
def should_change_file_to_write(self):
"""
To write the file
"""
_filePath = datetime.datetime.now().strftime(self.filefmt)
if _filePath != self.filePath:
self.filePath = _filePath
return True
return False
def do_change_file(self):
"""
To change file states
"""
self.baseFilename = os.path.abspath(self.filePath)
if self.stream:
self.stream.close()
self.stream = None
if not self.delay:
self.stream = self._open()
if self.backupCount > 0:
for s in self.get_files_to_delete():
os.remove(s)
def get_files_to_delete(self):
"""
To delete backup files
"""
dir_name, _ = os.path.split(self.baseFilename)
file_names = os.listdir(dir_name)
result = []
prefix = self.prefix + '-'
for file_name in file_names:
if file_name[:len(prefix)] == prefix:
suffix = file_name[len(prefix):-4]
if re.compile(self.extMath).match(suffix):
result.append(os.path.join(dir_name, file_name))
result.sort()
if len(result) < self.backupCount:
result = []
else:
result = result[:len(result) - self.backupCount]
return result
def emit(self, record):
"""
Emit a record
"""
try:
if self.should_change_file_to_write():
self.do_change_file()
logging.FileHandler.emit(self, record)
except (KeyboardInterrupt, SystemExit):
raise
except:
self.handleError(record)
def write_log():
"""
Init a logger
"""
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
# formatter = '%(asctime)s %(levelname)s %(filename)s %(funcName)s %(module)s %(lineno)s %(message)s'
fmt = logging.Formatter(
'%(asctime)s %(levelname)s %(filename)s %(funcName)s %(lineno)s %(message)s'
)
stream_handler = logging.StreamHandler(sys.stdout)
stream_handler.setLevel(logging.INFO)
stream_handler.setFormatter(fmt)
log_name = "audio-searching"
file_handler = MultiprocessHandler(log_name, when='D', backupCount=LOGS_NUM)
file_handler.setLevel(logging.DEBUG)
file_handler.setFormatter(fmt)
file_handler.do_change_file()
logger.addHandler(stream_handler)
logger.addHandler(file_handler)
return logger
LOGGER = write_log()
if __name__ == "__main__":
message = 'test writing logs'
LOGGER.info(message)
LOGGER.debug(message)
LOGGER.error(message)

@ -0,0 +1,168 @@
# 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
from typing import Optional
import uvicorn
from config import UPLOAD_PATH
from diskcache import Cache
from fastapi import FastAPI
from fastapi import File
from fastapi import UploadFile
from logs import LOGGER
from milvus_helpers import MilvusHelper
from mysql_helpers import MySQLHelper
from operations.count import do_count
from operations.drop import do_drop
from operations.load import do_load
from operations.search import do_search
from pydantic import BaseModel
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=["*"])
MODEL = None
MILVUS_CLI = MilvusHelper()
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.get('/data')
def audio_path(audio_path):
# Get the audio file
try:
LOGGER.info(f"Successfully load audio: {audio_path}")
return FileResponse(audio_path)
except Exception as e:
LOGGER.error(f"upload audio error: {e}")
return {'status': False, 'msg': e}, 400
@app.get('/progress')
def get_progress():
# Get the progress of dealing with data
try:
cache = Cache('./tmp')
return f"current: {cache['current']}, total: {cache['total']}"
except Exception as e:
LOGGER.error(f"Upload data error: {e}")
return {'status': False, 'msg': e}, 400
class Item(BaseModel):
Table: Optional[str] = None
File: str
@app.post('/audio/load')
async def load_audios(item: Item):
# Insert all the audio files under the file path to Milvus/MySQL
try:
total_num = do_load(item.Table, item.File, MILVUS_CLI, MYSQL_CLI)
LOGGER.info(f"Successfully loaded data, total count: {total_num}")
return {'status': True, 'msg': "Successfully loaded data!"}
except Exception as e:
LOGGER.error(e)
return {'status': False, 'msg': e}, 400
@app.post('/audio/search')
async def search_audio(request: Request,
table_name: str=None,
audio: UploadFile=File(...)):
# Search the uploaded audio in Milvus/MySQL
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']
_, paths, distances = do_search(host, table_name, query_audio_path,
MILVUS_CLI, MYSQL_CLI)
names = []
for path, score in zip(paths, distances):
names.append(os.path.basename(path))
LOGGER.info(f"search result {path}, score {score}")
res = dict(zip(paths, zip(names, distances)))
# Sort results by distance metric, closest distances first
res = sorted(res.items(), key=lambda item: item[1][1], reverse=True)
LOGGER.info("Successfully searched similar audio!")
return res
except Exception as e:
LOGGER.error(e)
return {'status': False, 'msg': e}, 400
@app.post('/audio/search/local')
async def search_local_audio(request: Request,
query_audio_path: str,
table_name: str=None):
# Search the uploaded audio in Milvus/MySQL
try:
host = request.headers['host']
_, paths, distances = do_search(host, table_name, query_audio_path,
MILVUS_CLI, MYSQL_CLI)
names = []
for path, score in zip(paths, distances):
names.append(os.path.basename(path))
LOGGER.info(f"search result {path}, score {score}")
res = dict(zip(paths, zip(names, distances)))
# Sort results by distance metric, closest distances first
res = sorted(res.items(), key=lambda item: item[1][1], reverse=True)
LOGGER.info("Successfully searched similar audio!")
return res
except Exception as e:
LOGGER.error(e)
return {'status': False, 'msg': e}, 400
@app.get('/audio/count')
async def count_audio(table_name: str=None):
# Returns the total number of vectors in the system
try:
num = do_count(table_name, MILVUS_CLI)
LOGGER.info("Successfully count the number of data!")
return num
except Exception as e:
LOGGER.error(e)
return {'status': False, 'msg': e}, 400
@app.post('/audio/drop')
async def drop_tables(table_name: str=None):
# Delete the collection of Milvus and MySQL
try:
status = do_drop(table_name, MILVUS_CLI, MYSQL_CLI)
LOGGER.info("Successfully drop tables in Milvus and MySQL!")
return status
except Exception as e:
LOGGER.error(e)
return {'status': False, 'msg': e}, 400
if __name__ == '__main__':
uvicorn.run(app=app, host='0.0.0.0', port=8002)

@ -0,0 +1,185 @@
# 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 sys
from config import METRIC_TYPE
from config import MILVUS_HOST
from config import MILVUS_PORT
from config import VECTOR_DIMENSION
from logs import LOGGER
from pymilvus import Collection
from pymilvus import CollectionSchema
from pymilvus import connections
from pymilvus import DataType
from pymilvus import FieldSchema
from pymilvus import utility
class MilvusHelper:
"""
the basic operations of PyMilvus
# This example shows how to:
# 1. connect to Milvus server
# 2. create a collection
# 3. insert entities
# 4. create index
# 5. search
# 6. delete a collection
"""
def __init__(self):
try:
self.collection = None
connections.connect(host=MILVUS_HOST, port=MILVUS_PORT)
LOGGER.debug(
f"Successfully connect to Milvus with IP:{MILVUS_HOST} and PORT:{MILVUS_PORT}"
)
except Exception as e:
LOGGER.error(f"Failed to connect Milvus: {e}")
sys.exit(1)
def set_collection(self, collection_name):
try:
if self.has_collection(collection_name):
self.collection = Collection(name=collection_name)
else:
raise Exception(
f"There is no collection named:{collection_name}")
except Exception as e:
LOGGER.error(f"Failed to set collection in Milvus: {e}")
sys.exit(1)
def has_collection(self, collection_name):
# Return if Milvus has the collection
try:
return utility.has_collection(collection_name)
except Exception as e:
LOGGER.error(f"Failed to check state of collection in Milvus: {e}")
sys.exit(1)
def create_collection(self, collection_name):
# Create milvus collection if not exists
try:
if not self.has_collection(collection_name):
field1 = FieldSchema(
name="id",
dtype=DataType.INT64,
descrition="int64",
is_primary=True,
auto_id=True)
field2 = FieldSchema(
name="embedding",
dtype=DataType.FLOAT_VECTOR,
descrition="speaker embeddings",
dim=VECTOR_DIMENSION,
is_primary=False)
schema = CollectionSchema(
fields=[field1, field2], description="embeddings info")
self.collection = Collection(
name=collection_name, schema=schema)
LOGGER.debug(f"Create Milvus collection: {collection_name}")
else:
self.set_collection(collection_name)
return "OK"
except Exception as e:
LOGGER.error(f"Failed to create collection in Milvus: {e}")
sys.exit(1)
def insert(self, collection_name, vectors):
# Batch insert vectors to milvus collection
try:
self.create_collection(collection_name)
data = [vectors]
self.set_collection(collection_name)
mr = self.collection.insert(data)
ids = mr.primary_keys
self.collection.load()
LOGGER.debug(
f"Insert vectors to Milvus in collection: {collection_name} with {len(vectors)} rows"
)
return ids
except Exception as e:
LOGGER.error(f"Failed to insert data to Milvus: {e}")
sys.exit(1)
def create_index(self, collection_name):
# Create IVF_FLAT index on milvus collection
try:
self.set_collection(collection_name)
default_index = {
"index_type": "IVF_SQ8",
"metric_type": METRIC_TYPE,
"params": {
"nlist": 16384
}
}
status = self.collection.create_index(
field_name="embedding", index_params=default_index)
if not status.code:
LOGGER.debug(
f"Successfully create index in collection:{collection_name} with param:{default_index}"
)
return status
else:
raise Exception(status.message)
except Exception as e:
LOGGER.error(f"Failed to create index: {e}")
sys.exit(1)
def delete_collection(self, collection_name):
# Delete Milvus collection
try:
self.set_collection(collection_name)
self.collection.drop()
LOGGER.debug("Successfully drop collection!")
return "ok"
except Exception as e:
LOGGER.error(f"Failed to drop collection: {e}")
sys.exit(1)
def search_vectors(self, collection_name, vectors, top_k):
# Search vector in milvus collection
try:
self.set_collection(collection_name)
search_params = {
"metric_type": METRIC_TYPE,
"params": {
"nprobe": 16
}
}
res = self.collection.search(
vectors,
anns_field="embedding",
param=search_params,
limit=top_k)
LOGGER.debug(f"Successfully search in collection: {res}")
return res
except Exception as e:
LOGGER.error(f"Failed to search vectors in Milvus: {e}")
sys.exit(1)
def count(self, collection_name):
# Get the number of milvus collection
try:
self.set_collection(collection_name)
num = self.collection.num_entities
LOGGER.debug(
f"Successfully get the num:{num} of the collection:{collection_name}"
)
return num
except Exception as e:
LOGGER.error(f"Failed to count vectors in Milvus: {e}")
sys.exit(1)

@ -0,0 +1,133 @@
# 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 sys
import pymysql
from config import MYSQL_DB
from config import MYSQL_HOST
from config import MYSQL_PORT
from config import MYSQL_PWD
from config import MYSQL_USER
from logs import LOGGER
class MySQLHelper():
"""
the basic operations of PyMySQL
# This example shows how to:
# 1. connect to MySQL server
# 2. create a table
# 3. insert data to table
# 4. search by milvus ids
# 5. delete table
"""
def __init__(self):
self.conn = pymysql.connect(
host=MYSQL_HOST,
user=MYSQL_USER,
port=MYSQL_PORT,
password=MYSQL_PWD,
database=MYSQL_DB,
local_infile=True)
self.cursor = self.conn.cursor()
def test_connection(self):
try:
self.conn.ping()
except Exception:
self.conn = pymysql.connect(
host=MYSQL_HOST,
user=MYSQL_USER,
port=MYSQL_PORT,
password=MYSQL_PWD,
database=MYSQL_DB,
local_infile=True)
self.cursor = self.conn.cursor()
def create_mysql_table(self, table_name):
# Create mysql table if not exists
self.test_connection()
sql = "create table if not exists " + table_name + "(milvus_id TEXT, audio_path 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(self, table_name, data):
# Batch insert (Milvus_ids, img_path) to mysql
self.test_connection()
sql = "insert into " + table_name + " (milvus_id,audio_path) values (%s,%s);"
try:
self.cursor.executemany(sql, data)
self.conn.commit()
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 search_by_milvus_ids(self, ids, table_name):
# Get the img_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 + ");"
try:
self.cursor.execute(sql)
results = self.cursor.fetchall()
results = [res[0] for res in results]
LOGGER.debug("MYSQL search by milvus id.")
return results
except Exception as e:
LOGGER.error(f"MYSQL ERROR: {e} with sql: {sql}")
sys.exit(1)
def delete_table(self, table_name):
# Delete mysql table if exists
self.test_connection()
sql = "drop table if exists " + table_name + ";"
try:
self.cursor.execute(sql)
LOGGER.debug(f"MYSQL delete table:{table_name}")
except Exception as e:
LOGGER.error(f"MYSQL ERROR: {e} with sql: {sql}")
sys.exit(1)
def delete_all_data(self, table_name):
# Delete all the data in mysql table
self.test_connection()
sql = 'delete from ' + table_name + ';'
try:
self.cursor.execute(sql)
self.conn.commit()
LOGGER.debug(f"MYSQL delete all data in table:{table_name}")
except Exception as e:
LOGGER.error(f"MYSQL ERROR: {e} with sql: {sql}")
sys.exit(1)
def count_table(self, table_name):
# Get the number of mysql table
self.test_connection()
sql = "select count(milvus_id) from " + table_name + ";"
try:
self.cursor.execute(sql)
results = self.cursor.fetchall()
LOGGER.debug(f"MYSQL count table:{table_name}")
return results[0][0]
except Exception as e:
LOGGER.error(f"MYSQL ERROR: {e} with sql: {sql}")
sys.exit(1)

@ -1,4 +1,4 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# 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.
@ -11,5 +11,3 @@
# 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 .backends import *
from .features import *

@ -0,0 +1,33 @@
# 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 sys
from config import DEFAULT_TABLE
from logs import LOGGER
def do_count(table_name, milvus_cli):
"""
Returns the total number of vectors in the system
"""
if not table_name:
table_name = DEFAULT_TABLE
try:
if not milvus_cli.has_collection(table_name):
return None
num = milvus_cli.count(table_name)
return num
except Exception as e:
LOGGER.error(f"Error attempting to count table {e}")
sys.exit(1)

@ -0,0 +1,34 @@
# 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 sys
from config import DEFAULT_TABLE
from logs import LOGGER
def do_drop(table_name, milvus_cli, mysql_cli):
"""
Delete the collection of Milvus and MySQL
"""
if not table_name:
table_name = DEFAULT_TABLE
try:
if not milvus_cli.has_collection(table_name):
return "Collection is not exist"
status = milvus_cli.delete_collection(table_name)
mysql_cli.delete_table(table_name)
return status
except Exception as e:
LOGGER.error(f"Error attempting to drop table: {e}")
sys.exit(1)

@ -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.
import os
import sys
from config import DEFAULT_TABLE
from diskcache import Cache
from encode import get_audio_embedding
from logs import LOGGER
def get_audios(path):
"""
List all wav and aif files recursively under the path folder.
"""
supported_formats = [".wav", ".mp3", ".ogg", ".flac", ".m4a"]
return [
item
for sublist in [[os.path.join(dir, file) for file in files]
for dir, _, files in list(os.walk(path))]
for item in sublist if os.path.splitext(item)[1] in supported_formats
]
def extract_features(audio_dir):
"""
Get the vector of audio
"""
try:
cache = Cache('./tmp')
feats = []
names = []
audio_list = get_audios(audio_dir)
total = len(audio_list)
cache['total'] = total
for i, audio_path in enumerate(audio_list):
norm_feat = get_audio_embedding(audio_path)
if norm_feat is None:
continue
feats.append(norm_feat)
names.append(audio_path.encode())
cache['current'] = i + 1
print(
f"Extracting feature from audio No. {i + 1} , {total} audios in total"
)
return feats, names
except Exception as e:
LOGGER.error(f"Error with extracting feature from audio {e}")
sys.exit(1)
def format_data(ids, names):
"""
Combine the id of the vector and the name of the audio into a list
"""
data = []
for i in range(len(ids)):
value = (str(ids[i]), names[i])
data.append(value)
return data
def do_load(table_name, audio_dir, milvus_cli, mysql_cli):
"""
Import vectors to Milvus and data to Mysql respectively
"""
if not table_name:
table_name = DEFAULT_TABLE
vectors, names = extract_features(audio_dir)
ids = milvus_cli.insert(table_name, vectors)
milvus_cli.create_index(table_name)
mysql_cli.create_mysql_table(table_name)
mysql_cli.load_data_to_mysql(table_name, format_data(ids, names))
return len(ids)

@ -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 sys
from config import DEFAULT_TABLE
from config import TOP_K
from encode import get_audio_embedding
from logs import LOGGER
def do_search(host, table_name, audio_path, milvus_cli, mysql_cli):
"""
Search the uploaded audio in Milvus/MySQL
"""
try:
if not table_name:
table_name = DEFAULT_TABLE
feat = get_audio_embedding(audio_path)
vectors = milvus_cli.search_vectors(table_name, [feat], TOP_K)
vids = [str(x.id) for x in vectors[0]]
paths = mysql_cli.search_by_milvus_ids(vids, table_name)
distances = [x.distance for x in vectors[0]]
for i in range(len(paths)):
tmp = "http://" + str(host) + "/data?audio_path=" + str(paths[i])
paths[i] = tmp
distances[i] = (1 - distances[i]) * 100
return vids, paths, distances
except Exception as e:
LOGGER.error(f"Error with search: {e}")
sys.exit(1)

@ -0,0 +1,95 @@
# 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 zipfile
import gdown
from fastapi.testclient import TestClient
from main import app
client = TestClient(app)
def download_audio_data():
"""
download audio data
"""
url = 'https://drive.google.com/uc?id=1bKu21JWBfcZBuEuzFEvPoAX6PmRrgnUp'
gdown.download(url)
with zipfile.ZipFile('example_audio.zip', 'r') as zip_ref:
zip_ref.extractall('./example_audio')
def test_drop():
"""
Delete the collection of Milvus and MySQL
"""
response = client.post("/audio/drop")
assert response.status_code == 200
def test_load():
"""
Insert all the audio files under the file path to Milvus/MySQL
"""
response = client.post("/audio/load", json={"File": "./example_audio"})
assert response.status_code == 200
assert response.json() == {
'status': True,
'msg': "Successfully loaded data!"
