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487 lines
19 KiB
487 lines
19 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 os
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import time
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from typing import Optional
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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 .pretrained_models import pretrained_models
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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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from paddlespeech.server.utils.paddle_predictor import init_predictor
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from paddlespeech.server.utils.paddle_predictor import run_model
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from paddlespeech.t2s.frontend import English
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from paddlespeech.t2s.frontend.zh_frontend import Frontend
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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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self.pretrained_models = pretrained_models
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def _init_from_path(
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self,
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am: str='fastspeech2_csmsc',
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am_model: Optional[os.PathLike]=None,
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am_params: Optional[os.PathLike]=None,
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am_sample_rate: int=24000,
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phones_dict: Optional[os.PathLike]=None,
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tones_dict: Optional[os.PathLike]=None,
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speaker_dict: Optional[os.PathLike]=None,
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voc: str='pwgan_csmsc',
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voc_model: Optional[os.PathLike]=None,
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voc_params: Optional[os.PathLike]=None,
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voc_sample_rate: int=24000,
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lang: str='zh',
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am_predictor_conf: dict=None,
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voc_predictor_conf: dict=None, ):
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"""
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Init model and other resources from a specific path.
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"""
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if hasattr(self, 'am_predictor') and hasattr(self, 'voc_predictor'):
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logger.info('Models had been initialized.')
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return
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# am
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am_tag = am + '-' + lang
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if am_model is None or am_params is None or phones_dict is None:
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am_res_path = self._get_pretrained_path(am_tag)
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self.am_res_path = am_res_path
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self.am_model = os.path.join(
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am_res_path, self.pretrained_models[am_tag]['model'])
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self.am_params = os.path.join(
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am_res_path, self.pretrained_models[am_tag]['params'])
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# must have phones_dict in acoustic
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self.phones_dict = os.path.join(
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am_res_path, self.pretrained_models[am_tag]['phones_dict'])
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self.am_sample_rate = self.pretrained_models[am_tag]['sample_rate']
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logger.info(am_res_path)
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logger.info(self.am_model)
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logger.info(self.am_params)
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else:
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self.am_model = os.path.abspath(am_model)
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self.am_params = os.path.abspath(am_params)
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self.phones_dict = os.path.abspath(phones_dict)
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self.am_sample_rate = am_sample_rate
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self.am_res_path = os.path.dirname(os.path.abspath(self.am_model))
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logger.info("self.phones_dict: {}".format(self.phones_dict))
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# for speedyspeech
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self.tones_dict = None
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if 'tones_dict' in self.pretrained_models[am_tag]:
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self.tones_dict = os.path.join(
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am_res_path, self.pretrained_models[am_tag]['tones_dict'])
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if tones_dict:
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self.tones_dict = tones_dict
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# for multi speaker fastspeech2
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self.speaker_dict = None
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if 'speaker_dict' in self.pretrained_models[am_tag]:
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self.speaker_dict = os.path.join(
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am_res_path, self.pretrained_models[am_tag]['speaker_dict'])
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if speaker_dict:
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self.speaker_dict = speaker_dict
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# voc
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voc_tag = voc + '-' + lang
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if voc_model is None or voc_params is None:
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voc_res_path = self._get_pretrained_path(voc_tag)
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self.voc_res_path = voc_res_path
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self.voc_model = os.path.join(
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voc_res_path, self.pretrained_models[voc_tag]['model'])
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self.voc_params = os.path.join(
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voc_res_path, self.pretrained_models[voc_tag]['params'])
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self.voc_sample_rate = self.pretrained_models[voc_tag][
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'sample_rate']
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logger.info(voc_res_path)
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logger.info(self.voc_model)
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logger.info(self.voc_params)
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else:
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self.voc_model = os.path.abspath(voc_model)
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self.voc_params = os.path.abspath(voc_params)
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self.voc_sample_rate = voc_sample_rate
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self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_model))
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assert (
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self.voc_sample_rate == self.am_sample_rate
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), "The sample rate of AM and Vocoder model are different, please check model."
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# Init body.
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with open(self.phones_dict, "r") as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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logger.info("vocab_size: {}".format(vocab_size))
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tone_size = None
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if self.tones_dict:
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with open(self.tones_dict, "r") as f:
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tone_id = [line.strip().split() for line in f.readlines()]
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tone_size = len(tone_id)
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logger.info("tone_size: {}".format(tone_size))
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spk_num = None
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if self.speaker_dict:
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with open(self.speaker_dict, 'rt') as f:
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spk_id = [line.strip().split() for line in f.readlines()]
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spk_num = len(spk_id)
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logger.info("spk_num: {}".format(spk_num))
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# frontend
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if lang == 'zh':
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self.frontend = Frontend(
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phone_vocab_path=self.phones_dict,
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tone_vocab_path=self.tones_dict)
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elif lang == 'en':
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self.frontend = English(phone_vocab_path=self.phones_dict)
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logger.info("frontend done!")
