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280 lines
10 KiB
280 lines
10 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import base64
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import io
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import time
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import librosa
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import numpy as np
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import paddle
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import soundfile as sf
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from scipy.io import wavfile
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from paddlespeech.cli.log import logger
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from paddlespeech.cli.tts.infer import TTSExecutor
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from paddlespeech.server.engine.base_engine import BaseEngine
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from paddlespeech.server.utils.audio_process import change_speed
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from paddlespeech.server.utils.errors import ErrorCode
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from paddlespeech.server.utils.exception import ServerBaseException
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__all__ = ['TTSEngine']
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class TTSServerExecutor(TTSExecutor):
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def __init__(self):
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super().__init__()
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pass
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class TTSEngine(BaseEngine):
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"""TTS server engine
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Args:
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metaclass: Defaults to Singleton.
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"""
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def __init__(self, name=None):
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"""Initialize TTS server engine
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"""
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super(TTSEngine, self).__init__()
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def init(self, config: dict) -> bool:
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self.executor = TTSServerExecutor()
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self.config = config
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try:
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if self.config.device is not None:
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self.device = self.config.device
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else:
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self.device = paddle.get_device()
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paddle.set_device(self.device)
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except BaseException as e:
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logger.error(
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"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
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)
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logger.error("Initialize TTS server engine Failed on device: %s." %
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(self.device))
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return False
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try:
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self.executor._init_from_path(
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am=self.config.am,
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am_config=self.config.am_config,
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am_ckpt=self.config.am_ckpt,
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am_stat=self.config.am_stat,
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phones_dict=self.config.phones_dict,
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tones_dict=self.config.tones_dict,
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speaker_dict=self.config.speaker_dict,
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voc=self.config.voc,
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voc_config=self.config.voc_config,
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voc_ckpt=self.config.voc_ckpt,
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voc_stat=self.config.voc_stat,
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lang=self.config.lang)
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except BaseException:
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logger.error("Failed to get model related files.")
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logger.error("Initialize TTS server engine Failed on device: %s." %
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(self.device))
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return False
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# warm up
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try:
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self.warm_up()
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logger.info("Warm up successfully.")
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except Exception as e:
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logger.error("Failed to warm up on tts engine.")
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return False
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logger.info("Initialize TTS server engine successfully on device: %s." %
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(self.device))
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return True
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def warm_up(self):
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"""warm up
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"""
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if self.config.lang == 'zh':
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sentence = "您好，欢迎使用语音合成服务。"
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if self.config.lang == 'en':
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sentence = "Hello and welcome to the speech synthesis service."
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logger.info("Start to warm up.")
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for i in range(3):
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st = time.time()
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self.executor.infer(
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text=sentence,
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lang=self.config.lang,
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am=self.config.am,
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spk_id=0, )
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logger.info(
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f"The response time of the {i} warm up: {time.time() - st} s")
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def postprocess(self,
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wav,
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original_fs: int,
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target_fs: int=0,
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volume: float=1.0,
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speed: float=1.0,
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audio_path: str=None):
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"""Post-processing operations, including speech, volume, sample rate, save audio file
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Args:
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wav (numpy(float)): Synthesized audio sample points
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original_fs (int): original audio sample rate
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target_fs (int): target audio sample rate
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volume (float): target volume
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speed (float): target speed
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Raises:
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ServerBaseException: Throws an exception if the change speed unsuccessfully.
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Returns:
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target_fs: target sample rate for synthesized audio.
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wav_base64: The base64 format of the synthesized audio.
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"""
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# transform sample_rate
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if target_fs == 0 or target_fs > original_fs:
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target_fs = original_fs
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wav_tar_fs = wav
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logger.info(
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"The sample rate of synthesized audio is the same as model, which is {}Hz".
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format(original_fs))
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else:
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wav_tar_fs = librosa.resample(
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np.squeeze(wav), original_fs, target_fs)
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logger.info(
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"The sample rate of model is {}Hz and the target sample rate is {}Hz. Converting the sample rate of the synthesized audio successfully.".
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format(original_fs, target_fs))
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# transform volume
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wav_vol = wav_tar_fs * volume
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logger.info("Transform the volume of the audio successfully.")
