# 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 time 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.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 __all__ = ['TTSEngine'] class TTSServerExecutor(TTSExecutor): def __init__(self): super().__init__() pass class TTSEngine(BaseEngine): """TTS server engine Args: metaclass: Defaults to Singleton. """ def __init__(self, name=None): """Initialize TTS server engine """ super(TTSEngine, self).__init__() def init(self, config: dict) -> bool: self.executor = TTSServerExecutor() try: self.config = config if self.config.device: self.device = self.config.device else: self.device = paddle.get_device() paddle.set_device(self.device) except BaseException: logger.error( "Set device failed, please check if device is already used and the parameter 'device' in the yaml file" ) logger.error("Initialize TTS server engine Failed on device: %s." % (self.device)) return False try: self.executor._init_from_path( am=self.config.am, am_config=self.config.am_config, am_ckpt=self.config.am_ckpt, am_stat=self.config.am_stat, phones_dict=self.config.phones_dict, tones_dict=self.config.tones_dict, speaker_dict=self.config.speaker_dict, voc=self.config.voc, voc_config=self.config.voc_config, voc_ckpt=self.config.voc_ckpt, voc_stat=self.config.voc_stat, lang=self.config.lang) except BaseException: logger.error("Failed to get model related files.") logger.error("Initialize TTS server engine Failed on device: %s." % (self.device)) return False logger.info("Initialize TTS server engine successfully on device: %s." % (self.device)) 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): """ run include inference and postprocess. Args: sentence (str): text to be synthesized spk_id (int, optional): speaker id for multi-speaker speech synthesis. Defaults to 0. speed (float, optional): speed. Defaults to 1.0. volume (float, optional): volume. Defaults to 1.0. sample_rate (int, optional): target sample rate for synthesized audio, 0 means the same as the model sampling rate. Defaults to 0. save_path (str, optional): The save path of the synthesized audio. None means do not save audio. Defaults to None. 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 duration = len(self.executor._outputs['wav'] .numpy()) / self.executor.am_config.fs rtf = infer_time / duration 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_config.fs, target_fs=sample_rate, volume=volume, speed=speed, audio_path=save_path) postprocess_et = time.time() postprocess_time = postprocess_et - postprocess_st 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: python") 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)) logger.info("device: {}".format(self.device)) return lang, target_sample_rate, wav_base64