diff --git a/speechserving/speechserving/conf/tts/tts_pd.yaml b/speechserving/speechserving/conf/tts/tts_pd.yaml new file mode 100644 index 00000000..7a1ca5dc --- /dev/null +++ b/speechserving/speechserving/conf/tts/tts_pd.yaml @@ -0,0 +1,45 @@ +# This is the parameter configuration file for TTS server. +# These are the static models that support paddle inference. + +################################################################## +# TTS SERVER SETTING # +################################################################## +host: '0.0.0.0' +port: 8692 + +################################################################## +# ACOUSTIC MODEL SETTING # +# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc'] +################################################################## +am: 'fastspeech2_csmsc' +am_model: +am_params: +phones_dict: './dict_dir/phone_id_map.txt' +tones_dict: +speaker_dict: +spk_id: 0 + +am_predictor_conf: + use_gpu: 'true' + enable_mkldnn: 'true' + switch_ir_optim: 'true' + + +################################################################## +# VOCODER SETTING # +# voc choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc'] +################################################################## +voc: 'pwgan_csmsc' +voc_model: +voc_params: + +voc_predictor_conf: + use_gpu: 'true' + enable_mkldnn: 'true' + switch_ir_optim: 'true' + +################################################################## +# OTHERS # +################################################################## +lang: 'zh' +device: paddle.get_device() \ No newline at end of file diff --git a/speechserving/speechserving/engine/tts/paddleinference/tts_engine.py b/speechserving/speechserving/engine/tts/paddleinference/tts_engine.py new file mode 100644 index 00000000..b394ceaa --- /dev/null +++ b/speechserving/speechserving/engine/tts/paddleinference/tts_engine.py @@ -0,0 +1,463 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import base64 +import io +import os +from typing import Optional + +import librosa +import numpy as np +import paddle +import soundfile as sf +import yaml +from engine.base_engine import BaseEngine +from scipy.io import wavfile + +from paddlespeech.cli.log import logger +from paddlespeech.cli.tts.infer import TTSExecutor +from paddlespeech.cli.utils import download_and_decompress +from paddlespeech.cli.utils import MODEL_HOME +from paddlespeech.t2s.frontend import English +from paddlespeech.t2s.frontend.zh_frontend import Frontend +from utils.audio_process import change_speed +from utils.errors import ErrorCode +from utils.exception import ServerBaseException +from utils.paddle_predictor import init_predictor +from utils.paddle_predictor import run_model +#from paddle.inference import Config +#from paddle.inference import create_predictor + +__all__ = ['TTSEngine'] + +# Static model applied on paddle inference +pretrained_models = { + # speedyspeech + "speedyspeech_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip', + 'md5': + '9a849a74d1be0c758dd5a1b9c8f77f3d', + 'model': + 'speedyspeech_csmsc.pdmodel', + 'params': + 'speedyspeech_csmsc.pdiparams', + 'phones_dict': + 'phone_id_map.txt', + 'tones_dict': + 'tone_id_map.txt', + }, + # fastspeech2 + "fastspeech2_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip', + 'md5': + '8eb01c2e4bc7e8b59beaa9fa046069cf', + 'model': + 'fastspeech2_csmsc.pdmodel', + 'params': + 'fastspeech2_csmsc.pdiparams', + 'phones_dict': + 'phone_id_map.txt', + }, + # pwgan + "pwgan_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip', + 'md5': + 'e3504aed9c5a290be12d1347836d2742', + 'model': + 'pwgan_csmsc.pdmodel', + 'params': + 'pwgan_csmsc.pdiparams', + }, + # mb_melgan + "mb_melgan_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_static_0.1.1.zip', + 'md5': + 'ac6eee94ba483421d750433f4c3b8d36', + 'model': + 'mb_melgan_csmsc.pdmodel', + 'params': + 'mb_melgan_csmsc.pdiparams', + }, + # hifigan + "hifigan_csmsc-zh": { + 'url': + 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_static_0.1.1.zip', + 'md5': + '7edd8c436b3a5546b3a7cb8cff9d5a0c', + 'model': + 'hifigan_csmsc.pdmodel', + 'params': + 'hifigan_csmsc.pdiparams', + }, +} + + +class TTSServerExecutor(TTSExecutor): + def __init__(self): + super().