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# transform speed
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try: # windows not support soxbindings
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wav_speed = change_speed(wav_vol, speed, target_fs)
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logger.info("Transform the speed of the audio successfully.")
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except ServerBaseException:
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raise ServerBaseException(
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ErrorCode.SERVER_INTERNAL_ERR,
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"Failed to transform speed. Can not install soxbindings on your system. \
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You need to set speed value 1.0.")
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except BaseException:
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logger.error("Failed to transform speed.")
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# wav to base64
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buf = io.BytesIO()
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wavfile.write(buf, target_fs, wav_speed)
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base64_bytes = base64.b64encode(buf.read())
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wav_base64 = base64_bytes.decode('utf-8')
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logger.info("Audio to string successfully.")
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# save audio
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if audio_path is not None:
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if audio_path.endswith(".wav"):
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sf.write(audio_path, wav_speed, target_fs)
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elif audio_path.endswith(".pcm"):
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wav_norm = wav_speed * (32767 / max(0.001,
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np.max(np.abs(wav_speed))))
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with open(audio_path, "wb") as f:
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f.write(wav_norm.astype(np.int16))
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logger.info("Save audio to {} successfully.".format(audio_path))
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else:
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logger.info("There is no need to save audio.")
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return target_fs, wav_base64
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def run(self,
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sentence: str,
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spk_id: int=0,
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speed: float=1.0,
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volume: float=1.0,
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sample_rate: int=0,
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save_path: str=None):
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""" run include inference and postprocess.
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Args:
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sentence (str): text to be synthesized
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spk_id (int, optional): speaker id for multi-speaker speech synthesis. Defaults to 0.
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speed (float, optional): speed. Defaults to 1.0.
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volume (float, optional): volume. Defaults to 1.0.
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sample_rate (int, optional): target sample rate for synthesized audio,
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0 means the same as the model sampling rate. Defaults to 0.
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save_path (str, optional): The save path of the synthesized audio.
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None means do not save audio. Defaults to None.
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Raises:
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ServerBaseException: Throws an exception if tts inference unsuccessfully.
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ServerBaseException: Throws an exception if postprocess unsuccessfully.
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Returns:
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lang: model language
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target_sample_rate: target sample rate for synthesized audio.
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wav_base64: The base64 format of the synthesized audio.
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"""
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lang = self.config.lang
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try:
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infer_st = time.time()
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self.executor.infer(
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text=sentence, lang=lang, am=self.config.am, spk_id=spk_id)
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infer_et = time.time()
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infer_time = infer_et - infer_st
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duration = len(self.executor._outputs['wav']
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.numpy()) / self.executor.am_config.fs
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rtf = infer_time / duration
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except ServerBaseException:
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raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR,
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"tts infer failed.")
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except BaseException:
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logger.error("tts infer failed.")
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try:
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postprocess_st = time.time()
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target_sample_rate, wav_base64 = self.postprocess(
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wav=self.executor._outputs['wav'].numpy(),
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original_fs=self.executor.am_config.fs,
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target_fs=sample_rate,
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volume=volume,
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speed=speed,
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audio_path=save_path)
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postprocess_et = time.time()
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postprocess_time = postprocess_et - postprocess_st
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except ServerBaseException:
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raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR,
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"tts postprocess failed.")
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except BaseException:
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logger.error("tts postprocess failed.")
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logger.info("AM model: {}".format(self.config.am))
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logger.info("Vocoder model: {}".format(self.config.voc))
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logger.info("Language: {}".format(lang))
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logger.info("tts engine type: python")
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logger.info("audio duration: {}".format(duration))
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logger.info(
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"frontend inference time: {}".format(self.executor.frontend_time))
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logger.info("AM inference time: {}".format(self.executor.am_time))
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logger.info("Vocoder inference time: {}".format(self.executor.voc_time))
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logger.info("total inference time: {}".format(infer_time))
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logger.info(
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"postprocess (change speed, volume, target sample rate) time: {}".
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format(postprocess_time))
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logger.info("total generate audio time: {}".format(infer_time +
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postprocess_time))
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logger.info("RTF: {}".format(rtf))
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logger.info("device: {}".format(self.device))
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return lang, target_sample_rate, duration, wav_base64
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