# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import base64 import io import os import time from typing import Optional import librosa import numpy as np import paddle import soundfile as sf 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.server.engine.base_engine import BaseEngine from paddlespeech.server.utils.audio_process import change_speed from paddlespeech.server.utils.errors import ErrorCode from paddlespeech.server.utils.exception import ServerBaseException from paddlespeech.server.utils.paddle_predictor import init_predictor from paddlespeech.server.utils.paddle_predictor import run_model from paddlespeech.t2s.frontend import English from paddlespeech.t2s.frontend.zh_frontend import Frontend __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': 'f10cbdedf47dc7a9668d2264494e1823', 'model': 'speedyspeech_csmsc.pdmodel', 'params': 'speedyspeech_csmsc.pdiparams', 'phones_dict': 'phone_id_map.txt', 'tones_dict': 'tone_id_map.txt', 'sample_rate': 24000, }, # fastspeech2 "fastspeech2_csmsc-zh": { 'url': 'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip', 'md5': '9788cd9745e14c7a5d12d32670b2a5a7', 'model': 'fastspeech2_csmsc.pdmodel', 'params': 'fastspeech2_csmsc.pdiparams', 'phones_dict': 'phone_id_map.txt', 'sample_rate': 24000, }, # 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', 'sample_rate': 24000, }, # 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', 'sample_rate': 24000, }, # 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', 'sample_rate': 24000, }, } class TTSServerExecutor(TTSExecutor): def __init__(self): super().__init__() pass 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, am_sample_rate: int=24000, 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, voc_sample_rate: int=24000, 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_predictor') and hasattr(self, 'voc_predictor'): logger.info('Models had been initialized.') return # am am_tag = am + '-' + lang if am_model is None or am_params is None or phones_dict is None: am_res_path = self._get_pretrained_path(am_tag) self.am_res_path = am_res_path self.am_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.am_sample_rate = pretrained_models[am_tag]['sample_rate'] 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_sample_rate = am_sample_rate self.am_res_path = os.path.dirname(os.path.abspath(self.am_model)) logger.info("self.phones_dict: {}".format(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']) self.voc_sample_rate = pretrained_models[voc_tag]['sample_rate'] 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_sample_rate = voc_sample_rate self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_model)) assert ( self.voc_sample_rate == self.am_sample_rate ), "The sample rate of AM and Vocoder model are different, please check 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) logger.info("vocab_size: {}".format(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) logger.info("tone_size: {}".format(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) logger.info("spk_num: {}".format(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) logger.info("frontend done!") try: # 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) logger.info("Create AM predictor successfully.") except BaseException: logger.error("Failed to create AM predictor.") try: # 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) logger.info("Create Vocoder predictor successfully.") except BaseException: logger.error("Failed to create Vocoder predictor.") @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 frontend_st = time.time() 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: logger.error("lang should in {'zh', 'en'}!") self.frontend_time = time.time() - frontend_st self.am_time = 0 self.voc_time = 0 flags = 0 for i in range(len(phone_ids)): am_st = time.time() 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] self.am_time += (time.time() - am_st) # voc voc_st = time.time() 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.voc_time += (time.time() - voc_st) self._outputs['wav'] = wav_all class TTSEngine(BaseEngine): """TTS server engine Args: metaclass: Defaults to Singleton. """ def __init__(self): """Initialize TTS server engine """ super(TTSEngine, self).__init__() def init(self, config: dict) -> bool: self.executor = TTSServerExecutor() try: self.config = config self.executor._init_from_path( am=self.config.am, am_model=self.config.am_model, am_params=self.config.am_params, am_sample_rate=self.config.am_sample_rate, phones_dict=self.config.phones_dict, tones_dict=self.config.tones_dict, speaker_dict=self.config.speaker_dict, voc=self.config.voc, voc_model=self.config.voc_model, voc_params=self.config.voc_params, voc_sample_rate=self.config.voc_sample_rate, lang=self.config.lang, am_predictor_conf=self.config.am_predictor_conf, voc_predictor_conf=self.config.voc_predictor_conf, ) except BaseException: logger.error("Initialize TTS server engine Failed.") return False logger.info("Initialize TTS server engine successfully.") return True def postprocess(self, wav, original_fs: int, target_fs: int=0, 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 Raises: ServerBaseException: Throws an exception if the change speed unsuccessfully. Returns: target_fs: target sample rate for synthesized audio. wav_base64: The base64 format of the synthesized audio. """ # transform sample_rate if target_fs == 0 or target_fs > original_fs: target_fs = original_fs wav_tar_fs = wav logger.info( "The sample rate of synthesized audio is the same as model, which is {}Hz". format(original_fs)) else: wav_tar_fs = librosa.resample( np.squeeze(wav), original_fs, target_fs) logger.info( "The sample rate of model is {}Hz and the target sample rate is {}Hz. Converting the sample rate of the synthesized audio successfully.". format(original_fs, target_fs)) # transform volume wav_vol = wav_tar_fs * volume logger.info("Transform the volume of the audio successfully.") # transform speed try: # windows not support soxbindings wav_speed = change_speed(wav_vol, speed, target_fs) logger.info("Transform the speed of the audio successfully.") except ServerBaseException: raise ServerBaseException( ErrorCode.SERVER_INTERNAL_ERR, "Failed to transform speed. Can not install soxbindings on your system. \ You need to set speed value 1.0.") except BaseException: logger.error("Failed to transform speed.") # 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') logger.info("Audio to string successfully.") # save audio if audio_path is not None: if audio_path.endswith(".wav"): sf.write(audio_path, wav_speed, target_fs) elif 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)) logger.info("Save audio to {} successfully.".format(audio_path)) else: logger.info("There is no need to save audio.") 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: Throws an exception if tts inference unsuccessfully. ServerBaseException: Throws an exception if postprocess unsuccessfully. Returns: lang: model language target_sample_rate: target sample rate for synthesized audio. wav_base64: The base64 format of the synthesized audio. """ lang = self.config.lang try: infer_st = time.time() self.executor.infer( text=sentence, lang=lang, am=self.config.am, spk_id=spk_id) infer_et = time.time() infer_time = infer_et - infer_st except ServerBaseException: raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR, "tts infer failed.") except BaseException: logger.error("tts infer failed.") try: postprocess_st = time.time() target_sample_rate, wav_base64 = self.postprocess( wav=self.executor._outputs['wav'].numpy(), original_fs=self.executor.am_sample_rate, target_fs=sample_rate, volume=volume, speed=speed, audio_path=save_path) postprocess_et = time.time() postprocess_time = postprocess_et - postprocess_st duration = len(self.executor._outputs['wav'] .numpy()) / self.executor.am_sample_rate rtf = infer_time / duration except ServerBaseException: raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR, "tts postprocess failed.") except BaseException: logger.error("tts postprocess failed.") logger.info("AM model: {}".format(self.config.am)) logger.info("Vocoder model: {}".format(self.config.voc)) logger.info("Language: {}".format(lang)) logger.info("tts engine type: paddle inference") logger.info("audio duration: {}".format(duration)) logger.info( "frontend inference time: {}".format(self.executor.frontend_time)) logger.info("AM inference time: {}".format(self.executor.am_time)) logger.info("Vocoder inference time: {}".format(self.executor.voc_time)) logger.info("total inference time: {}".format(infer_time)) logger.info( "postprocess (change speed, volume, target sample rate) time: {}". format(postprocess_time)) logger.info("total generate audio time: {}".format(infer_time + postprocess_time)) logger.info("RTF: {}".format(rtf)) return lang, target_sample_rate, wav_base64