}
def test_progress():
"""
Get the progress of dealing with data
"""
response = client.get("/progress")
assert response.status_code == 200
assert response.json() == "current: 20, total: 20"
def test_count():
"""
Returns the total number of vectors in the system
"""
response = client.get("audio/count")
assert response.status_code == 200
assert response.json() == 20
def test_search():
"""
Search the uploaded audio in Milvus/MySQL
"""
response = client.post(
"/audio/search/local?query_audio_path=.%2Fexample_audio%2Ftest.wav")
assert response.status_code == 200
assert len(response.json()) == 10
def test_data():
"""
Get the audio file
"""
response = client.get("/data?audio_path=.%2Fexample_audio%2Ftest.wav")
assert response.status_code == 200
if __name__ == "__main__":
download_audio_data()
test_load()
test_count()
test_search()
test_drop()

@ -84,5 +84,8 @@ Here is a list of pretrained models released by PaddleSpeech that can be used by
| Model | Language | Sample Rate
| :--- | :---: | :---: |
| conformer_wenetspeech| zh| 16000
| transformer_librispeech| en| 16000
| conformer_wenetspeech| zh| 16k
| transformer_librispeech| en| 16k
| deepspeech2offline_aishell| zh| 16k
| deepspeech2online_aishell | zh | 16k
|deepspeech2offline_librispeech|en| 16k

@ -81,5 +81,8 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
| 模型 | 语言 | 采样率
| :--- | :---: | :---: |
| conformer_wenetspeech| zh| 16000
| transformer_librispeech| en| 16000
| conformer_wenetspeech | zh | 16k
| transformer_librispeech | en | 16k
| deepspeech2offline_aishell| zh| 16k
| deepspeech2online_aishell | zh | 16k
| deepspeech2offline_librispeech | en | 16k

@ -10,21 +10,15 @@ This demo is an implementation of starting the voice service and accessing the s
### 1. Installation
see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
You can choose one way from easy, meduim and hard to install paddlespeech.
It is recommended to use **paddlepaddle 2.2.1** or above.
You can choose one way from meduim and hard to install paddlespeech.
### 2. Prepare config File
The configuration file contains the service-related configuration files and the model configuration related to the voice tasks contained in the service. They are all under the `conf` folder.
The configuration file can be found in `conf/application.yaml` .
Among them, `engine_list` indicates the speech engine that will be included in the service to be started, in the format of <speech task>_<engine type>.
At present, the speech tasks integrated by the service include: asr (speech recognition) and tts (speech synthesis).
Currently the engine type supports two forms: python and inference (Paddle Inference)
**Note: The configuration of `engine_backend` in `application.yaml` represents all speech tasks included in the started service. **
If the service you want to start contains only a certain speech task, then you need to comment out the speech tasks that do not need to be included. For example, if you only want to use the speech recognition (ASR) service, then you can comment out the speech synthesis (TTS) service, as in the following example:
```bash
engine_backend:
asr: 'conf/asr/asr.yaml'
#tts: 'conf/tts/tts.yaml'
```
**Note: The configuration file of `engine_backend` in `application.yaml` needs to match the configuration type of `engine_type`. **
When the configuration file of `engine_backend` is `XXX.yaml`, the configuration type of `engine_type` needs to be set to `python`; when the configuration file of `engine_backend` is `XXX_pd.yaml`, the configuration of `engine_type` needs to be set type is `inference`;
The input of ASR client demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.
@ -116,21 +110,22 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
- Python API
```python
from paddlespeech.server.bin.paddlespeech_client import ASRClientExecutor
import json
asrclient_executor = ASRClientExecutor()
asrclient_executor(
res = asrclient_executor(
input="./zh.wav",
server_ip="127.0.0.1",
port=8090,
sample_rate=16000,
lang="zh_cn",
audio_format="wav")
print(res.json())
```
Output:
```bash
{'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'transcription': '我认为跑步最重要的就是给我带来了身体健康'}}
time cost 0.604353 s.
```
### 5. TTS Client Usage
@ -152,7 +147,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
- `speed`: Audio speed, the value should be set between 0 and 3. Default: 1.0
- `volume`: Audio volume, the value should be set between 0 and 3. Default: 1.0
- `sample_rate`: Sampling rate, choice: [0, 8000, 16000], the default is the same as the model. Default: 0
- `output`: Output wave filepath. Default: `output.wav`.
- `output`: Output wave filepath. Default: None, which means not to save the audio to the local.
Output:
```bash
@ -166,9 +161,10 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
- Python API
```python
from paddlespeech.server.bin.paddlespeech_client import TTSClientExecutor
import json
ttsclient_executor = TTSClientExecutor()
ttsclient_executor(
res = ttsclient_executor(
input="您好,欢迎使用百度飞桨语音合成服务。",
server_ip="127.0.0.1",
port=8090,
@ -177,6 +173,11 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
volume=1.0,
sample_rate=0,
output="./output.wav")
response_dict = res.json()
print(response_dict["message"])
print("Save synthesized audio successfully on %s." % (response_dict['result']['save_path']))
print("Audio duration: %f s." %(response_dict['result']['duration']))
```
Output:
@ -184,7 +185,52 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
{'description': 'success.'}
Save synthesized audio successfully on ./output.wav.
Audio duration: 3.612500 s.
Response time: 0.388317 s.
```
### 6. CLS Client Usage
**Note:** The response time will be slightly longer when using the client for the first time
- Command Line (Recommended)
```
paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
```
Usage:
```bash
paddlespeech_client cls --help
```
Arguments:
- `server_ip`: server ip. Default: 127.0.0.1
- `port`: server port. Default: 8090
- `input`(required): Audio file to be classified.
- `topk`: topk scores of classification result.
Output:
```bash
[2022-03-09 20:44:39,974] [ INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}
[2022-03-09 20:44:39,975] [ INFO] - Response time 0.104360 s.
```
- Python API
```python
from paddlespeech.server.bin.paddlespeech_client import CLSClientExecutor
import json
clsclient_executor = CLSClientExecutor()
res = clsclient_executor(
input="./zh.wav",
server_ip="127.0.0.1",
port=8090,
topk=1)
print(res.json())
```
Output:
```bash
{'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}
```
@ -195,3 +241,6 @@ Get all models supported by the ASR service via `paddlespeech_server stats --tas
### TTS model
Get all models supported by the TTS service via `paddlespeech_server stats --task tts`, where static models can be used for paddle inference inference.
### CLS model
Get all models supported by the CLS service via `paddlespeech_server stats --task cls`, where static models can be used for paddle inference inference.

@ -10,19 +10,16 @@
### 1. 安装
请看 [安装文档](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
你可以从 easymediumhard 三中方式中选择一种方式安装 PaddleSpeech。
推荐使用 **paddlepaddle 2.2.1** 或以上版本。
你可以从 mediumhard 三中方式中选择一种方式安装 PaddleSpeech。
### 2. 准备配置文件
配置文件包含服务相关的配置文件和服务中包含的语音任务相关的模型配置。 它们都在 `conf` 文件夹下。
**注意:`application.yaml` 中 `engine_backend` 的配置表示启动的服务中包含的所有语音任务。**
如果你想启动的服务中只包含某项语音任务那么你需要注释掉不需要包含的语音任务。例如你只想使用语音识别ASR服务那么你可以将语音合成TTS服务注释掉如下示例
```bash
engine_backend:
asr: 'conf/asr/asr.yaml'
#tts: 'conf/tts/tts.yaml'
```
**注意:`application.yaml` 中 `engine_backend` 的配置文件需要和 `engine_type` 的配置类型匹配。**
当`engine_backend` 的配置文件为`XXX.yaml`时,需要设置`engine_type`的配置类型为`python`;当`engine_backend` 的配置文件为`XXX_pd.yaml`时,需要设置`engine_type`的配置类型为`inference`;
配置文件可参见 `conf/application.yaml`
其中,`engine_list`表示即将启动的服务将会包含的语音引擎,格式为 <语音任务>_<引擎类型>。
目前服务集成的语音任务有: asr(语音识别)、tts(语音合成)。
目前引擎类型支持两种形式python 及 inference (Paddle Inference)
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。
@ -83,8 +80,8 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
```
### 4. ASR客户端使用方法
**注意:**初次使用客户端时响应时间会略长
### 4. ASR 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)
```
paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
@ -114,29 +111,32 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
- Python API
```python
from paddlespeech.server.bin.paddlespeech_client import ASRClientExecutor
import json
asrclient_executor = ASRClientExecutor()
asrclient_executor(
res = asrclient_executor(
input="./zh.wav",
server_ip="127.0.0.1",
port=8090,
sample_rate=16000,
lang="zh_cn",
audio_format="wav")
print(res.json())
```
输出:
```bash
{'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'transcription': '我认为跑步最重要的就是给我带来了身体健康'}}
time cost 0.604353 s.
```
### 5. TTS客户端使用方法
**注意:**初次使用客户端时响应时间会略长
```bash
paddlespeech_client tts --server_ip 127.0.0.1 --port 8090 --input "您好,欢迎使用百度飞桨语音合成服务。" --output output.wav
```
### 5. TTS 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)
```bash
paddlespeech_client tts --server_ip 127.0.0.1 --port 8090 --input "您好,欢迎使用百度飞桨语音合成服务。" --output output.wav
```
使用帮助:
```bash
@ -151,7 +151,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
- `speed`: 音频速度,该值应设置在 0 到 3 之间。 默认值1.0
- `volume`: 音频音量,该值应设置在 0 到 3 之间。 默认值: 1.0
- `sample_rate`: 采样率,可选 [0, 8000, 16000],默认与模型相同。 默认值0
- `output`: 输出音频的路径, 默认值:output.wav
- `output`: 输出音频的路径, 默认值:None表示不保存音频到本地
输出:
```bash
@ -164,9 +164,10 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
- Python API
```python
from paddlespeech.server.bin.paddlespeech_client import TTSClientExecutor
import json
ttsclient_executor = TTSClientExecutor()
ttsclient_executor(
res = ttsclient_executor(
input="您好,欢迎使用百度飞桨语音合成服务。",
server_ip="127.0.0.1",
port=8090,
@ -175,6 +176,11 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
volume=1.0,
sample_rate=0,
output="./output.wav")
response_dict = res.json()
print(response_dict["message"])
print("Save synthesized audio successfully on %s." % (response_dict['result']['save_path']))
print("Audio duration: %f s." %(response_dict['result']['duration']))
```
输出:
@ -182,13 +188,63 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
{'description': 'success.'}
Save synthesized audio successfully on ./output.wav.
Audio duration: 3.612500 s.
Response time: 0.388317 s.
```
### 5. 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)
```
paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
```
使用帮助:
```bash
paddlespeech_client cls --help
```
参数:
- `server_ip`: 服务端ip地址默认: 127.0.0.1。
- `port`: 服务端口,默认: 8090。
- `input`(必须输入): 用于分类的音频文件。
- `topk`: 分类结果的topk。
输出:
```bash
[2022-03-09 20:44:39,974] [ INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}
[2022-03-09 20:44:39,975] [ INFO] - Response time 0.104360 s.
```
- Python API
```python
from paddlespeech.server.bin.paddlespeech_client import CLSClientExecutor
import json
clsclient_executor = CLSClientExecutor()
res = clsclient_executor(
input="./zh.wav",
server_ip="127.0.0.1",
port=8090,
topk=1)
print(res.json())
```
输出:
```bash
{'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}
```
## 服务支持的模型
### ASR支持的模型
通过 `paddlespeech_server stats --task asr` 获取ASR服务支持的所有模型其中静态模型可用于 paddle inference 推理。
### TTS支持的模型
通过 `paddlespeech_server stats --task tts` 获取TTS服务支持的所有模型其中静态模型可用于 paddle inference 推理。
### CLS支持的模型
通过 `paddlespeech_server stats --task cls` 获取CLS服务支持的所有模型其中静态模型可用于 paddle inference 推理。

@ -0,0 +1,4 @@
#!/bin/bash
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav --topk 1

@ -1,27 +1,137 @@
# This is the parameter configuration file for PaddleSpeech Serving.
##################################################################
# SERVER SETTING #
##################################################################
host: '127.0.0.1'
#################################################################################
# SERVER SETTING #
#################################################################################
host: 127.0.0.1
port: 8090
##################################################################
# CONFIG FILE #
##################################################################
# add engine backend type (Options: asr, tts) and config file here.
# Adding a speech task to engine_backend means starting the service.
engine_backend:
asr: 'conf/asr/asr.yaml'
tts: 'conf/tts/tts.yaml'
# The engine_type of speech task needs to keep the same type as the config file of speech task.
# E.g: The engine_type of asr is 'python', the engine_backend of asr is 'XX/asr.yaml'
# E.g: The engine_type of asr is 'inference', the engine_backend of asr is 'XX/asr_pd.yaml'
#
# add engine type (Options: python, inference)
engine_type:
asr: 'python'
tts: 'python'
# The task format in the engin_list is: <speech task>_<engine type>
# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference']
engine_list: ['asr_python', 'tts_python', 'cls_python']
#################################################################################
# ENGINE CONFIG #
#################################################################################
################################### ASR #########################################
################### speech task: asr; engine_type: python #######################
asr_python:
model: 'conformer_wenetspeech'
lang: 'zh'
sample_rate: 16000
cfg_path: # [optional]
ckpt_path: # [optional]
decode_method: 'attention_rescoring'
force_yes: True
device: # set 'gpu:id' or 'cpu'
################### speech task: asr; engine_type: inference #######################
asr_inference:
# model_type choices=['deepspeech2offline_aishell']
model_type: 'deepspeech2offline_aishell'
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
################################### TTS #########################################
################### speech task: tts; engine_type: python #######################
tts_python:
# am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc',
# 'fastspeech2_ljspeech', 'fastspeech2_aishell3',
# 'fastspeech2_vctk']
am: 'fastspeech2_csmsc'
am_config:
am_ckpt:
am_stat:
phones_dict:
tones_dict:
speaker_dict:
spk_id: 0
# voc (vocoder) choices=['pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3',
# 'pwgan_vctk', 'mb_melgan_csmsc']
voc: 'pwgan_csmsc'
voc_config:
voc_ckpt:
voc_stat:
# others
lang: 'zh'
device: # set 'gpu:id' or 'cpu'
################### speech task: tts; engine_type: inference #######################
tts_inference:
# am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
am: 'fastspeech2_csmsc'
am_model: # the pdmodel file of your am static model (XX.pdmodel)
am_params: # the pdiparams file of your am static model (XX.pdipparams)
am_sample_rate: 24000
phones_dict:
tones_dict:
speaker_dict:
spk_id: 0
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
# voc (vocoder) choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
voc: 'pwgan_csmsc'
voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
voc_sample_rate: 24000
voc_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
# others
lang: 'zh'
################################### CLS #########################################
################### speech task: cls; engine_type: python #######################
cls_python:
# model choices=['panns_cnn14', 'panns_cnn10', 'panns_cnn6']
model: 'panns_cnn14'
cfg_path: # [optional] Config of cls task.
ckpt_path: # [optional] Checkpoint file of model.
label_file: # [optional] Label file of cls task.
device: # set 'gpu:id' or 'cpu'
################### speech task: cls; engine_type: inference #######################
cls_inference:
# model_type choices=['panns_cnn14', 'panns_cnn10', 'panns_cnn6']
model_type: 'panns_cnn14'
cfg_path:
model_path: # the pdmodel file of am static model [optional]
params_path: # the pdiparams file of am static model [optional]
label_file: # [optional] Label file of cls task.
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

@ -1,8 +0,0 @@
model: 'conformer_wenetspeech'
lang: 'zh'
sample_rate: 16000
cfg_path: # [optional]
ckpt_path: # [optional]
decode_method: 'attention_rescoring'
force_yes: True
device: # set 'gpu:id' or 'cpu'

@ -1,26 +0,0 @@
# This is the parameter configuration file for ASR server.
# These are the static models that support paddle inference.
##################################################################
# ACOUSTIC MODEL SETTING #
# am choices=['deepspeech2offline_aishell'] TODO
##################################################################
model_type: 'deepspeech2offline_aishell'
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
##################################################################
# OTHERS #
##################################################################

@ -1,32 +0,0 @@
# This is the parameter configuration file for TTS server.
##################################################################
# ACOUSTIC MODEL SETTING #
# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc',
# 'fastspeech2_ljspeech', 'fastspeech2_aishell3',
# 'fastspeech2_vctk']
##################################################################
am: 'fastspeech2_csmsc'
am_config:
am_ckpt:
am_stat:
phones_dict:
tones_dict:
speaker_dict:
spk_id: 0
##################################################################
# VOCODER SETTING #
# voc choices=['pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3',
# 'pwgan_vctk', 'mb_melgan_csmsc']
##################################################################
voc: 'pwgan_csmsc'
voc_config:
voc_ckpt:
voc_stat:
##################################################################
# OTHERS #
##################################################################
lang: 'zh'
device: # set 'gpu:id' or 'cpu'