__init__() + + self.parser = argparse.ArgumentParser( + prog='paddlespeech.tts', add_help=True) + self.parser.add_argument( + '--conf', + type=str, + default='./conf/tts/tts_pd.yaml', + help='Configuration parameters.') + + def _get_pretrained_path(self, tag: str) -> os.PathLike: + """ + Download and returns pretrained resources path of current task. + """ + assert tag in pretrained_models, 'Can not find pretrained resources of {}.'.format( + tag) + + res_path = os.path.join(MODEL_HOME, tag) + decompressed_path = download_and_decompress(pretrained_models[tag], + res_path) + decompressed_path = os.path.abspath(decompressed_path) + logger.info( + 'Use pretrained model stored in: {}'.format(decompressed_path)) + return decompressed_path + + def _init_from_path( + self, + am: str='fastspeech2_csmsc', + am_model: Optional[os.PathLike]=None, + am_params: Optional[os.PathLike]=None, + phones_dict: Optional[os.PathLike]=None, + tones_dict: Optional[os.PathLike]=None, + speaker_dict: Optional[os.PathLike]=None, + voc: str='pwgan_csmsc', + voc_model: Optional[os.PathLike]=None, + voc_params: Optional[os.PathLike]=None, + lang: str='zh', + am_predictor_conf: dict=None, + voc_predictor_conf: dict=None, ): + """ + Init model and other resources from a specific path. + """ + if hasattr(self, 'am') and hasattr(self, 'voc'): + logger.info('Models had been initialized.') + return + # am + am_tag = am + '-' + lang + if phones_dict is None: + print("please input phones_dict!") + ### 后续下载的模型里加上 phone 和 tone的 dict 就不用这个了 + #if am_model is None or am_params is None or phones_dict is None: + if am_model is None or am_params is None: + am_res_path = self._get_pretrained_path(am_tag) + self.am_res_path = am_res_path + self.am_model = os.path.join(am_res_path, + pretrained_models[am_tag]['model']) + self.am_params = os.path.join(am_res_path, + pretrained_models[am_tag]['params']) + # must have phones_dict in acoustic + #self.phones_dict = os.path.join( + #am_res_path, pretrained_models[am_tag]['phones_dict']) + + self.phones_dict = os.path.abspath(phones_dict) + logger.info(am_res_path) + logger.info(self.am_model) + logger.info(self.am_params) + else: + self.am_model = os.path.abspath(am_model) + self.am_params = os.path.abspath(am_params) + self.phones_dict = os.path.abspath(phones_dict) + self.am_res_path = os.path.dirname(os.path.abspath(self.am_model)) + print("self.phones_dict:", self.phones_dict) + + # for speedyspeech + self.tones_dict = None + if 'tones_dict' in pretrained_models[am_tag]: + self.tones_dict = os.path.join( + am_res_path, pretrained_models[am_tag]['tones_dict']) + if tones_dict: + self.tones_dict = tones_dict + + # for multi speaker fastspeech2 + self.speaker_dict = None + if 'speaker_dict' in pretrained_models[am_tag]: + self.speaker_dict = os.path.join( + am_res_path, pretrained_models[am_tag]['speaker_dict']) + if speaker_dict: + self.speaker_dict = speaker_dict + + # voc + voc_tag = voc + '-' + lang + if voc_model is None or voc_params is None: + voc_res_path = self._get_pretrained_path(voc_tag) + self.voc_res_path = voc_res_path + self.voc_model = os.path.join(voc_res_path, + pretrained_models[voc_tag]['model']) + self.voc_params = os.path.join(voc_res_path, + pretrained_models[voc_tag]['params']) + logger.info(voc_res_path) + logger.info(self.voc_model) + logger.info(self.voc_params) + else: + self.voc_model = os.path.abspath(voc_model) + self.voc_params = os.path.abspath(voc_params) + self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_model)) + + # Init body. + with open(self.phones_dict, "r") as f: + phn_id = [line.strip().split() for line in f.readlines()] + vocab_size = len(phn_id) + print("vocab_size:", vocab_size) + + tone_size = None + if self.tones_dict: + with open(self.tones_dict, "r") as f: + tone_id = [line.strip().split() for line in f.readlines()] + tone_size = len(tone_id) + print("tone_size:", tone_size) + + spk_num = None + if self.speaker_dict: + with open(self.speaker_dict, 'rt') as f: + spk_id = [line.strip().split() for line in f.readlines()] + spk_num = len(spk_id) + print("spk_num:", spk_num) + + # frontend + if lang == 'zh': + self.frontend = Frontend( + phone_vocab_path=self.phones_dict, + tone_vocab_path=self.tones_dict) + + elif lang == 'en': + self.frontend = English(phone_vocab_path=self.phones_dict) + print("frontend done!") + + # am predictor + self.am_predictor_conf = am_predictor_conf + self.am_predictor = init_predictor( + model_file=self.am_model, + params_file=self.am_params, + predictor_conf=self.am_predictor_conf) + + # voc predictor + self.voc_predictor_conf = voc_predictor_conf + self.voc_predictor = init_predictor( + model_file=self.voc_model, + params_file=self.voc_params, + predictor_conf=self.voc_predictor_conf) + + @paddle.no_grad() + def infer(self, + text: str, + lang: str='zh', + am: str='fastspeech2_csmsc', + spk_id: int=0): + """ + Model inference and result stored in self.output. + """ + am_name = am[:am.rindex('_')] + am_dataset = am[am.rindex('_') + 1:] + get_tone_ids = False + merge_sentences = False + if am_name == 'speedyspeech': + get_tone_ids = True + if lang == 'zh': + input_ids = self.frontend.get_input_ids( + text, + merge_sentences=merge_sentences, + get_tone_ids=get_tone_ids) + phone_ids = input_ids["phone_ids"] + if get_tone_ids: + tone_ids = input_ids["tone_ids"] + elif lang == 'en': + input_ids = self.frontend.get_input_ids( + text, merge_sentences=merge_sentences) + phone_ids = input_ids["phone_ids"] + else: + print("lang should in {'zh', 'en'}!") + + flags = 0 + for i in range(len(phone_ids)): + part_phone_ids = phone_ids[i] + # am + if am_name == 'speedyspeech': + part_tone_ids = tone_ids[i] + am_result = run_model( + self.am_predictor, + [part_phone_ids.numpy(), part_tone_ids.numpy()]) + mel = am_result[0] + + # fastspeech2 + else: + # multi speaker do not have static model + if am_dataset in {"aishell3", "vctk"}: + pass + else: + am_result = run_model(self.am_predictor, + [part_phone_ids.numpy()]) + mel = am_result[0] + # voc + voc_result = run_model(self.voc_predictor, [mel]) + wav = voc_result[0] + wav = paddle.to_tensor(wav) + + if flags == 0: + wav_all = wav + flags = 1 + else: + wav_all = paddle.concat([wav_all, wav]) + self._outputs['wav'] = wav_all + + +class TTSEngine(BaseEngine): + """TTS server engine + + Args: + metaclass: Defaults to Singleton. + """ + + def __init__(self, name=None): + """Initialize TTS server engine + """ + super(TTSEngine, self).__init__() + self.executor = TTSServerExecutor() + + config_path = self.executor.parser.parse_args().conf + with open(config_path, 'rt') as f: + self.conf_dict = yaml.safe_load(f) + + self.executor._init_from_path( + am=self.conf_dict["am"], + am_model=self.conf_dict["am_model"], + am_params=self.conf_dict["am_params"], + phones_dict=self.conf_dict["phones_dict"], + tones_dict=self.conf_dict["tones_dict"], + speaker_dict=self.conf_dict["speaker_dict"], + voc=self.conf_dict["voc"], + voc_model=self.conf_dict["voc_model"], + voc_params=self.conf_dict["voc_params"], + lang=self.conf_dict["lang"], + am_predictor_conf=self.conf_dict["am_predictor_conf"], + voc_predictor_conf=self.conf_dict["voc_predictor_conf"], ) + + logger.info("Initialize TTS server engine successfully.") + + def postprocess(self, + wav, + original_fs: int, + target_fs: int=16000, + volume: float=1.0, + speed: float=1.0, + audio_path: str=None): + """Post-processing operations, including speech, volume, sample rate, save audio file + + Args: + wav (numpy(float)): Synthesized audio sample points + original_fs (int): original audio sample rate + target_fs (int): target audio sample rate + volume (float): target volume + speed (float): target speed + """ + + # transform sample_rate + if target_fs == 0 or target_fs > original_fs: + target_fs = original_fs + wav_tar_fs = wav + else: + wav_tar_fs = librosa.resample( + np.squeeze(wav), original_fs, target_fs) + + # transform volume + wav_vol = wav_tar_fs * volume + + # transform speed + try: # windows not support soxbindings + wav_speed = change_speed(wav_vol, speed, target_fs) + except: + raise ServerBaseException( + ErrorCode.SERVER_INTERNAL_ERR, + "Can not install soxbindings on your system.") + + # wav to base64 + buf = io.BytesIO() + wavfile.write(buf, target_fs, wav_speed) + base64_bytes = base64.b64encode(buf.read()) + wav_base64 = base64_bytes.decode('utf-8') + + # save audio + if audio_path is not None and audio_path.endswith(".wav"): + sf.write(audio_path, wav_speed, target_fs) + elif audio_path is not None and audio_path.endswith(".pcm"): + wav_norm = wav_speed * (32767 / max(0.001, + np.max(np.abs(wav_speed)))) + with open(audio_path, "wb") as f: + f.write(wav_norm.astype(np.int16)) + + return target_fs, wav_base64 + + def run(self, + sentence: str, + spk_id: int=0, + speed: float=1.0, + volume: float=1.0, + sample_rate: int=0, + save_path: str=None): + """get the result of the server response + + Args: + sentence (str): sentence to be synthesized + spk_id (int, optional): speaker id. Defaults to 0. + speed (float, optional): audio speed, 0 < speed <=3.0. Defaults to 1.0. + volume (float, optional): The volume relative to the audio synthesized by the model, + 0 < volume <=3.0. Defaults to 1.0. + sample_rate (int, optional): Set the sample rate of the synthesized audio. + 0 represents the sample rate for model synthesis. Defaults to 0. + save_path (str, optional): The save path of the synthesized audio. Defaults to None. + + Raises: + ServerBaseException: Exception + ServerBaseException: Exception + + Returns: + lang, target_sample_rate, wav_base64 + """ + + lang = self.conf_dict["lang"] + + try: + self.executor.infer( + text=sentence, + lang=lang, + am=self.conf_dict["am"], + spk_id=spk_id) + except: + raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR, + "tts infer failed.") + + try: + target_sample_rate, wav_base64 = self.postprocess( + wav=self.executor._outputs['wav'].numpy(), + #original_fs=self.executor.am_config.fs, + original_fs=24000, # TODO get sample rate from model + target_fs=sample_rate, + volume=volume, + speed=speed, + audio_path=save_path) + except: + raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR, + "tts postprocess failed.") + + return lang, target_sample_rate, wav_base64 diff --git a/speechserving/speechserving/utils/audio_process.py b/speechserving/speechserving/utils/audio_process.py index 51a19b36..78f120a6 100644 --- a/speechserving/speechserving/utils/audio_process.py +++ b/speechserving/speechserving/utils/audio_process.py @@ -17,11 +17,11 @@ import numpy as np def wav2pcm(wavfile, pcmfile, data_type=np.int16): - f = open(wavfile, "rb") - f.seek(0) - f.read(44) - data = np.fromfile(f, dtype=data_type) - data.tofile(pcmfile) + with open(wavfile, "rb") as f: + f.seek(0) + f.read(44) + data = np.fromfile(f, dtype=data_type) + data.tofile(pcmfile) def pcm2wav(pcm_file, wav_file, channels=1, bits=16, sample_rate=16000): @@ -52,7 +52,7 @@ def change_speed(sample_raw, speed_rate, sample_rate): :raises ValueError: If speed_rate <= 0.0. """ if speed_rate == 1.0: - return + return sample_raw if speed_rate <= 0: raise ValueError("speed_rate should be greater than zero.") diff --git a/speechserving/speechserving/utils/paddle_predictor.py b/speechserving/speechserving/utils/paddle_predictor.py new file mode 100644 index 00000000..608b2b4c --- /dev/null +++ b/speechserving/speechserving/utils/paddle_predictor.py @@ -0,0 +1,82 @@ +# 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 os +from typing import Optional + +from paddle.inference import Config +from paddle.inference import create_predictor + + +def init_predictor(model_dir: Optional[os.PathLike]=None, + model_file: Optional[os.PathLike]=None, + params_file: Optional[os.PathLike]=None, + predictor_conf: dict=None): + """Create predictor with Paddle inference + + Args: + model_dir (Optional[os.PathLike], optional): The path of the static model saved in the model layer. Defaults to None. + model_file (Optional[os.PathLike], optional): *.pdmodel file path. Defaults to None. + params_file (Optional[os.PathLike], optional): *.pdiparams file path.. Defaults to None. + predictor_conf (dict, optional): The configuration parameters of predictor. Defaults to None. + + Returns: + [type]: [description] + """ + if model_dir is not None: + config = Config(args.model_dir) + else: + config = Config(model_file, params_file) + + config.enable_memory_optim() + if "use_gpu" in predictor_conf and predictor_conf["use_gpu"] == "true": + config.enable_use_gpu(1000, 0) + if "enable_mkldnn" in predictor_conf and predictor_conf[ + "enable_mkldnn"] == "true": + config.enable_mkldnn() + if "switch_ir_optim" in predictor_conf and predictor_conf[ + "switch_ir_optim"] == "true": + config.switch_ir_optim() + + predictor = create_predictor(config) + + return predictor + + +def run_model(predictor, input: list): + """ run predictor + + Args: + predictor: paddle inference predictor + input (list): The input of predictor + + Returns: + list: result list + """ + input_names = predictor.get_input_names() + for i, name in enumerate(input_names): + input_handle = predictor.get_input_handle(name) + input_handle.copy_from_cpu(input[i]) + + # do the inference + predictor.run() + + results = [] + # get out data from output tensor + output_names = predictor.get_output_names() + for i, name in enumerate(output_names): + output_handle = predictor.get_output_handle(name) + output_data = output_handle.copy_to_cpu() + results.append(output_data) + + return results