# 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 from typing import Optional import librosa import numpy as np import paddle import soundfile as sf 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.config import get_config from utils.errors import ErrorCode from utils.exception import ServerBaseException from utils.paddle_predictor import init_predictor from utils.paddle_predictor import run_model __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') and hasattr(self, 'voc'): 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)) 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']) 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) 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): """Initialize TTS server engine """ super(TTSEngine, self).__init__() def init(self, config_file: str) -> bool: self.executor = TTSServerExecutor() self.config_file = config_file self.config = get_config(config_file) 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, ) logger.info("Initialize TTS server engine successfully.") return True 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 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 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: 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: self.executor.infer( text=sentence, lang=lang, am=self.config.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_sample_rate, 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