@ -1,42 +0,0 @@
# This is the parameter configuration file for TTS server.
# These are the static models that support paddle inference.
##################################################################
# ACOUSTIC MODEL SETTING #
# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
##################################################################
am: 'fastspeech2_csmsc'
am_model: # the pdmodel file of your am static model (XX.pdmodel)
am_params: # the pdiparams file of your am static model (XX.pdipparams)
am_sample_rate: 24000
phones_dict:
tones_dict:
speaker_dict:
spk_id: 0
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
##################################################################
# VOCODER SETTING #
# voc choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
##################################################################
voc: 'pwgan_csmsc'
voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
voc_sample_rate: 24000
voc_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
##################################################################
# OTHERS #
##################################################################
lang: 'zh'

@ -1,3 +1,3 @@
#!/bin/bash
paddlespeech_server start --config_file ./conf/application.yaml
paddlespeech_server start --config_file ./conf/application.yaml

@ -35,3 +35,7 @@ We borrowed a lot of code from these repos to build `model` and `engine`, thanks
* [librosa](https://github.com/librosa/librosa/blob/main/LICENSE.md)
- ISC License
- Audio feature
* [ThreadPool](https://github.com/progschj/ThreadPool/blob/master/COPYING)
- zlib License
- ThreadPool

@ -49,17 +49,18 @@ Model Type | Dataset| Example Link | Pretrained Models| Static Models|Size (stat
WaveFlow| LJSpeech |[waveflow-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc0)|[waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/waveflow/waveflow_ljspeech_ckpt_0.3.zip)|||
Parallel WaveGAN| CSMSC |[PWGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1)|[pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip)|[pwg_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip)|5.1MB|
Parallel WaveGAN| LJSpeech |[PWGAN-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc1)|[pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip)|||
Parallel WaveGAN|AISHELL-3 |[PWGAN-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1)|[pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip)|||
Parallel WaveGAN| AISHELL-3 |[PWGAN-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1)|[pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip)|||
Parallel WaveGAN| VCTK |[PWGAN-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/voc1)|[pwg_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.5.zip)|||
|Multi Band MelGAN | CSMSC |[MB MelGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc3) | [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) <br>[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)|[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) |8.2MB|
Style MelGAN | CSMSC |[Style MelGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc4)|[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)| | |
HiFiGAN | CSMSC |[HiFiGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc5)|[hifigan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip)|[hifigan_csmsc_static_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_static_0.1.1.zip)|50MB|
HiFiGAN | AISHELL-3 |[HiFiGAN-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc5)|[hifigan_aishell3_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip)|||
WaveRNN | CSMSC |[WaveRNN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc6)|[wavernn_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip)|[wavernn_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_static_0.2.0.zip)|18MB|
### Voice Cloning
Model Type | Dataset| Example Link | Pretrained Models
:-------------:| :------------:| :-----: | :-----:
:-------------:| :------------:| :-----: | :-----: |
GE2E| AISHELL-3, etc. |[ge2e](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/ge2e)|[ge2e_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/ge2e/ge2e_ckpt_0.3.zip)
GE2E + Tactron2| AISHELL-3 |[ge2e-tactron2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vc0)|[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)
GE2E + FastSpeech2 | AISHELL-3 |[ge2e-fastspeech2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vc1)|[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)
@ -67,9 +68,9 @@ GE2E + FastSpeech2 | AISHELL-3 |[ge2e-fastspeech2-aishell3](https://github.com/
## Audio Classification Models
Model Type | Dataset| Example Link | Pretrained Models
:-------------:| :------------:| :-----: | :-----:
PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams)
Model Type | Dataset| Example Link | Pretrained Models | Static Models
:-------------:| :------------:| :-----: | :-----: | :-----:
PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams) | [panns_cnn6_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz)(18M), [panns_cnn10_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz)(19M), [panns_cnn14_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz)(289M)
PANN | ESC-50 |[pann-esc50](../../examples/esc50/cls0)|[esc50_cnn6.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn6.tar.gz), [esc50_cnn10.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn10.tar.gz), [esc50_cnn14.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn14.tar.gz)
## Punctuation Restoration Models

@ -4,18 +4,44 @@ config_path=$1
train_output_path=$2
ckpt_name=$3
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize.py \
--am=fastspeech2_aishell3 \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=pwgan_aishell3 \
--voc_config=pwg_aishell3_ckpt_0.5/default.yaml \
--voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
--voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
--test_metadata=dump/test/norm/metadata.jsonl \
--output_dir=${train_output_path}/test \
--phones_dict=dump/phone_id_map.txt \
--speaker_dict=dump/speaker_id_map.txt
stage=0
stop_stage=0
# pwgan
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize.py \
--am=fastspeech2_aishell3 \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=pwgan_aishell3 \
--voc_config=pwg_aishell3_ckpt_0.5/default.yaml \
--voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
--voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
--test_metadata=dump/test/norm/metadata.jsonl \
--output_dir=${train_output_path}/test \
--phones_dict=dump/phone_id_map.txt \
--speaker_dict=dump/speaker_id_map.txt
fi
# hifigan
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 ${BIN_DIR}/../synthesize.py \
--am=fastspeech2_aishell3 \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=hifigan_aishell3 \
--voc_config=hifigan_aishell3_ckpt_0.2.0/default.yaml \
--voc_ckpt=hifigan_aishell3_ckpt_0.2.0/snapshot_iter_2500000.pd \
--voc_stat=hifigan_aishell3_ckpt_0.2.0/feats_stats.npy \
--test_metadata=dump/test/norm/metadata.jsonl \
--output_dir=${train_output_path}/test \
--phones_dict=dump/phone_id_map.txt \
--speaker_dict=dump/speaker_id_map.txt
fi

@ -4,21 +4,50 @@ config_path=$1
train_output_path=$2
ckpt_name=$3
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_aishell3 \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=pwgan_aishell3 \
--voc_config=pwg_aishell3_ckpt_0.5/default.yaml \
--voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
--voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../sentences.txt \
--output_dir=${train_output_path}/test_e2e \
--phones_dict=dump/phone_id_map.txt \
--speaker_dict=dump/speaker_id_map.txt \
--spk_id=0 \
--inference_dir=${train_output_path}/inference
stage=0
stop_stage=0
# pwgan
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_aishell3 \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=pwgan_aishell3 \
--voc_config=pwg_aishell3_ckpt_0.5/default.yaml \
--voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
--voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../sentences.txt \
--output_dir=${train_output_path}/test_e2e \
--phones_dict=dump/phone_id_map.txt \
--speaker_dict=dump/speaker_id_map.txt \
--spk_id=0 \
--inference_dir=${train_output_path}/inference
fi
# hifigan
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "in hifigan syn_e2e"
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_aishell3 \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=fastspeech2_nosil_aishell3_ckpt_0.4/speech_stats.npy \
--voc=hifigan_aishell3 \
--voc_config=hifigan_aishell3_ckpt_0.2.0/default.yaml \
--voc_ckpt=hifigan_aishell3_ckpt_0.2.0/snapshot_iter_2500000.pdz \
--voc_stat=hifigan_aishell3_ckpt_0.2.0/feats_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../sentences.txt \
--output_dir=${train_output_path}/test_e2e \
--phones_dict=fastspeech2_nosil_aishell3_ckpt_0.4/phone_id_map.txt \
--speaker_dict=fastspeech2_nosil_aishell3_ckpt_0.4/speaker_id_map.txt \
--spk_id=0 \
--inference_dir=${train_output_path}/inference
fi

@ -1,6 +1,6 @@
#!/bin/bash
stage=3
stage=0
stop_stage=100
config_path=$1

@ -3,7 +3,7 @@
set -e
source path.sh
gpus=0
gpus=0,1
stage=0
stop_stage=100

@ -0,0 +1,156 @@
# HiFiGAN with AISHELL-3
This example contains code used to train a [HiFiGAN](https://arxiv.org/abs/2010.05646) model with [AISHELL-3](http://www.aishelltech.com/aishell_3).
AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus that could be used to train multi-speaker Text-to-Speech (TTS) systems.
## Dataset
### Download and Extract
Download AISHELL-3.
```bash
wget https://www.openslr.org/resources/93/data_aishell3.tgz
```
Extract AISHELL-3.
```bash
mkdir data_aishell3
tar zxvf data_aishell3.tgz -C data_aishell3
```
### Get MFA Result and Extract
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
## Get Started
Assume the path to the dataset is `~/datasets/data_aishell3`.
Assume the path to the MFA result of AISHELL-3 is `./aishell3_alignment_tone`.
Run the command below to
1. **source path**.
2. preprocess the dataset.
3. train the model.
4. synthesize wavs.
- synthesize waveform from `metadata.jsonl`.
```bash
./run.sh
```
You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
```bash
./run.sh --stage 0 --stop-stage 0
```
### Data Preprocessing
```bash
./local/preprocess.sh ${conf_path}
```
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
```text
dump
├── dev
│ ├── norm
│ └── raw
├── test
│ ├── norm
│ └── raw
└── train
├── norm
├── raw
└── feats_stats.npy
```
The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
### Model Training
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
```
`./local/train.sh` calls `${BIN_DIR}/train.py`.
Here's the complete help message.
```text
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
[--ngpu NGPU] [--batch-size BATCH_SIZE] [--max-iter MAX_ITER]
[--run-benchmark RUN_BENCHMARK]
[--profiler_options PROFILER_OPTIONS]
Train a ParallelWaveGAN model.
optional arguments:
-h, --help show this help message and exit
--config CONFIG config file to overwrite default config.
--train-metadata TRAIN_METADATA
training data.
--dev-metadata DEV_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
benchmark:
arguments related to benchmark.
--batch-size BATCH_SIZE
batch size.
--max-iter MAX_ITER train max steps.
--run-benchmark RUN_BENCHMARK
runing benchmark or not, if True, use the --batch-size
and --max-iter.
--profiler_options PROFILER_OPTIONS
The option of profiler, which should be in format
"key1=value1;key2=value2;key3=value3".
```
1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
### Synthesizing
`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
```
```text
usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG]
[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
[--output-dir OUTPUT_DIR] [--ngpu NGPU]
Synthesize with GANVocoder.
optional arguments:
-h, --help show this help message and exit
--generator-type GENERATOR_TYPE
type of GANVocoder, should in {pwgan, mb_melgan,
style_melgan, } now
--config CONFIG GANVocoder config file.
--checkpoint CHECKPOINT
snapshot to load.
--test-metadata TEST_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
```
1. `--config` config file. You should use the same config with which the model is trained.
2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory.
3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
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).
Model | Step | eval/generator_loss | eval/mel_loss| eval/feature_matching_loss
:-------------:| :------------:| :-----: | :-----: | :--------:
default| 1(gpu) x 2500000|24.060|0.1068|7.499
HiFiGAN checkpoint contains files listed below.
```text
hifigan_aishell3_ckpt_0.2.0
├── default.yaml # default config used to train hifigan
├── feats_stats.npy # statistics used to normalize spectrogram when training hifigan
└── snapshot_iter_2500000.pdz # generator parameters of hifigan
```
## Acknowledgement
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.

@ -0,0 +1,168 @@
# This is the configuration file for AISHELL-3 dataset.
# This configuration is based on HiFiGAN V1, which is
# an official configuration. But I found that the optimizer
# setting does not work well with my implementation.
# So I changed optimizer settings as follows:
# - AdamW -> Adam
# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
# - Scheduler: ExponentialLR -> MultiStepLR
# To match the shift size difference, the upsample scales
# is also modified from the original 256 shift setting.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
fs: 24000 # Sampling rate.
n_fft: 2048 # FFT size (samples).
n_shift: 300 # Hop size (samples). 12.5ms
win_length: 1200 # Window length (samples). 50ms
# If set to null, it will be the same as fft_size.
window: "hann" # Window function.
n_mels: 80 # Number of mel basis.
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
###########################################################
# GENERATOR NETWORK ARCHITECTURE SETTING #
###########################################################
generator_params:
in_channels: 80 # Number of input channels.
out_channels: 1 # Number of output channels.
channels: 512 # Number of initial channels.
kernel_size: 7 # Kernel size of initial and final conv layers.
upsample_scales: [5, 5, 4, 3] # Upsampling scales.
upsample_kernel_sizes: [10, 10, 8, 6] # Kernel size for upsampling layers.
resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
resblock_dilations: # Dilations for residual blocks.
- [1, 3, 5]
- [1, 3, 5]
- [1, 3, 5]
use_additional_convs: True # Whether to use additional conv layer in residual blocks.
bias: True # Whether to use bias parameter in conv.
nonlinear_activation: "leakyrelu" # Nonlinear activation type.
nonlinear_activation_params: # Nonlinear activation paramters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
###########################################################
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
###########################################################
discriminator_params:
scales: 3 # Number of multi-scale discriminator.
scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
scale_downsample_pooling_params:
kernel_size: 4 # Pooling kernel size.
stride: 2 # Pooling stride.
padding: 2 # Padding size.
scale_discriminator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
channels: 128 # Initial number of channels.
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
max_groups: 16 # Maximum number of groups in downsampling conv layers.
bias: True
downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
nonlinear_activation: "leakyrelu" # Nonlinear activation.
nonlinear_activation_params:
negative_slope: 0.1
follow_official_norm: True # Whether to follow the official norm setting.
periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
period_discriminator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_sizes: [5, 3] # List of kernel sizes.
channels: 32 # Initial number of channels.
downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
bias: True # Whether to use bias parameter in conv layer."
nonlinear_activation: "leakyrelu" # Nonlinear activation.
nonlinear_activation_params: # Nonlinear activation paramters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.
###########################################################
# STFT LOSS SETTING #
###########################################################
use_stft_loss: False # Whether to use multi-resolution STFT loss.
use_mel_loss: True # Whether to use Mel-spectrogram loss.
mel_loss_params:
fs: 24000
fft_size: 2048
hop_size: 300
win_length: 1200
window: "hann"
num_mels: 80
fmin: 0
fmax: 12000
log_base: null
generator_adv_loss_params:
average_by_discriminators: False # Whether to average loss by #discriminators.
discriminator_adv_loss_params:
average_by_discriminators: False # Whether to average loss by #discriminators.
use_feat_match_loss: True
feat_match_loss_params:
average_by_discriminators: False # Whether to average loss by #discriminators.
average_by_layers: False # Whether to average loss by #layers in each discriminator.
include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
###########################################################
# ADVERSARIAL LOSS SETTING #
###########################################################
lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 16 # Batch size.
batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size.
num_workers: 2 # Number of workers in DataLoader.
###########################################################
# OPTIMIZER & SCHEDULER SETTING #
###########################################################
generator_optimizer_params:
beta1: 0.5
beta2: 0.9
weight_decay: 0.0 # Generator's weight decay coefficient.
generator_scheduler_params:
learning_rate: 2.0e-4 # Generator's learning rate.
gamma: 0.5 # Generator's scheduler gamma.
milestones: # At each milestone, lr will be multiplied by gamma.
- 200000
- 400000
- 600000
- 800000
generator_grad_norm: -1 # Generator's gradient norm.
discriminator_optimizer_params:
beta1: 0.5
beta2: 0.9
weight_decay: 0.0 # Discriminator's weight decay coefficient.
discriminator_scheduler_params:
learning_rate: 2.0e-4 # Discriminator's learning rate.
gamma: 0.5 # Discriminator's scheduler gamma.
milestones: # At each milestone, lr will be multiplied by gamma.
- 200000
- 400000
- 600000
- 800000
discriminator_grad_norm: -1 # Discriminator's gradient norm.
###########################################################
# INTERVAL SETTING #
###########################################################
generator_train_start_steps: 1 # Number of steps to start to train discriminator.
discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
train_max_steps: 2500000 # Number of training steps.
save_interval_steps: 5000 # Interval steps to save checkpoint.
eval_interval_steps: 1000 # Interval steps to evaluate the network.
###########################################################
# OTHER SETTING #
###########################################################
num_snapshots: 10 # max number of snapshots to keep while training
seed: 42 # random seed for paddle, random, and np.random

@ -0,0 +1,55 @@
#!/bin/bash
stage=0
stop_stage=100
config_path=$1
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# get durations from MFA's result
echo "Generate durations.txt from MFA results ..."
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
--inputdir=./aishell3_alignment_tone \
--output=durations.txt \
--config=${config_path}
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# extract features
echo "Extract features ..."
python3 ${BIN_DIR}/../preprocess.py \
--rootdir=~/datasets/data_aishell3/ \
--dataset=aishell3 \
--dumpdir=dump \
--dur-file=durations.txt \
--config=${config_path} \
--cut-sil=True \
--num-cpu=20
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# get features' stats(mean and std)
echo "Get features' stats ..."
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
--metadata=dump/train/raw/metadata.jsonl \
--field-name="feats"
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# normalize, dev and test should use train's stats
echo "Normalize ..."
python3 ${BIN_DIR}/../normalize.py \
--metadata=dump/train/raw/metadata.jsonl \
--dumpdir=dump/train/norm \
--stats=dump/train/feats_stats.npy
python3 ${BIN_DIR}/../normalize.py \
--metadata=dump/dev/raw/metadata.jsonl \
--dumpdir=dump/dev/norm \
--stats=dump/train/feats_stats.npy
python3 ${BIN_DIR}/../normalize.py \
--metadata=dump/test/raw/metadata.jsonl \
--dumpdir=dump/test/norm \
--stats=dump/train/feats_stats.npy
fi

@ -0,0 +1,14 @@
#!/bin/bash
config_path=$1
train_output_path=$2
ckpt_name=$3
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize.py \
--config=${config_path} \
--checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
--test-metadata=dump/test/norm/metadata.jsonl \
--output-dir=${train_output_path}/test \
--generator-type=hifigan

@ -0,0 +1,13 @@
#!/bin/bash
config_path=$1
train_output_path=$2
FLAGS_cudnn_exhaustive_search=true \
FLAGS_conv_workspace_size_limit=4000 \
python ${BIN_DIR}/train.py \
--train-metadata=dump/train/norm/metadata.jsonl \
--dev-metadata=dump/dev/norm/metadata.jsonl \
--config=${config_path} \
--output-dir=${train_output_path} \
--ngpu=1

@ -0,0 +1,13 @@
#!/bin/bash
export MAIN_ROOT=`realpath ${PWD}/../../../`
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
export LC_ALL=C
export PYTHONDONTWRITEBYTECODE=1
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
MODEL=hifigan
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}

@ -0,0 +1,32 @@
#!/bin/bash
set -e
source path.sh
gpus=0
stage=0
stop_stage=100
conf_path=conf/default.yaml
train_output_path=exp/default
ckpt_name=snapshot_iter_5000.pdz
# with the following command, you can choose the stage range you want to run
# such as `./run.sh --stage 0 --stop-stage 0`
# this can not be mixed use with `$1`, `$2` ...
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data
./local/preprocess.sh ${conf_path} || exit -1
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# synthesize
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi

@ -7,7 +7,7 @@ ckpt_name=$3
stage=0
stop_stage=0
# TODO: tacotron2 动转静的结果没有态图的响亮, 可能还是 decode 的时候某个函数动静不对齐
# TODO: tacotron2 动转静的结果没有态图的响亮, 可能还是 decode 的时候某个函数动静不对齐
# pwgan
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
FLAGS_allocator_strategy=naive_best_fit \

@ -14,7 +14,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--am=speedyspeech_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--am_stat=dump/train/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 \
@ -34,7 +34,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
--am=speedyspeech_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--am_stat=dump/train/feats_stats.npy \
--voc=mb_melgan_csmsc \
--voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \
--voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\
@ -53,7 +53,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
--am=speedyspeech_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--am_stat=dump/train/feats_stats.npy \
--voc=style_melgan_csmsc \
--voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \
--voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \
@ -73,7 +73,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
--am=speedyspeech_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--am_stat=dump/train/feats_stats.npy \
--voc=hifigan_csmsc \
--voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \
--voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \
@ -93,7 +93,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
--am=speedyspeech_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--am_stat=dump/train/feats_stats.npy \
--voc=wavernn_csmsc \
--voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
--voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \

@ -0,0 +1,133 @@
# HiFiGAN with the LJSpeech-1.1
This example contains code used to train a [HiFiGAN](https://arxiv.org/abs/2010.05646) model with [LJSpeech-1.1](https://keithito.com/LJ-Speech-Dataset/).
## Dataset
### Download and Extract
Download LJSpeech-1.1 from the [official website](https://keithito.com/LJ-Speech-Dataset/).
### Get MFA Result and Extract
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut the silence in the edge of audio.
You can download from here [ljspeech_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) of our repo.
## Get Started
Assume the path to the dataset is `~/datasets/LJSpeech-1.1`.
Assume the path to the MFA result of LJSpeech-1.1 is `./ljspeech_alignment`.
Run the command below to
1. **source path**.
2. preprocess the dataset.
3. train the model.
4. synthesize wavs.
- synthesize waveform from `metadata.jsonl`.
```bash
./run.sh
```
You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, running the following command will only preprocess the dataset.
```bash
./run.sh --stage 0 --stop-stage 0
```
### Data Preprocessing
```bash
./local/preprocess.sh ${conf_path}
```
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
```text
dump
├── dev
│ ├── norm
│ └── raw
├── test
│ ├── norm
│ └── raw
└── train
├── norm
├── raw
└── feats_stats.npy
```
The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
### Model Training
`./local/train.sh` calls `${BIN_DIR}/train.py`.
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
```
Here's the complete help message.
```text
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
[--ngpu NGPU] [--batch-size BATCH_SIZE] [--max-iter MAX_ITER]
[--run-benchmark RUN_BENCHMARK]
[--profiler_options PROFILER_OPTIONS]
Train a ParallelWaveGAN model.
optional arguments:
-h, --help show this help message and exit
--config CONFIG config file to overwrite default config.
--train-metadata TRAIN_METADATA
training data.
--dev-metadata DEV_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
benchmark:
arguments related to benchmark.
--batch-size BATCH_SIZE
batch size.
--max-iter MAX_ITER train max steps.