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# Create am predictor
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self.am_predictor_conf = am_predictor_conf
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self.am_predictor = init_predictor(
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model_file=self.am_model,
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params_file=self.am_params,
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predictor_conf=self.am_predictor_conf)
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logger.info("Create AM predictor successfully.")
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# Create voc predictor
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self.voc_predictor_conf = voc_predictor_conf
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self.voc_predictor = init_predictor(
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model_file=self.voc_model,
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params_file=self.voc_params,
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predictor_conf=self.voc_predictor_conf)
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logger.info("Create Vocoder predictor successfully.")
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@paddle.no_grad()
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def infer(self,
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text: str,
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lang: str='zh',
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am: str='fastspeech2_csmsc',
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spk_id: int=0):
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"""
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Model inference and result stored in self.output.
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"""
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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get_tone_ids = False
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merge_sentences = False
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frontend_st = time.time()
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if am_name == 'speedyspeech':
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get_tone_ids = True
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if lang == 'zh':
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input_ids = self.frontend.get_input_ids(
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text,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids)
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phone_ids = input_ids["phone_ids"]
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if get_tone_ids:
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tone_ids = input_ids["tone_ids"]
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elif lang == 'en':
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input_ids = self.frontend.get_input_ids(
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text, merge_sentences=merge_sentences)
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phone_ids = input_ids["phone_ids"]
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else:
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logger.error("lang should in {'zh', 'en'}!")
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self.frontend_time = time.time() - frontend_st
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self.am_time = 0
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self.voc_time = 0
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flags = 0
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for i in range(len(phone_ids)):
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am_st = time.time()
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part_phone_ids = phone_ids[i]
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# am
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if am_name == 'speedyspeech':
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part_tone_ids = tone_ids[i]
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am_result = run_model(
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self.am_predictor,
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[part_phone_ids.numpy(), part_tone_ids.numpy()])
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mel = am_result[0]
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# fastspeech2
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else:
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# multi speaker do not have static model
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if am_dataset in {"aishell3", "vctk"}:
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pass
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else:
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am_result = run_model(self.am_predictor,
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[part_phone_ids.numpy()])
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mel = am_result[0]
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self.am_time += (time.time() - am_st)
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# voc
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voc_st = time.time()
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voc_result = run_model(self.voc_predictor, [mel])
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wav = voc_result[0]
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wav = paddle.to_tensor(wav)
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if flags == 0:
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wav_all = wav
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flags = 1
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else:
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wav_all = paddle.concat([wav_all, wav])
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self.voc_time += (time.time() - voc_st)
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self._outputs['wav'] = wav_all
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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):
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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.am_predictor_conf.device is not None:
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self.device = self.config.am_predictor_conf.device
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elif self.config.voc_predictor_conf.device is not None:
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self.device = self.config.voc_predictor_conf.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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self.executor._init_from_path(
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am=self.config.am,
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am_model=self.config.am_model,
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am_params=self.config.am_params,
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am_sample_rate=self.config.am_sample_rate,
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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_model=self.config.voc_model,
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voc_params=self.config.voc_params,
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voc_sample_rate=self.config.voc_sample_rate,
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lang=self.config.lang,
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am_predictor_conf=self.config.am_predictor_conf,
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voc_predictor_conf=self.config.voc_predictor_conf, )
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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.")
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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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"""get the result of the server response
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Args:
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sentence (str): sentence to be synthesized
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spk_id (int, optional): speaker id. Defaults to 0.
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speed (float, optional): audio speed, 0 < speed <=3.0. Defaults to 1.0.
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volume (float, optional): The volume relative to the audio synthesized by the model,
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0 < volume <=3.0. Defaults to 1.0.
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sample_rate (int, optional): Set the sample rate of the synthesized audio.
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0 represents the sample rate for model synthesis. Defaults to 0.
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save_path (str, optional): The save path of the synthesized 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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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_sample_rate,
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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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duration = len(self.executor._outputs['wav']
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.numpy()) / self.executor.am_sample_rate
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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 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: paddle inference")
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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))
|
|
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, duration, wav_base64
|