--run-benchmark RUN_BENCHMARK
runing benchmark or not, if True, use the --batch-size
and --max-iter.
--profiler_options PROFILER_OPTIONS
The option of profiler, which should be in format
"key1=value1;key2=value2;key3=value3".
```
1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
### Synthesizing
`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
```
```text
usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG]
[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
[--output-dir OUTPUT_DIR] [--ngpu NGPU]
Synthesize with GANVocoder.
optional arguments:
-h, --help show this help message and exit
--generator-type GENERATOR_TYPE
type of GANVocoder, should in {pwgan, mb_melgan,
style_melgan, } now
--config CONFIG GANVocoder config file.
--checkpoint CHECKPOINT
snapshot to load.
--test-metadata TEST_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
```
1. `--config` parallel wavegan config file. You should use the same config with which the model is trained.
2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory.
3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
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 Model
## Acknowledgement
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.

@ -0,0 +1,167 @@
# This is the configuration file for LJSpeech dataset.
# This configuration is based on HiFiGAN V1, which is an official configuration.
# But I found that the optimizer setting does not work well with my implementation.
# So I changed optimizer settings as follows:
# - AdamW -> Adam
# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
# - Scheduler: ExponentialLR -> MultiStepLR
# To match the shift size difference, the upsample scales is also modified from the original 256 shift setting.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
fs: 22050 # Sampling rate.
n_fft: 1024 # FFT size (samples).
n_shift: 256 # Hop size (samples). 11.6ms
win_length: null # Window length (samples).
# If set to null, it will be the same as fft_size.
window: "hann" # Window function.
n_mels: 80 # Number of mel basis.
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
###########################################################
# GENERATOR NETWORK ARCHITECTURE SETTING #
###########################################################
generator_params:
in_channels: 80 # Number of input channels.
out_channels: 1 # Number of output channels.
channels: 512 # Number of initial channels.
kernel_size: 7 # Kernel size of initial and final conv layers.
upsample_scales: [8, 8, 2, 2] # Upsampling scales.
upsample_kernel_sizes: [16, 16, 4, 4] # Kernel size for upsampling layers.
resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
resblock_dilations: # Dilations for residual blocks.
- [1, 3, 5]
- [1, 3, 5]
- [1, 3, 5]
use_additional_convs: True # Whether to use additional conv layer in residual blocks.
bias: True # Whether to use bias parameter in conv.
nonlinear_activation: "leakyrelu" # Nonlinear activation type.
nonlinear_activation_params: # Nonlinear activation paramters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
###########################################################
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
###########################################################
discriminator_params:
scales: 3 # Number of multi-scale discriminator.
scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
scale_downsample_pooling_params:
kernel_size: 4 # Pooling kernel size.
stride: 2 # Pooling stride.
padding: 2 # Padding size.
scale_discriminator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
channels: 128 # Initial number of channels.
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
max_groups: 16 # Maximum number of groups in downsampling conv layers.
bias: True
downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
nonlinear_activation: "leakyrelu" # Nonlinear activation.
nonlinear_activation_params:
negative_slope: 0.1
follow_official_norm: True # Whether to follow the official norm setting.
periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
period_discriminator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_sizes: [5, 3] # List of kernel sizes.
channels: 32 # Initial number of channels.
downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
bias: True # Whether to use bias parameter in conv layer."
nonlinear_activation: "leakyrelu" # Nonlinear activation.
nonlinear_activation_params: # Nonlinear activation paramters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.
###########################################################
# STFT LOSS SETTING #
###########################################################
use_stft_loss: False # Whether to use multi-resolution STFT loss.
use_mel_loss: True # Whether to use Mel-spectrogram loss.
mel_loss_params:
fs: 22050
fft_size: 1024
hop_size: 256
win_length: null
window: "hann"
num_mels: 80
fmin: 0
fmax: 11025
log_base: null
generator_adv_loss_params:
average_by_discriminators: False # Whether to average loss by #discriminators.
discriminator_adv_loss_params:
average_by_discriminators: False # Whether to average loss by #discriminators.
use_feat_match_loss: True
feat_match_loss_params:
average_by_discriminators: False # Whether to average loss by #discriminators.
average_by_layers: False # Whether to average loss by #layers in each discriminator.
include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
###########################################################
# ADVERSARIAL LOSS SETTING #
###########################################################
lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 16 # Batch size.
batch_max_steps: 8192 # Length of each audio in batch. Make sure dividable by hop_size.
num_workers: 2 # Number of workers in DataLoader.
###########################################################
# OPTIMIZER & SCHEDULER SETTING #
###########################################################
generator_optimizer_params:
beta1: 0.5
beta2: 0.9
weight_decay: 0.0 # Generator's weight decay coefficient.
generator_scheduler_params:
learning_rate: 2.0e-4 # Generator's learning rate.
gamma: 0.5 # Generator's scheduler gamma.
milestones: # At each milestone, lr will be multiplied by gamma.
- 200000
- 400000
- 600000
- 800000
generator_grad_norm: -1 # Generator's gradient norm.
discriminator_optimizer_params:
beta1: 0.5
beta2: 0.9
weight_decay: 0.0 # Discriminator's weight decay coefficient.
discriminator_scheduler_params:
learning_rate: 2.0e-4 # Discriminator's learning rate.
gamma: 0.5 # Discriminator's scheduler gamma.
milestones: # At each milestone, lr will be multiplied by gamma.
- 200000
- 400000
- 600000
- 800000
discriminator_grad_norm: -1 # Discriminator's gradient norm.
###########################################################
# INTERVAL SETTING #
###########################################################
generator_train_start_steps: 1 # Number of steps to start to train discriminator.
discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
train_max_steps: 2500000 # Number of training steps.
save_interval_steps: 5000 # Interval steps to save checkpoint.
eval_interval_steps: 1000 # Interval steps to evaluate the network.
###########################################################
# OTHER SETTING #
###########################################################
num_snapshots: 10 # max number of snapshots to keep while training
seed: 42 # random seed for paddle, random, and np.random

@ -0,0 +1,55 @@
#!/bin/bash
stage=0
stop_stage=100
config_path=$1
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# get durations from MFA's result
echo "Generate durations.txt from MFA results ..."
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
--inputdir=./ljspeech_alignment \
--output=durations.txt \
--config=${config_path}
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# extract features
echo "Extract features ..."
python3 ${BIN_DIR}/../preprocess.py \
--rootdir=~/datasets/LJSpeech-1.1/ \
--dataset=ljspeech \
--dumpdir=dump \
--dur-file=durations.txt \
--config=${config_path} \
--cut-sil=True \
--num-cpu=20
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# get features' stats(mean and std)
echo "Get features' stats ..."
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
--metadata=dump/train/raw/metadata.jsonl \
--field-name="feats"
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# normalize, dev and test should use train's stats
echo "Normalize ..."
python3 ${BIN_DIR}/../normalize.py \
--metadata=dump/train/raw/metadata.jsonl \
--dumpdir=dump/train/norm \
--stats=dump/train/feats_stats.npy
python3 ${BIN_DIR}/../normalize.py \
--metadata=dump/dev/raw/metadata.jsonl \
--dumpdir=dump/dev/norm \
--stats=dump/train/feats_stats.npy
python3 ${BIN_DIR}/../normalize.py \
--metadata=dump/test/raw/metadata.jsonl \
--dumpdir=dump/test/norm \
--stats=dump/train/feats_stats.npy
fi

@ -0,0 +1,14 @@
#!/bin/bash
config_path=$1
train_output_path=$2
ckpt_name=$3
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize.py \
--config=${config_path} \
--checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
--test-metadata=dump/test/norm/metadata.jsonl \
--output-dir=${train_output_path}/test \
--generator-type=hifigan

@ -0,0 +1,13 @@
#!/bin/bash
config_path=$1
train_output_path=$2
FLAGS_cudnn_exhaustive_search=true \
FLAGS_conv_workspace_size_limit=4000 \
python ${BIN_DIR}/train.py \
--train-metadata=dump/train/norm/metadata.jsonl \
--dev-metadata=dump/dev/norm/metadata.jsonl \
--config=${config_path} \
--output-dir=${train_output_path} \
--ngpu=1

@ -0,0 +1,13 @@
#!/bin/bash
export MAIN_ROOT=`realpath ${PWD}/../../../`
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
export LC_ALL=C
export PYTHONDONTWRITEBYTECODE=1
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
MODEL=hifigan
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}

@ -0,0 +1,32 @@
#!/bin/bash
set -e
source path.sh
gpus=0,1
stage=0
stop_stage=100
conf_path=conf/default.yaml
train_output_path=exp/default
ckpt_name=snapshot_iter_5000.pdz
# with the following command, you can choose the stage range you want to run
# such as `./run.sh --stage 0 --stop-stage 0`
# this can not be mixed use with `$1`, `$2` ...
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data
./local/preprocess.sh ${conf_path} || exit -1
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# synthesize
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi

@ -1 +1,9 @@
# Changelog
Date: 2022-3-15, Author: Xiaojie Chen.
- kaldi and librosa mfcc, fbank, spectrogram.
- unit test and benchmark.
Date: 2022-2-25, Author: Hui Zhang.
- Refactor architecture.
- dtw distance and mcd style dtw.

@ -1,170 +0,0 @@
# 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.
from typing import List
import numpy as np
from numpy import ndarray as array
from ..backends import depth_convert
from ..utils import ParameterError
__all__ = [
'depth_augment',
'spect_augment',
'random_crop1d',
'random_crop2d',
'adaptive_spect_augment',
]
def randint(high: int) -> int:
"""Generate one random integer in range [0 high)
This is a helper function for random data augmentaiton
"""
return int(np.random.randint(0, high=high))
def rand() -> float:
"""Generate one floating-point number in range [0 1)
This is a helper function for random data augmentaiton
"""
return float(np.random.rand(1))
def depth_augment(y: array,
choices: List=['int8', 'int16'],
probs: List[float]=[0.5, 0.5]) -> array:
""" Audio depth augmentation
Do audio depth augmentation to simulate the distortion brought by quantization.
"""
assert len(probs) == len(
choices
), 'number of choices {} must be equal to size of probs {}'.format(
len(choices), len(probs))
depth = np.random.choice(choices, p=probs)
src_depth = y.dtype
y1 = depth_convert(y, depth)
y2 = depth_convert(y1, src_depth)
return y2
def adaptive_spect_augment(spect: array, tempo_axis: int=0,
level: float=0.1) -> array:
"""Do adpative spectrogram augmentation
The level of the augmentation is gowern by the paramter level,
ranging from 0 to 1, with 0 represents no augmentation
"""
assert spect.ndim == 2., 'only supports 2d tensor or numpy array'
if tempo_axis == 0:
nt, nf = spect.shape
else:
nf, nt = spect.shape
time_mask_width = int(nt * level * 0.5)
freq_mask_width = int(nf * level * 0.5)
num_time_mask = int(10 * level)
num_freq_mask = int(10 * level)
if tempo_axis == 0:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[start:start + time_mask_width, :] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[:, start:start + freq_mask_width] = 0
else:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[:, start:start + time_mask_width] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[start:start + freq_mask_width, :] = 0
return spect
def spect_augment(spect: array,
tempo_axis: int=0,
max_time_mask: int=3,
max_freq_mask: int=3,
max_time_mask_width: int=30,
max_freq_mask_width: int=20) -> array:
"""Do spectrogram augmentation in both time and freq axis
Reference:
"""
assert spect.ndim == 2., 'only supports 2d tensor or numpy array'
if tempo_axis == 0:
nt, nf = spect.shape
else:
nf, nt = spect.shape
num_time_mask = randint(max_time_mask)
num_freq_mask = randint(max_freq_mask)
time_mask_width = randint(max_time_mask_width)
freq_mask_width = randint(max_freq_mask_width)
if tempo_axis == 0:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[start:start + time_mask_width, :] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[:, start:start + freq_mask_width] = 0
else:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[:, start:start + time_mask_width] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[start:start + freq_mask_width, :] = 0
return spect
def random_crop1d(y: array, crop_len: int) -> array:
""" Do random cropping on 1d input signal
The input is a 1d signal, typically a sound waveform
"""
if y.ndim != 1:
'only accept 1d tensor or numpy array'
n = len(y)
idx = randint(n - crop_len)
return y[idx:idx + crop_len]
def random_crop2d(s: array, crop_len: int, tempo_axis: int=0) -> array:
""" Do random cropping for 2D array, typically a spectrogram.
The cropping is done in temporal direction on the time-freq input signal.
"""
if tempo_axis >= s.ndim:
raise ParameterError('axis out of range')
n = s.shape[tempo_axis]
idx = randint(high=n - crop_len)
sli = [slice(None) for i in range(s.ndim)]
sli[tempo_axis] = slice(idx, idx + crop_len)
out = s[tuple(sli)]
return out

@ -1,461 +0,0 @@
# 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 math
from functools import partial
from typing import Optional
from typing import Union
import paddle
import paddle.nn as nn
from .window import get_window
__all__ = [
'Spectrogram',
'MelSpectrogram',
'LogMelSpectrogram',
]
def hz_to_mel(freq: Union[paddle.Tensor, float],
htk: bool=False) -> Union[paddle.Tensor, float]:
"""Convert Hz to Mels.
Parameters:
freq: the input tensor of arbitrary shape, or a single floating point number.
htk: use HTK formula to do the conversion.
The default value is False.
Returns:
The frequencies represented in Mel-scale.
"""
if htk:
if isinstance(freq, paddle.Tensor):
return 2595.0 * paddle.log10(1.0 + freq / 700.0)
else:
return 2595.0 * math.log10(1.0 + freq / 700.0)
# Fill in the linear part
f_min = 0.0
f_sp = 200.0 / 3
mels = (freq - f_min) / f_sp
# Fill in the log-scale part
min_log_hz = 1000.0 # beginning of log region (Hz)
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
logstep = math.log(6.4) / 27.0 # step size for log region
if isinstance(freq, paddle.Tensor):
target = min_log_mel + paddle.log(
freq / min_log_hz + 1e-10) / logstep # prevent nan with 1e-10
mask = (freq > min_log_hz).astype(freq.dtype)
mels = target * mask + mels * (
1 - mask) # will replace by masked_fill OP in future
else:
if freq >= min_log_hz:
mels = min_log_mel + math.log(freq / min_log_hz + 1e-10) / logstep
return mels
def mel_to_hz(mel: Union[float, paddle.Tensor],
htk: bool=False) -> Union[float, paddle.Tensor]:
"""Convert mel bin numbers to frequencies.
Parameters:
mel: the mel frequency represented as a tensor of arbitrary shape, or a floating point number.
htk: use HTK formula to do the conversion.
Returns:
The frequencies represented in hz.
"""
if htk:
return 700.0 * (10.0**(mel / 2595.0) - 1.0)
f_min = 0.0
f_sp = 200.0 / 3
freqs = f_min + f_sp * mel
# And now the nonlinear scale
min_log_hz = 1000.0 # beginning of log region (Hz)
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
logstep = math.log(6.4) / 27.0 # step size for log region
if isinstance(mel, paddle.Tensor):
target = min_log_hz * paddle.exp(logstep * (mel - min_log_mel))
mask = (mel > min_log_mel).astype(mel.dtype)
freqs = target * mask + freqs * (
1 - mask) # will replace by masked_fill OP in future
else:
if mel >= min_log_mel:
freqs = min_log_hz * math.exp(logstep * (mel - min_log_mel))
return freqs
def mel_frequencies(n_mels: int=64,
f_min: float=0.0,
f_max: float=11025.0,
htk: bool=False,
dtype: str=paddle.float32):
"""Compute mel frequencies.
Parameters:
n_mels(int): number of Mel bins.
f_min(float): the lower cut-off frequency, below which the filter response is zero.
f_max(float): the upper cut-off frequency, above which the filter response is zero.
htk(bool): whether to use htk formula.
dtype(str): the datatype of the return frequencies.
Returns:
The frequencies represented in Mel-scale
"""
# 'Center freqs' of mel bands - uniformly spaced between limits
min_mel = hz_to_mel(f_min, htk=htk)
max_mel = hz_to_mel(f_max, htk=htk)
mels = paddle.linspace(min_mel, max_mel, n_mels, dtype=dtype)
freqs = mel_to_hz(mels, htk=htk)
return freqs
def fft_frequencies(sr: int, n_fft: int, dtype: str=paddle.float32):
"""Compute fourier frequencies.
Parameters:
sr(int): the audio sample rate.
n_fft(float): the number of fft bins.
dtype(str): the datatype of the return frequencies.
Returns:
The frequencies represented in hz.
"""
return paddle.linspace(0, float(sr) / 2, int(1 + n_fft // 2), dtype=dtype)
def compute_fbank_matrix(sr: int,
n_fft: int,
n_mels: int=64,
f_min: float=0.0,
f_max: Optional[float]=None,
htk: bool=False,
norm: Union[str, float]='slaney',
dtype: str=paddle.float32):
"""Compute fbank matrix.
Parameters:
sr(int): the audio sample rate.
n_fft(int): the number of fft bins.
n_mels(int): the number of Mel bins.
f_min(float): the lower cut-off frequency, below which the filter response is zero.
f_max(float): the upper cut-off frequency, above which the filter response is zero.
htk: whether to use htk formula.
return_complex(bool): whether to return complex matrix. If True, the matrix will
be complex type. Otherwise, the real and image part will be stored in the last
axis of returned tensor.
dtype(str): the datatype of the returned fbank matrix.
Returns:
The fbank matrix of shape (n_mels, int(1+n_fft//2)).
Shape:
output: (n_mels, int(1+n_fft//2))
"""
if f_max is None:
f_max = float(sr) / 2
# Initialize the weights
weights = paddle.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype)
# Center freqs of each FFT bin
fftfreqs = fft_frequencies(sr=sr, n_fft=n_fft, dtype=dtype)
# 'Center freqs' of mel bands - uniformly spaced between limits
mel_f = mel_frequencies(
n_mels + 2, f_min=f_min, f_max=f_max, htk=htk, dtype=dtype)
fdiff = mel_f[1:] - mel_f[:-1] #np.diff(mel_f)
ramps = mel_f.unsqueeze(1) - fftfreqs.unsqueeze(0)
#ramps = np.subtract.outer(mel_f, fftfreqs)
for i in range(n_mels):
# lower and upper slopes for all bins
lower = -ramps[i] / fdiff[i]
upper = ramps[i + 2] / fdiff[i + 1]
# .. then intersect them with each other and zero
weights[i] = paddle.maximum(
paddle.zeros_like(lower), paddle.minimum(lower, upper))
# Slaney-style mel is scaled to be approx constant energy per channel
if norm == 'slaney':
enorm = 2.0 / (mel_f[2:n_mels + 2] - mel_f[:n_mels])
weights *= enorm.unsqueeze(1)
elif isinstance(norm, int) or isinstance(norm, float):
weights = paddle.nn.functional.normalize(weights, p=norm, axis=-1)
return weights
def power_to_db(magnitude: paddle.Tensor,
ref_value: float=1.0,
amin: float=1e-10,
top_db: Optional[float]=None) -> paddle.Tensor:
"""Convert a power spectrogram (amplitude squared) to decibel (dB) units.
The function computes the scaling ``10 * log10(x / ref)`` in a numerically
stable way.
Parameters:
magnitude(Tensor): the input magnitude tensor of any shape.
ref_value(float): the reference value. If smaller than 1.0, the db level
of the signal will be pulled up accordingly. Otherwise, the db level
is pushed down.
amin(float): the minimum value of input magnitude, below which the input
magnitude is clipped(to amin).
top_db(float): the maximum db value of resulting spectrum, above which the
spectrum is clipped(to top_db).
Returns:
The spectrogram in log-scale.
shape:
input: any shape
output: same as input
"""
if amin <= 0:
raise Exception("amin must be strictly positive")
if ref_value <= 0:
raise Exception("ref_value must be strictly positive")
ones = paddle.ones_like(magnitude)
log_spec = 10.0 * paddle.log10(paddle.maximum(ones * amin, magnitude))
log_spec -= 10.0 * math.log10(max(ref_value, amin))
if top_db is not None:
if top_db < 0:
raise Exception("top_db must be non-negative")
log_spec = paddle.maximum(log_spec, ones * (log_spec.max() - top_db))
return log_spec
class Spectrogram(nn.Layer):
def __init__(self,
n_fft: int=512,
hop_length: Optional[int]=None,
win_length: Optional[int]=None,
window: str='hann',
center: bool=True,
pad_mode: str='reflect',
dtype: str=paddle.float32):
"""Compute spectrogram of a given signal, typically an audio waveform.
The spectorgram is defined as the complex norm of the short-time
Fourier transformation.
Parameters:
n_fft(int): the number of frequency components of the discrete Fourier transform.
The default value is 2048,
hop_length(int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
The default value is None.
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
The default value is None.
window(str): the name of the window function applied to the single before the Fourier transform.
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
The default value is 'hann'
center(bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
If False, frame t begins at x[t * hop_length]
The default value is True
pad_mode(str): the mode to pad the signal if necessary. The supported modes are 'reflect'
and 'constant'. The default value is 'reflect'.
dtype(str): the data type of input and window.
Notes:
The Spectrogram transform relies on STFT transform to compute the spectrogram.
By default, the weights are not learnable. To fine-tune the Fourier coefficients,
set stop_gradient=False before training.
For more information, see STFT().
"""
super(Spectrogram, self).__init__()
if win_length is None:
win_length = n_fft
fft_window = get_window(window, win_length, fftbins=True, dtype=dtype)
self._stft = partial(
paddle.signal.stft,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=fft_window,
center=center,
pad_mode=pad_mode)
def forward(self, x):
stft = self._stft(x)
spectrogram = paddle.square(paddle.abs(stft))
return spectrogram
class MelSpectrogram(nn.Layer):
def __init__(self,
sr: int=22050,
n_fft: int=512,
hop_length: Optional[int]=None,
win_length: Optional[int]=None,
window: str='hann',
center: bool=True,
pad_mode: str='reflect',
n_mels: int=64,
f_min: float=50.0,
f_max: Optional[float]=None,
htk: bool=False,
norm: Union[str, float]='slaney',
dtype: str=paddle.float32):
"""Compute the melspectrogram of a given signal, typically an audio waveform.
The melspectrogram is also known as filterbank or fbank feature in audio community.
It is computed by multiplying spectrogram with Mel filter bank matrix.
Parameters:
sr(int): the audio sample rate.
The default value is 22050.
n_fft(int): the number of frequency components of the discrete Fourier transform.
The default value is 2048,
hop_length(int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
The default value is None.
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
The default value is None.
window(str): the name of the window function applied to the single before the Fourier transform.
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
The default value is 'hann'
center(bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
If False, frame t begins at x[t * hop_length]
The default value is True
pad_mode(str): the mode to pad the signal if necessary. The supported modes are 'reflect'
and 'constant'.
The default value is 'reflect'.
n_mels(int): the mel bins.
f_min(float): the lower cut-off frequency, below which the filter response is zero.
f_max(float): the upper cut-off frequency, above which the filter response is zeros.
htk(bool): whether to use HTK formula in computing fbank matrix.
norm(str|float): the normalization type in computing fbank matrix. Slaney-style is used by default.
You can specify norm=1.0/2.0 to use customized p-norm normalization.
dtype(str): the datatype of fbank matrix used in the transform. Use float64 to increase numerical
accuracy. Note that the final transform will be conducted in float32 regardless of dtype of fbank matrix.
"""
super(MelSpectrogram, self).__init__()
self._spectrogram = Spectrogram(
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=window,
center=center,
pad_mode=pad_mode,
dtype=dtype)
self.n_mels = n_mels
self.f_min = f_min
self.f_max = f_max
self.htk = htk
self.norm = norm
if f_max is None:
f_max = sr // 2
self.fbank_matrix = compute_fbank_matrix(
sr=sr,
n_fft=n_fft,
n_mels=n_mels,
f_min=f_min,
f_max=f_max,
htk=htk,
norm=norm,
dtype=dtype) # float64 for better numerical results
self.register_buffer('fbank_matrix', self.fbank_matrix)
def forward(self, x):
spect_feature = self._spectrogram(x)
mel_feature = paddle.matmul(self.fbank_matrix, spect_feature)
return mel_feature
class LogMelSpectrogram(nn.Layer):
def __init__(self,
sr: int=22050,
n_fft: int=512,
hop_length: Optional[int]=None,
win_length: Optional[int]=None,
window: str='hann',
center: bool=True,
pad_mode: str='reflect',
n_mels: int=64,
f_min: float=50.0,
f_max: Optional[float]=None,
htk: bool=False,
norm: Union[str, float]='slaney',
ref_value: float=1.0,
amin: float=1e-10,
top_db: Optional[float]=None,
dtype: str=paddle.float32):
"""Compute log-mel-spectrogram(also known as LogFBank) feature of a given signal,
typically an audio waveform.
Parameters:
sr(int): the audio sample rate.
The default value is 22050.
n_fft(int): the number of frequency components of the discrete Fourier transform.
The default value is 2048,
hop_length(int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
The default value is None.
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
The default value is None.
window(str): the name of the window function applied to the single before the Fourier transform.
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
The default value is 'hann'
center(bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
If False, frame t begins at x[t * hop_length]
The default value is True
pad_mode(str): the mode to pad the signal if necessary. The supported modes are 'reflect'
and 'constant'.
The default value is 'reflect'.
n_mels(int): the mel bins.
f_min(float): the lower cut-off frequency, below which the filter response is zero.
f_max(float): the upper cut-off frequency, above which the filter response is zeros.
ref_value(float): the reference value. If smaller than 1.0, the db level
htk(bool): whether to use HTK formula in computing fbank matrix.
norm(str|float): the normalization type in computing fbank matrix. Slaney-style is used by default.
You can specify norm=1.0/2.0 to use customized p-norm normalization.
dtype(str): the datatype of fbank matrix used in the transform. Use float64 to increase numerical
accuracy. Note that the final transform will be conducted in float32 regardless of dtype of fbank matrix.
amin(float): the minimum value of input magnitude, below which the input of the signal will be pulled up accordingly.
Otherwise, the db level is pushed down.
magnitude is clipped(to amin). For numerical stability, set amin to a larger value,
e.g., 1e-3.
top_db(float): the maximum db value of resulting spectrum, above which the
spectrum is clipped(to top_db).
"""
super(LogMelSpectrogram, self).__init__()
self._melspectrogram = MelSpectrogram(
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=window,
center=center,
pad_mode=pad_mode,
n_mels=n_mels,
f_min=f_min,
f_max=f_max,
htk=htk,
norm=norm,
dtype=dtype)
self.ref_value = ref_value
self.amin = amin
self.top_db = top_db
def forward(self, x):
# import ipdb; ipdb.set_trace()
mel_feature = self._melspectrogram(x)
log_mel_feature = power_to_db(
mel_feature,
ref_value=self.ref_value,
amin=self.amin,
top_db=self.top_db)
return log_mel_feature

@ -0,0 +1,22 @@
# 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.
from . import compliance
from . import datasets
from . import features
from . import functional
from . import io
from . import metric
from . import sox_effects
from .backends import load
from .backends import save

@ -0,0 +1,19 @@
# 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.
from .soundfile_backend import depth_convert
from .soundfile_backend import load
from .soundfile_backend import normalize
from .soundfile_backend import resample
from .soundfile_backend import save
from .soundfile_backend import to_mono

@ -1,4 +1,4 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# 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.
@ -29,7 +29,7 @@ __all__ = [
'to_mono',
'depth_convert',
'normalize',
'save_wav',
'save',
'load',
]
NORMALMIZE_TYPES = ['linear', 'gaussian']
@ -41,12 +41,9 @@ EPS = 1e-8
def resample(y: array, src_sr: int, target_sr: int,
mode: str='kaiser_fast') -> array:
""" Audio resampling
This function is the same as using resampy.resample().
Notes:
The default mode is kaiser_fast. For better audio quality, use mode = 'kaiser_fast'
"""
if mode == 'kaiser_best':
@ -106,7 +103,6 @@ def to_mono(y: array, merge_type: str='average') -> array:
def _safe_cast(y: array, dtype: Union[type, str]) -> array:
""" data type casting in a safe way, i.e., prevent overflow or underflow
This function is used internally.
"""
return np.clip(y, np.iinfo(dtype).min, np.iinfo(dtype).max).astype(dtype)
@ -115,10 +111,8 @@ def _safe_cast(y: array, dtype: Union[type, str]) -> array:
def depth_convert(y: array, dtype: Union[type, str],
dithering: bool=True) -> array:
"""Convert audio array to target dtype safely
This function convert audio waveform to a target dtype, with addition steps of
preventing overflow/underflow and preserving audio range.
"""
SUPPORT_DTYPE = ['int16', 'int8', 'float32', 'float64']
@ -168,12 +162,9 @@ def sound_file_load(file: str,
dtype: str='int16',
duration: Optional[int]=None) -> Tuple[array, int]:
"""Load audio using soundfile library
This function load audio file using libsndfile.
Reference:
http://www.mega-nerd.com/libsndfile/#Features
"""
with sf.SoundFile(file) as sf_desc:
sr_native = sf_desc.samplerate
@ -188,33 +179,9 @@ def sound_file_load(file: str,
return y, sf_desc.samplerate
def audio_file_load():
"""Load audio using audiofile library
This function load audio file using audiofile.
Reference:
https://audiofile.68k.org/
"""
raise NotImplementedError()
def sox_file_load():
"""Load audio using sox library
This function load audio file using sox.
Reference:
http://sox.sourceforge.net/
"""
raise NotImplementedError()
def normalize(y: array, norm_type: str='linear',
mul_factor: float=1.0) -> array:
""" normalize an input audio with additional multiplier.
"""
if norm_type == 'linear':
@ -232,14 +199,12 @@ def normalize(y: array, norm_type: str='linear',
return y
def save_wav(y: array, sr: int, file: str) -> None:
def save(y: array, sr: int, file: str) -> None:
"""Save audio file to disk.
This function saves audio to disk using scipy.io.wavfile, with additional step
to convert input waveform to int16 unless it already is int16
Notes:
It only support raw wav format.
"""
if not file.endswith('.wav'):
raise ParameterError(
@ -274,11 +239,8 @@ def load(
resample_mode: str='kaiser_fast') -> Tuple[array, int]:
"""Load audio file from disk.
This function loads audio from disk using using audio beackend.
Parameters:
Notes:
"""
y, r = sound_file_load(file, offset=offset, dtype=dtype, duration=duration)

@ -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.

@ -1,6 +1,6 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# 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
#
@ -11,8 +11,3 @@
# 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 .download import *
from .env import *
from .error import *
from .log import *
from .time import *

@ -0,0 +1,638 @@
# 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.
# Modified from torchaudio(https://github.com/pytorch/audio)
import math
from typing import Tuple
import paddle
from paddle import Tensor
from ..functional import create_dct
from ..functional.window import get_window
__all__ = [
'spectrogram',
'fbank',
'mfcc',
]
# window types
HANNING = 'hann'
HAMMING = 'hamming'
POVEY = 'povey'
RECTANGULAR = 'rect'
BLACKMAN = 'blackman'
def _get_epsilon(dtype):
return paddle.to_tensor(1e-07, dtype=dtype)
def _next_power_of_2(x: int) -> int:
return 1 if x == 0 else 2**(x - 1).bit_length()
def _get_strided(waveform: Tensor,
window_size: int,
window_shift: int,
snip_edges: bool) -> Tensor:
assert waveform.dim() == 1
num_samples = waveform.shape[0]
if snip_edges:
if num_samples < window_size:
return paddle.empty((0, 0), dtype=waveform.dtype)
else:
m = 1 + (num_samples - window_size) // window_shift
else:
reversed_waveform = paddle.flip(waveform, [0])
m = (num_samples + (window_shift // 2)) // window_shift
pad = window_size // 2 - window_shift // 2
pad_right = reversed_waveform
if pad > 0:
pad_left = reversed_waveform[-pad:]
waveform = paddle.concat((pad_left, waveform, pad_right), axis=0)
else:
waveform = paddle.concat((waveform[-pad:], pad_right), axis=0)
return paddle.signal.frame(waveform, window_size, window_shift)[:, :m].T
def _feature_window_function(
window_type: str,
window_size: int,
blackman_coeff: float,
dtype: int, ) -> Tensor:
if window_type == HANNING:
return get_window('hann', window_size, fftbins=False, dtype=dtype)
elif window_type == HAMMING:
return get_window('hamming', window_size, fftbins=False, dtype=dtype)
elif window_type == POVEY:
return get_window(
'hann', window_size, fftbins=False, dtype=dtype).pow(0.85)
elif window_type == RECTANGULAR:
return paddle.ones([window_size], dtype=dtype)
elif window_type == BLACKMAN:
a = 2 * math.pi / (window_size - 1)
window_function = paddle.arange(window_size, dtype=dtype)
return (blackman_coeff - 0.5 * paddle.cos(a * window_function) +
(0.5 - blackman_coeff) * paddle.cos(2 * a * window_function)
).astype(dtype)
else:
raise Exception('Invalid window type ' + window_type)
def _get_log_energy(strided_input: Tensor, epsilon: Tensor,
energy_floor: float) -> Tensor:
log_energy = paddle.maximum(strided_input.pow(2).sum(1), epsilon).log()
if energy_floor == 0.0:
return log_energy
return paddle.maximum(
log_energy,
paddle.to_tensor(math.log(energy_floor), dtype=strided_input.dtype))
def _get_waveform_and_window_properties(
waveform: Tensor,
channel: int,
sr: int,
frame_shift: float,
frame_length: float,
round_to_power_of_two: bool,
preemphasis_coefficient: float) -> Tuple[Tensor, int, int, int]:
channel = max(channel, 0)
assert channel < waveform.shape[0], (
'Invalid channel {} for size {}'.format(channel, waveform.shape[0]))
waveform = waveform[channel, :] # size (n)
window_shift = int(
sr * frame_shift *
0.001) # pass frame_shift and frame_length in milliseconds
window_size = int(sr * frame_length * 0.001)
padded_window_size = _next_power_of_2(
window_size) if round_to_power_of_two else window_size
assert 2 <= window_size <= len(waveform), (
'choose a window size {} that is [2, {}]'.format(window_size,
len(waveform)))
assert 0 < window_shift, '`window_shift` must be greater than 0'
assert padded_window_size % 2 == 0, 'the padded `window_size` must be divisible by two.' \
' use `round_to_power_of_two` or change `frame_length`'
assert 0. <= preemphasis_coefficient <= 1.0, '`preemphasis_coefficient` must be between [0,1]'
assert sr > 0, '`sr` must be greater than zero'
return waveform, window_shift, window_size, padded_window_size
def _get_window(waveform: Tensor,
padded_window_size: int,
window_size: int,
window_shift: int,
window_type: str,
blackman_coeff: float,
snip_edges: bool,
raw_energy: bool,
energy_floor: float,
dither: float,
remove_dc_offset: bool,
preemphasis_coefficient: float) -> Tuple[Tensor, Tensor]:
dtype = waveform.dtype
epsilon = _get_epsilon(dtype)
# (m, window_size)
strided_input = _get_strided(waveform, window_size, window_shift,
snip_edges)
if dither != 0.0:
x = paddle.maximum(epsilon,
paddle.rand(strided_input.shape, dtype=dtype))
rand_gauss = paddle.sqrt(-2 * x.log()) * paddle.cos(2 * math.pi * x)
strided_input = strided_input + rand_gauss * dither
if remove_dc_offset:
row_means = paddle.mean(strided_input, axis=1).unsqueeze(1) # (m, 1)
strided_input = strided_input - row_means
if raw_energy:
signal_log_energy = _get_log_energy(strided_input, epsilon,
energy_floor) # (m)
if preemphasis_coefficient != 0.0:
offset_strided_input = paddle.nn.functional.pad(
strided_input.unsqueeze(0), (1, 0),
data_format='NCL',
mode='replicate').squeeze(0) # (m, window_size + 1)
strided_input = strided_input - preemphasis_coefficient * offset_strided_input[:, :
-1]
window_function = _feature_window_function(
window_type, window_size, blackman_coeff,
dtype).unsqueeze(0) # (1, window_size)
strided_input = strided_input * window_function # (m, window_size)
# (m, padded_window_size)
if padded_window_size != window_size:
padding_right = padded_window_size - window_size
strided_input = paddle.nn.functional.pad(
strided_input.unsqueeze(0), (0, padding_right),
data_format='NCL',
mode='constant',
value=0).squeeze(0)
if not raw_energy:
signal_log_energy = _get_log_energy(strided_input, epsilon,
energy_floor) # size (m)
return strided_input, signal_log_energy
def _subtract_column_mean(tensor: Tensor, subtract_mean: bool) -> Tensor:
if subtract_mean:
col_means = paddle.mean(tensor, axis=0).unsqueeze(0)
tensor = tensor - col_means
return tensor
def spectrogram(waveform: Tensor,
blackman_coeff: float=0.42,
channel: int=-1,
dither: float=0.0,
energy_floor: float=1.0,
frame_length: float=25.0,
frame_shift: float=10.0,
preemphasis_coefficient: float=0.97,
raw_energy: bool=True,
remove_dc_offset: bool=True,
round_to_power_of_two: bool=True,
sr: int=16000,
snip_edges: bool=True,
subtract_mean: bool=False,
window_type: str=POVEY) -> Tensor:
"""Compute and return a spectrogram from a waveform. The output is identical to Kaldi's.
Args:
waveform (Tensor): A waveform tensor with shape [C, T].
blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
channel (int, optional): Select the channel of waveform. Defaults to -1.
dither (float, optional): Dithering constant . Defaults to 0.0.
energy_floor (float, optional): Floor on energy of the output Spectrogram. Defaults to 1.0.
frame_length (float, optional): Frame length in milliseconds. Defaults to 25.0.
frame_shift (float, optional): Shift between adjacent frames in milliseconds. Defaults to 10.0.
preemphasis_coefficient (float, optional): Preemphasis coefficient for input waveform. Defaults to 0.97.
raw_energy (bool, optional): Whether to compute before preemphasis and windowing. Defaults to True.
remove_dc_offset (bool, optional): Whether to subtract mean from waveform on frames. Defaults to True.
round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
to FFT. Defaults to True.
sr (int, optional): Sample rate of input waveform. Defaults to 16000.
snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a singal frame when it
is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
Returns:
Tensor: A spectrogram tensor with shape (m, padded_window_size // 2 + 1) where m is the number of frames
depends on frame_length and frame_shift.
"""
dtype = waveform.dtype
epsilon = _get_epsilon(dtype)
waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
waveform, channel, sr, frame_shift, frame_length, round_to_power_of_two,
preemphasis_coefficient)
strided_input, signal_log_energy = _get_window(
waveform, padded_window_size, window_size, window_shift, window_type,
blackman_coeff, snip_edges, raw_energy, energy_floor, dither,
remove_dc_offset, preemphasis_coefficient)
# (m, padded_window_size // 2 + 1, 2)
fft = paddle.fft.rfft(strided_input)
power_spectrum = paddle.maximum(
fft.abs().pow(2.), epsilon).log() # (m, padded_window_size // 2 + 1)
power_spectrum[:, 0] = signal_log_energy
power_spectrum = _subtract_column_mean(power_spectrum, subtract_mean)
return power_spectrum
def _inverse_mel_scale_scalar(mel_freq: float) -> float:
return 700.0 * (math.exp(mel_freq / 1127.0) - 1.0)
def _inverse_mel_scale(mel_freq: Tensor) -> Tensor:
return 700.0 * ((mel_freq / 1127.0).exp() - 1.0)
def _mel_scale_scalar(freq: float) -> float:
return 1127.0 * math.log(1.0 + freq / 700.0)
def _mel_scale(freq: Tensor) -> Tensor:
return 1127.0 * (1.0 + freq / 700.0).log()
def _vtln_warp_freq(vtln_low_cutoff: float,
vtln_high_cutoff: float,
low_freq: float,
high_freq: float,
vtln_warp_factor: float,
freq: Tensor) -> Tensor:
assert vtln_low_cutoff > low_freq, 'be sure to set the vtln_low option higher than low_freq'
assert vtln_high_cutoff < high_freq, 'be sure to set the vtln_high option lower than high_freq [or negative]'
l = vtln_low_cutoff * max(1.0, vtln_warp_factor)
h = vtln_high_cutoff * min(1.0, vtln_warp_factor)
scale = 1.0 / vtln_warp_factor
Fl = scale * l
Fh = scale * h
assert l > low_freq and h < high_freq
scale_left = (Fl - low_freq) / (l - low_freq)
scale_right = (high_freq - Fh) / (high_freq - h)
res = paddle.empty_like(freq)
outside_low_high_freq = paddle.less_than(freq, paddle.to_tensor(low_freq)) \
| paddle.greater_than(freq, paddle.to_tensor(high_freq))
before_l = paddle.less_than(freq, paddle.to_tensor(l))
before_h = paddle.less_than(freq, paddle.to_tensor(h))
after_h = paddle.greater_equal(freq, paddle.to_tensor(h))
res[after_h] = high_freq + scale_right * (freq[after_h] - high_freq)
res[before_h] = scale * freq[before_h]
res[before_l] = low_freq + scale_left * (freq[before_l] - low_freq)
res[outside_low_high_freq] = freq[outside_low_high_freq]
return res
def _vtln_warp_mel_freq(vtln_low_cutoff: float,
vtln_high_cutoff: float,
low_freq,
high_freq: float,
vtln_warp_factor: float,
mel_freq: Tensor) -> Tensor:
return _mel_scale(
_vtln_warp_freq(vtln_low_cutoff, vtln_high_cutoff, low_freq, high_freq,
vtln_warp_factor, _inverse_mel_scale(mel_freq)))
def _get_mel_banks(num_bins: int,
window_length_padded: int,
sample_freq: float,
low_freq: float,
high_freq: float,
vtln_low: float,
vtln_high: float,
vtln_warp_factor: float) -> Tuple[Tensor, Tensor]:
assert num_bins > 3, 'Must have at least 3 mel bins'
assert window_length_padded % 2 == 0
num_fft_bins = window_length_padded / 2
nyquist = 0.5 * sample_freq
if high_freq <= 0.0:
high_freq += nyquist
assert (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq), \
('Bad values in options: low-freq {} and high-freq {} vs. nyquist {}'.format(low_freq, high_freq, nyquist))
fft_bin_width = sample_freq / window_length_padded
mel_low_freq = _mel_scale_scalar(low_freq)
mel_high_freq = _mel_scale_scalar(high_freq)
mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1)
if vtln_high < 0.0:
vtln_high += nyquist
assert vtln_warp_factor == 1.0 or ((low_freq < vtln_low < high_freq) and
(0.0 < vtln_high < high_freq) and (vtln_low < vtln_high)), \
('Bad values in options: vtln-low {} and vtln-high {}, versus '
'low-freq {} and high-freq {}'.format(vtln_low, vtln_high, low_freq, high_freq))
bin = paddle.arange(num_bins).unsqueeze(1)
left_mel = mel_low_freq + bin * mel_freq_delta # (num_bins, 1)
center_mel = mel_low_freq + (bin + 1.0) * mel_freq_delta # (num_bins, 1)
right_mel = mel_low_freq + (bin + 2.0) * mel_freq_delta # (num_bins, 1)
if vtln_warp_factor != 1.0:
left_mel = _vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq,
vtln_warp_factor, left_mel)
center_mel = _vtln_warp_mel_freq(vtln_low, vtln_high, low_freq,
high_freq, vtln_warp_factor,
center_mel)
right_mel = _vtln_warp_mel_freq(vtln_low, vtln_high, low_freq,
high_freq, vtln_warp_factor, right_mel)
center_freqs = _inverse_mel_scale(center_mel) # (num_bins)
# (1, num_fft_bins)
mel = _mel_scale(fft_bin_width * paddle.arange(num_fft_bins)).unsqueeze(0)
# (num_bins, num_fft_bins)
up_slope = (mel - left_mel) / (center_mel - left_mel)
down_slope = (right_mel - mel) / (right_mel - center_mel)
if vtln_warp_factor == 1.0:
bins = paddle.maximum(
paddle.zeros([1]), paddle.minimum(up_slope, down_slope))
else:
bins = paddle.zeros_like(up_slope)
up_idx = paddle.greater_than(mel, left_mel) & paddle.less_than(
mel, center_mel)
down_idx = paddle.greater_than(mel, center_mel) & paddle.less_than(
mel, right_mel)
bins[up_idx] = up_slope[up_idx]
bins[down_idx] = down_slope[down_idx]
return bins, center_freqs
def fbank(waveform: Tensor,
blackman_coeff: float=0.42,
channel: int=-1,
dither: float=0.0,
energy_floor: float=1.0,
frame_length: float=25.0,
frame_shift: float=10.0,
high_freq: float=0.0,
htk_compat: bool=False,
low_freq: float=20.0,
n_mels: int=23,
preemphasis_coefficient: float=0.97,
raw_energy: bool=True,
remove_dc_offset: bool=True,
round_to_power_of_two: bool=True,
sr: int=16000,
snip_edges: bool=True,
subtract_mean: bool=False,
use_energy: bool=False,
use_log_fbank: bool=True,
use_power: bool=True,
vtln_high: float=-500.0,
vtln_low: float=100.0,
vtln_warp: float=1.0,
window_type: str=POVEY) -> Tensor:
"""Compute and return filter banks from a waveform. The output is identical to Kaldi's.
Args:
waveform (Tensor): A waveform tensor with shape [C, T].
blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
channel (int, optional): Select the channel of waveform. Defaults to -1.
dither (float, optional): Dithering constant . Defaults to 0.0.
energy_floor (float, optional): Floor on energy of the output Spectrogram. Defaults to 1.0.
frame_length (float, optional): Frame length in milliseconds. Defaults to 25.0.
frame_shift (float, optional): Shift between adjacent frames in milliseconds. Defaults to 10.0.
high_freq (float, optional): The upper cut-off frequency. Defaults to 0.0.
htk_compat (bool, optional): Put energy to the last when it is set True. Defaults to False.
low_freq (float, optional): The lower cut-off frequency. Defaults to 20.0.
n_mels (int, optional): Number of output mel bins. Defaults to 23.
preemphasis_coefficient (float, optional): Preemphasis coefficient for input waveform. Defaults to 0.97.
raw_energy (bool, optional): Whether to compute before preemphasis and windowing. Defaults to True.
remove_dc_offset (bool, optional): Whether to subtract mean from waveform on frames. Defaults to True.
round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
to FFT. Defaults to True.
sr (int, optional): Sample rate of input waveform. Defaults to 16000.
snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a singal frame when it
is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
use_energy (bool, optional): Add an dimension with energy of spectrogram to the output. Defaults to False.
use_log_fbank (bool, optional): Return log fbank when it is set True. Defaults to True.
use_power (bool, optional): Whether to use power instead of magnitude. Defaults to True.
vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function. Defaults to -500.0.
vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function. Defaults to 100.0.
vtln_warp (float, optional): Vtln warp factor. Defaults to 1.0.
window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
Returns:
Tensor: A filter banks tensor with shape (m, n_mels).
"""
dtype = waveform.dtype
waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
waveform, channel, sr, frame_shift, frame_length, round_to_power_of_two,
preemphasis_coefficient)
strided_input, signal_log_energy = _get_window(
waveform, padded_window_size, window_size, window_shift, window_type,
blackman_coeff, snip_edges, raw_energy, energy_floor, dither,
remove_dc_offset, preemphasis_coefficient)
# (m, padded_window_size // 2 + 1)
spectrum = paddle.fft.rfft(strided_input).abs()
if use_power:
spectrum = spectrum.pow(2.)
# (n_mels, padded_window_size // 2)
mel_energies, _ = _get_mel_banks(n_mels, padded_window_size, sr, low_freq,
high_freq, vtln_low, vtln_high, vtln_warp)
mel_energies = mel_energies.astype(dtype)
# (n_mels, padded_window_size // 2 + 1)
mel_energies = paddle.nn.functional.pad(
mel_energies.unsqueeze(0), (0, 1),
data_format='NCL',
mode='constant',
value=0).squeeze(0)
# (m, n_mels)
mel_energies = paddle.mm(spectrum, mel_energies.T)
if use_log_fbank:
mel_energies = paddle.maximum(mel_energies, _get_epsilon(dtype)).log()
if use_energy:
signal_log_energy = signal_log_energy.unsqueeze(1)
if htk_compat:
mel_energies = paddle.concat(
(mel_energies, signal_log_energy), axis=1)
else:
mel_energies = paddle.concat(
(signal_log_energy, mel_energies), axis=1)
# (m, n_mels + 1)
mel_energies = _subtract_column_mean(mel_energies, subtract_mean)
return mel_energies
def _get_dct_matrix(n_mfcc: int, n_mels: int) -> Tensor:
dct_matrix = create_dct(n_mels, n_mels, 'ortho')
dct_matrix[:, 0] = math.sqrt(1 / float(n_mels))
dct_matrix = dct_matrix[:, :n_mfcc] # (n_mels, n_mfcc)
return dct_matrix
def _get_lifter_coeffs(n_mfcc: int, cepstral_lifter: float) -> Tensor:
i = paddle.arange(n_mfcc)
return 1.0 + 0.5 * cepstral_lifter * paddle.sin(math.pi * i /
cepstral_lifter)
def mfcc(waveform: Tensor,
blackman_coeff: float=0.42,
cepstral_lifter: float=22.0,
channel: int=-1,
dither: float=0.0,
energy_floor: float=1.0,
frame_length: float=25.0,
frame_shift: float=10.0,
high_freq: float=0.0,
htk_compat: bool=False,
low_freq: float=20.0,
n_mfcc: int=13,
n_mels: int=23,
preemphasis_coefficient: float=0.97,
raw_energy: bool=True,
remove_dc_offset: bool=True,
round_to_power_of_two: bool=True,
sr: int=16000,
snip_edges: bool=True,
subtract_mean: bool=False,
use_energy: bool=False,
vtln_high: float=-500.0,
vtln_low: float=100.0,
vtln_warp: float=1.0,
window_type: str=POVEY) -> Tensor:
"""Compute and return mel frequency cepstral coefficients from a waveform. The output is
identical to Kaldi's.
Args:
waveform (Tensor): A waveform tensor with shape [C, T].
blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
cepstral_lifter (float, optional): Scaling of output mfccs. Defaults to 22.0.
channel (int, optional): Select the channel of waveform. Defaults to -1.
dither (float, optional): Dithering constant . Defaults to 0.0.
energy_floor (float, optional): Floor on energy of the output Spectrogram. Defaults to 1.0.
frame_length (float, optional): Frame length in milliseconds. Defaults to 25.0.
frame_shift (float, optional): Shift between adjacent frames in milliseconds. Defaults to 10.0.
high_freq (float, optional): The upper cut-off frequency. Defaults to 0.0.
htk_compat (bool, optional): Put energy to the last when it is set True. Defaults to False.
low_freq (float, optional): The lower cut-off frequency. Defaults to 20.0.
n_mfcc (int, optional): Number of cepstra in MFCC. Defaults to 13.
n_mels (int, optional): Number of output mel bins. Defaults to 23.
preemphasis_coefficient (float, optional): Preemphasis coefficient for input waveform. Defaults to 0.97.
raw_energy (bool, optional): Whether to compute before preemphasis and windowing. Defaults to True.
remove_dc_offset (bool, optional): Whether to subtract mean from waveform on frames. Defaults to True.
round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
to FFT. Defaults to True.
sr (int, optional): Sample rate of input waveform. Defaults to 16000.
snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a singal frame when it
is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
use_energy (bool, optional): Add an dimension with energy of spectrogram to the output. Defaults to False.
vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function. Defaults to -500.0.
vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function. Defaults to 100.0.
vtln_warp (float, optional): Vtln warp factor. Defaults to 1.0.
window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
Returns:
Tensor: A mel frequency cepstral coefficients tensor with shape (m, n_mfcc).
"""
assert n_mfcc <= n_mels, 'n_mfcc cannot be larger than n_mels: %d vs %d' % (
n_mfcc, n_mels)
dtype = waveform.dtype
# (m, n_mels + use_energy)
feature = fbank(
waveform=waveform,
blackman_coeff=blackman_coeff,
channel=channel,
dither=dither,
energy_floor=energy_floor,
frame_length=frame_length,
frame_shift=frame_shift,
high_freq=high_freq,
htk_compat=htk_compat,
low_freq=low_freq,
n_mels=n_mels,
preemphasis_coefficient=preemphasis_coefficient,
raw_energy=raw_energy,
remove_dc_offset=remove_dc_offset,
round_to_power_of_two=round_to_power_of_two,
sr=sr,
snip_edges=snip_edges,
subtract_mean=False,
use_energy=use_energy,
use_log_fbank=True,
use_power=True,
vtln_high=vtln_high,
vtln_low=vtln_low,
vtln_warp=vtln_warp,
window_type=window_type)
if use_energy:
# (m)
signal_log_energy = feature[:, n_mels if htk_compat else 0]
mel_offset = int(not htk_compat)
feature = feature[:, mel_offset:(n_mels + mel_offset)]
# (n_mels, n_mfcc)
dct_matrix = _get_dct_matrix(n_mfcc, n_mels).astype(dtype=dtype)
# (m, n_mfcc)
feature = feature.matmul(dct_matrix)
if cepstral_lifter != 0.0:
# (1, n_mfcc)
lifter_coeffs = _get_lifter_coeffs(n_mfcc, cepstral_lifter).unsqueeze(0)
feature *= lifter_coeffs.astype(dtype=dtype)
if use_energy:
feature[:, 0] = signal_log_energy
if htk_compat:
energy = feature[:, 0].unsqueeze(1) # (m, 1)
feature = feature[:, 1:] # (m, n_mfcc - 1)
if not use_energy:
energy *= math.sqrt(2)
feature = paddle.concat((feature, energy), axis=1)
feature = _subtract_column_mean(feature, subtract_mean)
return feature

@ -21,11 +21,13 @@ import numpy as np
import scipy
from numpy import ndarray as array
from numpy.lib.stride_tricks import as_strided
from scipy.signal import get_window
from scipy import signal
from ..backends import depth_convert
from ..utils import ParameterError
__all__ = [
# dsp
'stft',
'mfcc',
'hz_to_mel',
@ -38,6 +40,12 @@ __all__ = [
'spectrogram',
'mu_encode',
'mu_decode',
# augmentation
'depth_augment',
'spect_augment',
'random_crop1d',
'random_crop2d',
'adaptive_spect_augment',
]
@ -303,7 +311,7 @@ def stft(x: array,
if hop_length is None:
hop_length = int(win_length // 4)
fft_window = get_window(window, win_length, fftbins=True)
fft_window = signal.get_window(window, win_length, fftbins=True)
# Pad the window out to n_fft size
fft_window = pad_center(fft_window, n_fft)
@ -576,3 +584,145 @@ def mu_decode(y: array, mu: int=255, quantized: bool=True) -> array:
y = y * 2 / mu - 1
x = np.sign(y) / mu * ((1 + mu)**np.abs(y) - 1)
return x
def randint(high: int) -> int:
"""Generate one random integer in range [0 high)
This is a helper function for random data augmentaiton
"""
return int(np.random.randint(0, high=high))
def rand() -> float:
"""Generate one floating-point number in range [0 1)
This is a helper function for random data augmentaiton
"""
return float(np.random.rand(1))
def depth_augment(y: array,
choices: List=['int8', 'int16'],
probs: List[float]=[0.5, 0.5]) -> array:
""" Audio depth augmentation
Do audio depth augmentation to simulate the distortion brought by quantization.
"""
assert len(probs) == len(
choices
), 'number of choices {} must be equal to size of probs {}'.format(
len(choices), len(probs))
depth = np.random.choice(choices, p=probs)
src_depth = y.dtype
y1 = depth_convert(y, depth)
y2 = depth_convert(y1, src_depth)
return y2
def adaptive_spect_augment(spect: array, tempo_axis: int=0,
level: float=0.1) -> array:
"""Do adpative spectrogram augmentation
The level of the augmentation is gowern by the paramter level,
ranging from 0 to 1, with 0 represents no augmentation
"""
assert spect.ndim == 2., 'only supports 2d tensor or numpy array'
if tempo_axis == 0:
nt, nf = spect.shape
else:
nf, nt = spect.shape
time_mask_width = int(nt * level * 0.5)
freq_mask_width = int(nf * level * 0.5)
num_time_mask = int(10 * level)
num_freq_mask = int(10 * level)
if tempo_axis == 0:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[start:start + time_mask_width, :] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[:, start:start + freq_mask_width] = 0
else:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[:, start:start + time_mask_width] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[start:start + freq_mask_width, :] = 0
return spect
def spect_augment(spect: array,
tempo_axis: int=0,
max_time_mask: int=3,
max_freq_mask: int=3,
max_time_mask_width: int=30,
max_freq_mask_width: int=20) -> array:
"""Do spectrogram augmentation in both time and freq axis
Reference:
"""
assert spect.ndim == 2., 'only supports 2d tensor or numpy array'
if tempo_axis == 0:
nt, nf = spect.shape
else:
nf, nt = spect.shape
num_time_mask = randint(max_time_mask)
num_freq_mask = randint(max_freq_mask)
time_mask_width = randint(max_time_mask_width)
freq_mask_width = randint(max_freq_mask_width)
if tempo_axis == 0:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[start:start + time_mask_width, :] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[:, start:start + freq_mask_width] = 0
else:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[:, start:start + time_mask_width] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[start:start + freq_mask_width, :] = 0
return spect
def random_crop1d(y: array, crop_len: int) -> array:
""" Do random cropping on 1d input signal
The input is a 1d signal, typically a sound waveform
"""
if y.ndim != 1:
'only accept 1d tensor or numpy array'
n = len(y)
idx = randint(n - crop_len)
return y[idx:idx + crop_len]
def random_crop2d(s: array, crop_len: int, tempo_axis: int=0) -> array:
""" Do random cropping for 2D array, typically a spectrogram.
The cropping is done in temporal direction on the time-freq input signal.
"""
if tempo_axis >= s.ndim:
raise ParameterError('axis out of range')
n = s.shape[tempo_axis]
idx = randint(high=n - crop_len)
sli = [slice(None) for i in range(s.ndim)]
sli[tempo_axis] = slice(idx, idx + crop_len)
out = s[tuple(sli)]
return out

@ -15,10 +15,3 @@ from .esc50 import ESC50
from .gtzan import GTZAN
from .tess import TESS
from .urban_sound import UrbanSound8K
__all__ = [
'ESC50',
'UrbanSound8K',
'GTZAN',
'TESS',
]

@ -17,8 +17,8 @@ import numpy as np
import paddle
from ..backends import load as load_audio
from ..features import melspectrogram
from ..features import mfcc
from ..compliance.librosa import melspectrogram
from ..compliance.librosa import mfcc
feat_funcs = {
'raw': None,

@ -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.
from .augment import *
from .core import *
from .spectrum import *
from .layers import LogMelSpectrogram
from .layers import MelSpectrogram
from .layers import MFCC
from .layers import Spectrogram

@ -0,0 +1,350 @@
# 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.
from functools import partial
from typing import Optional
from typing import Union
import paddle
import paddle.nn as nn
from ..functional import compute_fbank_matrix
from ..functional import create_dct
from ..functional import power_to_db
from ..functional.window import get_window
__all__ = [
'Spectrogram',
'MelSpectrogram',
'LogMelSpectrogram',
'MFCC',
]
class Spectrogram(nn.Layer):
def __init__(self,
n_fft: int=512,
hop_length: Optional[int]=None,
win_length: Optional[int]=None,
window: str='hann',
power: float=2.0,
center: bool=True,
pad_mode: str='reflect',
dtype: str=paddle.float32):
"""Compute spectrogram of a given signal, typically an audio waveform.
The spectorgram is defined as the complex norm of the short-time
Fourier transformation.
Parameters:
n_fft (int): the number of frequency components of the discrete Fourier transform.
The default value is 2048,
hop_length (int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
The default value is None.
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
The default value is None.
window (str): the name of the window function applied to the single before the Fourier transform.
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
The default value is 'hann'
power (float): Exponent for the magnitude spectrogram. The default value is 2.0.
center (bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
If False, frame t begins at x[t * hop_length]
The default value is True
pad_mode (str): the mode to pad the signal if necessary. The supported modes are 'reflect'
and 'constant'. The default value is 'reflect'.
dtype (str): the data type of input and window.
Notes:
The Spectrogram transform relies on STFT transform to compute the spectrogram.
By default, the weights are not learnable. To fine-tune the Fourier coefficients,
set stop_gradient=False before training.
For more information, see STFT().
"""
super(Spectrogram, self).__init__()
assert power > 0, 'Power of spectrogram must be > 0.'
self.power = power
if win_length is None:
win_length = n_fft
self.fft_window = get_window(
window, win_length, fftbins=True, dtype=dtype)
self._stft = partial(
paddle.signal.stft,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=self.fft_window,
center=center,
pad_mode=pad_mode)
self.register_buffer('fft_window', self.fft_window)
def forward(self, x):
stft = self._stft(x)
spectrogram = paddle.pow(paddle.abs(stft), self.power)
return spectrogram
class MelSpectrogram(nn.Layer):
def __init__(self,
sr: int=22050,
n_fft: int=512,
hop_length: Optional[int]=None,
win_length: Optional[int]=None,
window: str='hann',
power: float=2.0,
center: bool=True,
pad_mode: str='reflect',
n_mels: int=64,
f_min: float=50.0,
f_max: Optional[float]=None,
htk: bool=False,
norm: Union[str, float]='slaney',
dtype: str=paddle.float32):
"""Compute the melspectrogram of a given signal, typically an audio waveform.
The melspectrogram is also known as filterbank or fbank feature in audio community.
It is computed by multiplying spectrogram with Mel filter bank matrix.
Parameters:
sr(int): the audio sample rate.
The default value is 22050.
n_fft(int): the number of frequency components of the discrete Fourier transform.
The default value is 2048,
hop_length(int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
The default value is None.
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
The default value is None.
window(str): the name of the window function applied to the single before the Fourier transform.
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
The default value is 'hann'
power (float): Exponent for the magnitude spectrogram. The default value is 2.0.
center(bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
If False, frame t begins at x[t * hop_length]
The default value is True
pad_mode(str): the mode to pad the signal if necessary. The supported modes are 'reflect'
and 'constant'.
The default value is 'reflect'.
n_mels(int): the mel bins.
f_min(float): the lower cut-off frequency, below which the filter response is zero.
f_max(float): the upper cut-off frequency, above which the filter response is zeros.
htk(bool): whether to use HTK formula in computing fbank matrix.
norm(str|float): the normalization type in computing fbank matrix. Slaney-style is used by default.
You can specify norm=1.0/2.0 to use customized p-norm normalization.
dtype(str): the datatype of fbank matrix used in the transform. Use float64 to increase numerical
accuracy. Note that the final transform will be conducted in float32 regardless of dtype of fbank matrix.
"""
super(MelSpectrogram, self).__init__()
self._spectrogram = Spectrogram(
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=window,
power=power,
center=center,
pad_mode=pad_mode,
dtype=dtype)
self.n_mels = n_mels
self.f_min = f_min
self.f_max = f_max
self.htk = htk
self.norm = norm
if f_max is None:
f_max = sr // 2
self.fbank_matrix = compute_fbank_matrix(
sr=sr,
n_fft=n_fft,
n_mels=n_mels,
f_min=f_min,
f_max=f_max,
htk=htk,
norm=norm,
dtype=dtype) # float64 for better numerical results
self.register_buffer('fbank_matrix', self.fbank_matrix)
def forward(self, x):
spect_feature = self._spectrogram(x)
mel_feature = paddle.matmul(self.fbank_matrix, spect_feature)
return mel_feature
class LogMelSpectrogram(nn.Layer):
def __init__(self,
sr: int=22050,
n_fft: int=512,
hop_length: Optional[int]=None,
win_length: Optional[int]=None,
window: str='hann',
power: float=2.0,
center: bool=True,
pad_mode: str='reflect',
n_mels: int=64,
f_min: float=50.0,
f_max: Optional[float]=None,
htk: bool=False,
norm: Union[str, float]='slaney',
ref_value: float=1.0,
amin: float=1e-10,
top_db: Optional[float]=None,
dtype: str=paddle.float32):
"""Compute log-mel-spectrogram(also known as LogFBank) feature of a given signal,
typically an audio waveform.
Parameters:
sr (int): the audio sample rate.
The default value is 22050.
n_fft (int): the number of frequency components of the discrete Fourier transform.
The default value is 2048,
hop_length (int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
The default value is None.
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
The default value is None.
window (str): the name of the window function applied to the single before the Fourier transform.
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
The default value is 'hann'
center (bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
If False, frame t begins at x[t * hop_length]
The default value is True
pad_mode (str): the mode to pad the signal if necessary. The supported modes are 'reflect'
and 'constant'.
The default value is 'reflect'.
n_mels (int): the mel bins.
f_min (float): the lower cut-off frequency, below which the filter response is zero.
f_max (float): the upper cut-off frequency, above which the filter response is zeros.
htk (bool): whether to use HTK formula in computing fbank matrix.
norm (str|float): the normalization type in computing fbank matrix. Slaney-style is used by default.
You can specify norm=1.0/2.0 to use customized p-norm normalization.
ref_value (float): the reference value. If smaller than 1.0, the db level of the signal will be pulled up accordingly. Otherwise, the db level is pushed down.
amin (float): the minimum value of input magnitude, below which the input magnitude is clipped(to amin).
top_db (float): the maximum db value of resulting spectrum, above which the
spectrum is clipped(to top_db).
dtype (str): the datatype of fbank matrix used in the transform. Use float64 to increase numerical
accuracy. Note that the final transform will be conducted in float32 regardless of dtype of fbank matrix.
"""
super(LogMelSpectrogram, self).__init__()
self._melspectrogram = MelSpectrogram(
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=window,
power=power,
center=center,
pad_mode=pad_mode,
n_mels=n_mels,
f_min=f_min,
f_max=f_max,
htk=htk,
norm=norm,
dtype=dtype)
self.ref_value = ref_value
self.amin = amin
self.top_db = top_db
def forward(self, x):
mel_feature = self._melspectrogram(x)
log_mel_feature = power_to_db(
mel_feature,
ref_value=self.ref_value,
amin=self.amin,
top_db=self.top_db)
return log_mel_feature
class MFCC(nn.Layer):
def __init__(self,
sr: int=22050,
n_mfcc: int=40,
n_fft: int=512,
hop_length: Optional[int]=None,
win_length: Optional[int]=None,
window: str='hann',
power: float=2.0,
center: bool=True,
pad_mode: str='reflect',
n_mels: int=64,
f_min: float=50.0,
f_max: Optional[float]=None,
htk: bool=False,
norm: Union[str, float]='slaney',
ref_value: float=1.0,
amin: float=1e-10,
top_db: Optional[float]=None,
dtype: str=paddle.float32):
"""Compute mel frequency cepstral coefficients(MFCCs) feature of given waveforms.
Parameters:
sr(int): the audio sample rate.
The default value is 22050.
n_mfcc (int, optional): Number of cepstra in MFCC. Defaults to 40.
n_fft (int): the number of frequency components of the discrete Fourier transform.
The default value is 2048,
hop_length (int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
The default value is None.
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
The default value is None.
window (str): the name of the window function applied to the single before the Fourier transform.
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
The default value is 'hann'
power (float): Exponent for the magnitude spectrogram. The default value is 2.0.
center (bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
If False, frame t begins at x[t * hop_length]
The default value is True
pad_mode (str): the mode to pad the signal if necessary. The supported modes are 'reflect'
and 'constant'.
The default value is 'reflect'.
n_mels (int): the mel bins.
f_min (float): the lower cut-off frequency, below which the filter response is zero.
f_max (float): the upper cut-off frequency, above which the filter response is zeros.
htk (bool): whether to use HTK formula in computing fbank matrix.
norm (str|float): the normalization type in computing fbank matrix. Slaney-style is used by default.
You can specify norm=1.0/2.0 to use customized p-norm normalization.
ref_value (float): the reference value. If smaller than 1.0, the db level of the signal will be pulled up accordingly. Otherwise, the db level is pushed down.
amin (float): the minimum value of input magnitude, below which the input magnitude is clipped(to amin).
top_db (float): the maximum db value of resulting spectrum, above which the
spectrum is clipped(to top_db).
dtype (str): the datatype of fbank matrix used in the transform. Use float64 to increase numerical
accuracy. Note that the final transform will be conducted in float32 regardless of dtype of fbank matrix.
"""
super(MFCC, self).__init__()
assert n_mfcc <= n_mels, 'n_mfcc cannot be larger than n_mels: %d vs %d' % (
n_mfcc, n_mels)
self._log_melspectrogram = LogMelSpectrogram(
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=window,
power=power,
center=center,
pad_mode=pad_mode,
n_mels=n_mels,
f_min=f_min,
f_max=f_max,
htk=htk,
norm=norm,
ref_value=ref_value,
amin=amin,
top_db=top_db,
dtype=dtype)
self.dct_matrix = create_dct(n_mfcc=n_mfcc, n_mels=n_mels, dtype=dtype)
self.register_buffer('dct_matrix', self.dct_matrix)
def forward(self, x):
log_mel_feature = self._log_melspectrogram(x)
mfcc = paddle.matmul(
log_mel_feature.transpose((0, 2, 1)), self.dct_matrix).transpose(
(0, 2, 1)) # (B, n_mels, L)
return mfcc

@ -0,0 +1,20 @@
# 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 .functional import compute_fbank_matrix
from .functional import create_dct
from .functional import fft_frequencies
from .functional import hz_to_mel
from .functional import mel_frequencies
from .functional import mel_to_hz
from .functional import power_to_db

@ -0,0 +1,265 @@
# 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.
# Modified from librosa(https://github.com/librosa/librosa)
import math
from typing import Optional
from typing import Union
import paddle
__all__ = [
'hz_to_mel',
'mel_to_hz',
'mel_frequencies',
'fft_frequencies',
'compute_fbank_matrix',
'power_to_db',
'create_dct',
]
def hz_to_mel(freq: Union[paddle.Tensor, float],
htk: bool=False) -> Union[paddle.Tensor, float]:
"""Convert Hz to Mels.
Parameters:
freq: the input tensor of arbitrary shape, or a single floating point number.
htk: use HTK formula to do the conversion.
The default value is False.
Returns:
The frequencies represented in Mel-scale.
"""
if htk:
if isinstance(freq, paddle.Tensor):
return 2595.0 * paddle.log10(1.0 + freq / 700.0)
else:
return 2595.0 * math.log10(1.0 + freq / 700.0)
# Fill in the linear part
f_min = 0.0
f_sp = 200.0 / 3
mels = (freq - f_min) / f_sp
# Fill in the log-scale part
min_log_hz = 1000.0 # beginning of log region (Hz)
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
logstep = math.log(6.4) / 27.0 # step size for log region
if isinstance(freq, paddle.Tensor):
target = min_log_mel + paddle.log(
freq / min_log_hz + 1e-10) / logstep # prevent nan with 1e-10
mask = (freq > min_log_hz).astype(freq.dtype)
mels = target * mask + mels * (
1 - mask) # will replace by masked_fill OP in future
else:
if freq >= min_log_hz:
mels = min_log_mel + math.log(freq / min_log_hz + 1e-10) / logstep
return mels
def mel_to_hz(mel: Union[float, paddle.Tensor],
htk: bool=False) -> Union[float, paddle.Tensor]:
"""Convert mel bin numbers to frequencies.
Parameters:
mel: the mel frequency represented as a tensor of arbitrary shape, or a floating point number.
htk: use HTK formula to do the conversion.
Returns:
The frequencies represented in hz.
"""
if htk:
return 700.0 * (10.0**(mel / 2595.0) - 1.0)
f_min = 0.0
f_sp = 200.0 / 3
freqs = f_min + f_sp * mel
# And now the nonlinear scale
min_log_hz = 1000.0 # beginning of log region (Hz)
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
logstep = math.log(6.4) / 27.0 # step size for log region
if isinstance(mel, paddle.Tensor):
target = min_log_hz * paddle.exp(logstep * (mel - min_log_mel))
mask = (mel > min_log_mel).astype(mel.dtype)
freqs = target * mask + freqs * (
1 - mask) # will replace by masked_fill OP in future
else:
if mel >= min_log_mel:
freqs = min_log_hz * math.exp(logstep * (mel - min_log_mel))
return freqs
def mel_frequencies(n_mels: int=64,
f_min: float=0.0,
f_max: float=11025.0,
htk: bool=False,
dtype: str=paddle.float32):
"""Compute mel frequencies.
Parameters:
n_mels(int): number of Mel bins.
f_min(float): the lower cut-off frequency, below which the filter response is zero.
f_max(float): the upper cut-off frequency, above which the filter response is zero.
htk(bool): whether to use htk formula.
dtype(str): the datatype of the return frequencies.
Returns:
The frequencies represented in Mel-scale
"""
# 'Center freqs' of mel bands - uniformly spaced between limits
min_mel = hz_to_mel(f_min, htk=htk)
max_mel = hz_to_mel(f_max, htk=htk)
mels = paddle.linspace(min_mel, max_mel, n_mels, dtype=dtype)
freqs = mel_to_hz(mels, htk=htk)
return freqs
def fft_frequencies(sr: int, n_fft: int, dtype: str=paddle.float32):
"""Compute fourier frequencies.
Parameters:
sr(int): the audio sample rate.
n_fft(float): the number of fft bins.
dtype(str): the datatype of the return frequencies.
Returns:
The frequencies represented in hz.
"""
return paddle.linspace(0, float(sr) / 2, int(1 + n_fft // 2), dtype=dtype)
def compute_fbank_matrix(sr: int,
n_fft: int,
n_mels: int=64,
f_min: float=0.0,
f_max: Optional[float]=None,
htk: bool=False,
norm: Union[str, float]='slaney',
dtype: str=paddle.float32):
"""Compute fbank matrix.
Parameters:
sr(int): the audio sample rate.
n_fft(int): the number of fft bins.
n_mels(int): the number of Mel bins.
f_min(float): the lower cut-off frequency, below which the filter response is zero.
f_max(float): the upper cut-off frequency, above which the filter response is zero.
htk: whether to use htk formula.
return_complex(bool): whether to return complex matrix. If True, the matrix will
be complex type. Otherwise, the real and image part will be stored in the last
axis of returned tensor.
dtype(str): the datatype of the returned fbank matrix.
Returns:
The fbank matrix of shape (n_mels, int(1+n_fft//2)).
Shape:
output: (n_mels, int(1+n_fft//2))
"""
if f_max is None:
f_max = float(sr) / 2
# Initialize the weights
weights = paddle.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype)
# Center freqs of each FFT bin
fftfreqs = fft_frequencies(sr=sr, n_fft=n_fft, dtype=dtype)
# 'Center freqs' of mel bands - uniformly spaced between limits
mel_f = mel_frequencies(
n_mels + 2, f_min=f_min, f_max=f_max, htk=htk, dtype=dtype)
fdiff = mel_f[1:] - mel_f[:-1] #np.diff(mel_f)
ramps = mel_f.unsqueeze(1) - fftfreqs.unsqueeze(0)
#ramps = np.subtract.outer(mel_f, fftfreqs)
for i in range(n_mels):
# lower and upper slopes for all bins
lower = -ramps[i] / fdiff[i]
upper = ramps[i + 2] / fdiff[i + 1]
# .. then intersect them with each other and zero
weights[i] = paddle.maximum(
paddle.zeros_like(lower), paddle.minimum(lower, upper))
# Slaney-style mel is scaled to be approx constant energy per channel
if norm == 'slaney':
enorm = 2.0 / (mel_f[2:n_mels + 2] - mel_f[:n_mels])
weights *= enorm.unsqueeze(1)
elif isinstance(norm, int) or isinstance(norm, float):
weights = paddle.nn.functional.normalize(weights, p=norm, axis=-1)
return weights
def power_to_db(magnitude: paddle.Tensor,
ref_value: float=1.0,
amin: float=1e-10,
top_db: Optional[float]=None) -> paddle.Tensor:
"""Convert a power spectrogram (amplitude squared) to decibel (dB) units.
The function computes the scaling ``10 * log10(x / ref)`` in a numerically
stable way.
Parameters:
magnitude(Tensor): the input magnitude tensor of any shape.
ref_value(float): the reference value. If smaller than 1.0, the db level
of the signal will be pulled up accordingly. Otherwise, the db level
is pushed down.
amin(float): the minimum value of input magnitude, below which the input
magnitude is clipped(to amin).
top_db(float): the maximum db value of resulting spectrum, above which the
spectrum is clipped(to top_db).
Returns:
The spectrogram in log-scale.
shape:
input: any shape
output: same as input
"""
if amin <= 0:
raise Exception("amin must be strictly positive")
if ref_value <= 0:
raise Exception("ref_value must be strictly positive")
ones = paddle.ones_like(magnitude)
log_spec = 10.0 * paddle.log10(paddle.maximum(ones * amin, magnitude))
log_spec -= 10.0 * math.log10(max(ref_value, amin))
if top_db is not None:
if top_db < 0:
raise Exception("top_db must be non-negative")
log_spec = paddle.maximum(log_spec, ones * (log_spec.max() - top_db))
return log_spec
def create_dct(n_mfcc: int,
n_mels: int,
norm: Optional[str]='ortho',
dtype: Optional[str]=paddle.float32) -> paddle.Tensor:
"""Create a discrete cosine transform(DCT) matrix.
Parameters:
n_mfcc (int): Number of mel frequency cepstral coefficients.
n_mels (int): Number of mel filterbanks.
norm (str, optional): Normalizaiton type. Defaults to 'ortho'.
Returns:
Tensor: The DCT matrix with shape (n_mels, n_mfcc).
"""
n = paddle.arange(n_mels, dtype=dtype)
k = paddle.arange(n_mfcc, dtype=dtype).unsqueeze(1)
dct = paddle.cos(math.pi / float(n_mels) * (n + 0.5) *
k) # size (n_mfcc, n_mels)
if norm is None:
dct *= 2.0
else:
assert norm == "ortho"
dct[0] *= 1.0 / math.sqrt(2.0)
dct *= math.sqrt(2.0 / float(n_mels))
return dct.T

@ -20,6 +20,19 @@ from paddle import Tensor
__all__ = [
'get_window',
# windows
'taylor',
'hamming',
'hann',
'tukey',
'kaiser',
'gaussian',
'exponential',
'triang',
'bohman',
'blackman',
'cosine',
]
@ -73,6 +86,21 @@ def general_gaussian(M: int, p, sig, sym: bool=True,
return _truncate(w, needs_trunc)
def general_cosine(M: int, a: float, sym: bool=True,
dtype: str='float64') -> Tensor:
"""Compute a generic weighted sum of cosine terms window.
This function is consistent with scipy.signal.windows.general_cosine().
"""
if _len_guards(M):
return paddle.ones((M, ), dtype=dtype)
M, needs_trunc = _extend(M, sym)
fac = paddle.linspace(-math.pi, math.pi, M, dtype=dtype)
w = paddle.zeros((M, ), dtype=dtype)
for k in range(len(a)):
w += a[k] * paddle.cos(k * fac)
return _truncate(w, needs_trunc)
def general_hamming(M: int, alpha: float, sym: bool=True,
dtype: str='float64') -> Tensor:
"""Compute a generalized Hamming window.
@ -143,21 +171,6 @@ def taylor(M: int,
return _truncate(w, needs_trunc)
def general_cosine(M: int, a: float, sym: bool=True,
dtype: str='float64') -> Tensor:
"""Compute a generic weighted sum of cosine terms window.
This function is consistent with scipy.signal.windows.general_cosine().
"""
if _len_guards(M):
return paddle.ones((M, ), dtype=dtype)
M, needs_trunc = _extend(M, sym)
fac = paddle.linspace(-math.pi, math.pi, M, dtype=dtype)
w = paddle.zeros((M, ), dtype=dtype)
for k in range(len(a)):
w += a[k] * paddle.cos(k * fac)
return _truncate(w, needs_trunc)
def hamming(M: int, sym: bool=True, dtype: str='float64') -> Tensor:
"""Compute a Hamming window.
The Hamming window is a taper formed by using a raised cosine with
@ -375,6 +388,7 @@ def cosine(M: int, sym: bool=True, dtype: str='float64') -> Tensor:
return _truncate(w, needs_trunc)
## factory function
def get_window(window: Union[str, Tuple[str, float]],
win_length: int,
fftbins: bool=True,

@ -11,4 +11,3 @@
# 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 import *

@ -0,0 +1,15 @@
# 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 .dtw import dtw_distance
from .mcd import mcd_distance

@ -0,0 +1,42 @@
# 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
from dtaidistance import dtw_ndim
__all__ = [
'dtw_distance',
]
def dtw_distance(xs: np.ndarray, ys: np.ndarray) -> float:
"""dtw distance
Dynamic Time Warping.
This function keeps a compact matrix, not the full warping paths matrix.
Uses dynamic programming to compute:
wps[i, j] = (s1[i]-s2[j])**2 + min(
wps[i-1, j ] + penalty, // vertical / insertion / expansion
wps[i , j-1] + penalty, // horizontal / deletion / compression
wps[i-1, j-1]) // diagonal / match
dtw = sqrt(wps[-1, -1])
Args:
xs (np.ndarray): ref sequence, [T,D]
ys (np.ndarray): hyp sequence, [T,D]
Returns:
float: dtw distance
"""
return dtw_ndim.distance(xs, ys)

@ -0,0 +1,48 @@
# 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 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=mt.logSpecDbDist):
"""Mel cepstral distortion (MCD), dtw distance.
Dynamic Time Warping.
Uses dynamic programming to compute:
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:
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]
Returns:
float: dtw distance
"""
min_cost, path = dtw.dtw(xs, ys, cost_fn)
return min_cost

@ -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.

@ -0,0 +1,25 @@
# 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.
from .download import decompress
from .download import download_and_decompress
from .download import load_state_dict_from_url
from .env import DATA_HOME
from .env import MODEL_HOME
from .env import PPAUDIO_HOME
from .env import USER_HOME
from .error import ParameterError
from .log import Logger
from .log import logger
from .time import seconds_to_hms
from .time import Timer

@ -22,6 +22,12 @@ from .log import logger
download.logger = logger
__all__ = [
'decompress',
'download_and_decompress',
'load_state_dict_from_url',
]
def decompress(file: str):
"""

@ -20,6 +20,13 @@ PPAUDIO_HOME --> the root directory for storing PaddleAudio related data. D
'''
import os
__all__ = [
'USER_HOME',
'PPAUDIO_HOME',
'MODEL_HOME',
'DATA_HOME',
]
def _get_user_home():
return os.path.expanduser('~')

@ -19,7 +19,10 @@ import time
import colorlog
loggers = {}
__all__ = [
'Logger',
'logger',
]
log_config = {
'DEBUG': {

@ -14,6 +14,11 @@
import math
import time
__all__ = [
'Timer',
'seconds_to_hms',
]
class Timer(object):
'''Calculate runing speed and estimated time of arrival(ETA)'''

@ -11,19 +11,46 @@
# 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 glob
import os
import setuptools
from setuptools.command.install import install
from setuptools.command.test import test
# set the version here
VERSION = '0.1.0'
VERSION = '0.2.0'
# Inspired by the example at https://pytest.org/latest/goodpractises.html
class TestCommand(test):
def finalize_options(self):
test.finalize_options(self)
self.test_args = []
self.test_suite = True
def run(self):
self.run_benchmark()
super(TestCommand, self).run()
def run_tests(self):
# Run nose ensuring that argv simulates running nosetests directly
import nose
nose.run_exit(argv=['nosetests', '-w', 'tests'])
def run_benchmark(self):
for benchmark_item in glob.glob('tests/benchmark/*py'):
os.system(f'pytest {benchmark_item}')
class InstallCommand(install):
def run(self):
install.run(self)
def write_version_py(filename='paddleaudio/__init__.py'):
import paddleaudio
if hasattr(paddleaudio,
"__version__") and paddleaudio.__version__ == VERSION:
return
with open(filename, "a") as f:
f.write(f"\n__version__ = '{VERSION}'\n")
f.write(f"__version__ = '{VERSION}'")
def remove_version_py(filename='paddleaudio/__init__.py'):
@ -35,6 +62,7 @@ def remove_version_py(filename='paddleaudio/__init__.py'):
f.write(line)
remove_version_py()
write_version_py()
setuptools.setup(
@ -59,6 +87,18 @@ setuptools.setup(
'resampy >= 0.2.2',
'soundfile >= 0.9.0',
'colorlog',
], )
'dtaidistance >= 2.3.6',
'mcd >= 0.4',
],
extras_require={
'test': [
'nose', 'librosa==0.8.1', 'soundfile==0.10.3.post1',
'torchaudio==0.10.2', 'pytest-benchmark'
],
},
cmdclass={
'install': InstallCommand,
'test': TestCommand,
}, )
remove_version_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.

@ -0,0 +1,34 @@
# 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 unittest
import urllib.request
mono_channel_wav = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
multi_channels_wav = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/cat.wav'
class BackendTest(unittest.TestCase):
def setUp(self):
self.initWavInput()
def initWavInput(self):
self.files = []
for url in [mono_channel_wav, multi_channels_wav]:
if not os.path.isfile(os.path.basename(url)):
urllib.request.urlretrieve(url, os.path.basename(url))
self.files.append(os.path.basename(url))
def initParmas(self):
raise NotImplementedError

@ -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.

@ -0,0 +1,73 @@
# 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 filecmp
import os
import unittest
import numpy as np
import soundfile as sf
import paddleaudio
from ..base import BackendTest
class TestIO(BackendTest):
def test_load_mono_channel(self):
sf_data, sf_sr = sf.read(self.files[0])
pa_data, pa_sr = paddleaudio.load(
self.files[0], normal=False, dtype='float64')
self.assertEqual(sf_data.dtype, pa_data.dtype)
self.assertEqual(sf_sr, pa_sr)
np.testing.assert_array_almost_equal(sf_data, pa_data)
def test_load_multi_channels(self):
sf_data, sf_sr = sf.read(self.files[1])
sf_data = sf_data.T # Channel dim first
pa_data, pa_sr = paddleaudio.load(
self.files[1], mono=False, normal=False, dtype='float64')
self.assertEqual(sf_data.dtype, pa_data.dtype)
self.assertEqual(sf_sr, pa_sr)
np.testing.assert_array_almost_equal(sf_data, pa_data)
def test_save_mono_channel(self):
waveform, sr = np.random.randint(
low=-32768, high=32768, size=(48000), dtype=np.int16), 16000
sf_tmp_file = 'sf_tmp.wav'
pa_tmp_file = 'pa_tmp.wav'
sf.write(sf_tmp_file, waveform, sr)
paddleaudio.save(waveform, sr, pa_tmp_file)
self.assertTrue(filecmp.cmp(sf_tmp_file, pa_tmp_file))
for file in [sf_tmp_file, pa_tmp_file]:
os.remove(file)
def test_save_multi_channels(self):
waveform, sr = np.random.randint(
low=-32768, high=32768, size=(2, 48000), dtype=np.int16), 16000
sf_tmp_file = 'sf_tmp.wav'
pa_tmp_file = 'pa_tmp.wav'
sf.write(sf_tmp_file, waveform.T, sr)
paddleaudio.save(waveform.T, sr, pa_tmp_file)
self.assertTrue(filecmp.cmp(sf_tmp_file, pa_tmp_file))
for file in [sf_tmp_file, pa_tmp_file]:
os.remove(file)
if __name__ == '__main__':
unittest.main()

@ -0,0 +1,39 @@
# 1. Prepare
First, install `pytest-benchmark` via pip.
```sh
pip install pytest-benchmark
```
# 2. Run
Run the specific script for profiling.
```sh
pytest melspectrogram.py
```
Result:
```sh
========================================================================== test session starts ==========================================================================
platform linux -- Python 3.7.7, pytest-7.0.1, pluggy-1.0.0
benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /ssd3/chenxiaojie06/PaddleSpeech/DeepSpeech/paddleaudio
plugins: typeguard-2.12.1, benchmark-3.4.1, anyio-3.5.0
collected 4 items
melspectrogram.py .... [100%]
-------------------------------------------------------------------------------------------------- benchmark: 4 tests -------------------------------------------------------------------------------------------------
Name (time in us) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_melspect_gpu_torchaudio 202.0765 (1.0) 360.6230 (1.0) 218.1168 (1.0) 16.3022 (1.0) 214.2871 (1.0) 21.8451 (1.0) 40;3 4,584.7001 (1.0) 286 1
test_melspect_gpu 657.8509 (3.26) 908.0470 (2.52) 724.2545 (3.32) 106.5771 (6.54) 669.9096 (3.13) 113.4719 (5.19) 1;0 1,380.7300 (0.30) 5 1
test_melspect_cpu_torchaudio 1,247.6053 (6.17) 2,892.5799 (8.02) 1,443.2853 (6.62) 345.3732 (21.19) 1,262.7263 (5.89) 221.6385 (10.15) 56;53 692.8637 (0.15) 399 1
test_melspect_cpu 20,326.2549 (100.59) 20,607.8682 (57.15) 20,473.4125 (93.86) 63.8654 (3.92) 20,467.0429 (95.51) 68.4294 (3.13) 8;1 48.8438 (0.01) 29 1
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Legend:
Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
OPS: Operations Per Second, computed as 1 / Mean
========================================================================== 4 passed in 21.12s ===========================================================================
```

@ -0,0 +1,124 @@
# 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 urllib.request
import librosa
import numpy as np
import paddle
import torch
import torchaudio
import paddleaudio
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
if not os.path.isfile(os.path.basename(wav_url)):
urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
waveform, sr = paddleaudio.load(os.path.abspath(os.path.basename(wav_url)))
waveform_tensor = paddle.to_tensor(waveform).unsqueeze(0)
waveform_tensor_torch = torch.from_numpy(waveform).unsqueeze(0)
# Feature conf
mel_conf = {
'sr': sr,
'n_fft': 512,
'hop_length': 128,
'n_mels': 40,
}
mel_conf_torchaudio = {
'sample_rate': sr,
'n_fft': 512,
'hop_length': 128,
'n_mels': 40,
'norm': 'slaney',
'mel_scale': 'slaney',
}
def enable_cpu_device():
paddle.set_device('cpu')
def enable_gpu_device():
paddle.set_device('gpu')
log_mel_extractor = paddleaudio.features.LogMelSpectrogram(
**mel_conf, f_min=0.0, top_db=80.0, dtype=waveform_tensor.dtype)
def log_melspectrogram():
return log_mel_extractor(waveform_tensor).squeeze(0)
def test_log_melspect_cpu(benchmark):
enable_cpu_device()
feature_paddleaudio = benchmark(log_melspectrogram)
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=3)
def test_log_melspect_gpu(benchmark):
enable_gpu_device()
feature_paddleaudio = benchmark(log_melspectrogram)
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=2)
mel_extractor_torchaudio = torchaudio.transforms.MelSpectrogram(
**mel_conf_torchaudio, f_min=0.0)
amplitude_to_DB = torchaudio.transforms.AmplitudeToDB('power', top_db=80.0)
def melspectrogram_torchaudio():
return mel_extractor_torchaudio(waveform_tensor_torch).squeeze(0)
def log_melspectrogram_torchaudio():
mel_specgram = mel_extractor_torchaudio(waveform_tensor_torch)
return amplitude_to_DB(mel_specgram).squeeze(0)
def test_log_melspect_cpu_torchaudio(benchmark):
global waveform_tensor_torch, mel_extractor_torchaudio, amplitude_to_DB
mel_extractor_torchaudio = mel_extractor_torchaudio.to('cpu')
waveform_tensor_torch = waveform_tensor_torch.to('cpu')
amplitude_to_DB = amplitude_to_DB.to('cpu')
feature_paddleaudio = benchmark(log_melspectrogram_torchaudio)
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=3)
def test_log_melspect_gpu_torchaudio(benchmark):
global waveform_tensor_torch, mel_extractor_torchaudio, amplitude_to_DB
mel_extractor_torchaudio = mel_extractor_torchaudio.to('cuda')
waveform_tensor_torch = waveform_tensor_torch.to('cuda')
amplitude_to_DB = amplitude_to_DB.to('cuda')
feature_torchaudio = benchmark(log_melspectrogram_torchaudio)
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
np.testing.assert_array_almost_equal(
feature_librosa, feature_torchaudio.cpu(), decimal=2)

@ -0,0 +1,108 @@
# 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 urllib.request
import librosa
import numpy as np
import paddle
import torch
import torchaudio
import paddleaudio
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
if not os.path.isfile(os.path.basename(wav_url)):
urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
waveform, sr = paddleaudio.load(os.path.abspath(os.path.basename(wav_url)))
waveform_tensor = paddle.to_tensor(waveform).unsqueeze(0)
waveform_tensor_torch = torch.from_numpy(waveform).unsqueeze(0)
# Feature conf
mel_conf = {
'sr': sr,
'n_fft': 512,
'hop_length': 128,
'n_mels': 40,
}
mel_conf_torchaudio = {
'sample_rate': sr,
'n_fft': 512,
'hop_length': 128,
'n_mels': 40,
'norm': 'slaney',
'mel_scale': 'slaney',
}
def enable_cpu_device():
paddle.set_device('cpu')
def enable_gpu_device():
paddle.set_device('gpu')
mel_extractor = paddleaudio.features.MelSpectrogram(
**mel_conf, f_min=0.0, dtype=waveform_tensor.dtype)
def melspectrogram():
return mel_extractor(waveform_tensor).squeeze(0)
def test_melspect_cpu(benchmark):
enable_cpu_device()
feature_paddleaudio = benchmark(melspectrogram)
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=3)
def test_melspect_gpu(benchmark):
enable_gpu_device()
feature_paddleaudio = benchmark(melspectrogram)
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=3)
mel_extractor_torchaudio = torchaudio.transforms.MelSpectrogram(
**mel_conf_torchaudio, f_min=0.0)
def melspectrogram_torchaudio():
return mel_extractor_torchaudio(waveform_tensor_torch).squeeze(0)
def test_melspect_cpu_torchaudio(benchmark):
global waveform_tensor_torch, mel_extractor_torchaudio
mel_extractor_torchaudio = mel_extractor_torchaudio.to('cpu')
waveform_tensor_torch = waveform_tensor_torch.to('cpu')
feature_paddleaudio = benchmark(melspectrogram_torchaudio)
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=3)
def test_melspect_gpu_torchaudio(benchmark):
global waveform_tensor_torch, mel_extractor_torchaudio
mel_extractor_torchaudio = mel_extractor_torchaudio.to('cuda')
waveform_tensor_torch = waveform_tensor_torch.to('cuda')
feature_torchaudio = benchmark(melspectrogram_torchaudio)
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
np.testing.assert_array_almost_equal(
feature_librosa, feature_torchaudio.cpu(), decimal=3)

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