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536 lines
19 KiB
536 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 argparse
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import os
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import time
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from collections import OrderedDict
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from typing import Any
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from typing import List
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from typing import Optional
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from typing import Union
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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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import yaml
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from yacs.config import CfgNode
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from ..executor import BaseExecutor
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from ..log import logger
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from ..utils import cli_register
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from ..utils import stats_wrapper
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from .pretrained_models import model_alias
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from .pretrained_models import pretrained_models
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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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from paddlespeech.t2s.modules.normalizer import ZScore
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from paddlespeech.utils.dynamic_import import dynamic_import
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__all__ = ['TTSExecutor']
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@cli_register(
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name='paddlespeech.tts', description='Text to Speech infer command.')
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class TTSExecutor(BaseExecutor):
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def __init__(self):
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super().__init__()
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self.model_alias = model_alias
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self.pretrained_models = pretrained_models
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self.parser = argparse.ArgumentParser(
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prog='paddlespeech.tts', add_help=True)
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self.parser.add_argument(
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'--input', type=str, default=None, help='Input text to generate.')
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# acoustic model
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self.parser.add_argument(
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'--am',
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type=str,
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default='fastspeech2_csmsc',
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choices=[
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'speedyspeech_csmsc',
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'fastspeech2_csmsc',
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'fastspeech2_ljspeech',
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'fastspeech2_aishell3',
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'fastspeech2_vctk',
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'tacotron2_csmsc',
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'tacotron2_ljspeech',
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],
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help='Choose acoustic model type of tts task.')
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self.parser.add_argument(
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'--am_config',
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type=str,
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default=None,
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help='Config of acoustic model. Use deault config when it is None.')
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self.parser.add_argument(
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'--am_ckpt',
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type=str,
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default=None,
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help='Checkpoint file of acoustic model.')
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self.parser.add_argument(
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"--am_stat",
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type=str,
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default=None,
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help="mean and standard deviation used to normalize spectrogram when training acoustic model."
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)
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self.parser.add_argument(
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"--phones_dict",
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type=str,
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default=None,
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help="phone vocabulary file.")
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self.parser.add_argument(
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"--tones_dict",
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type=str,
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default=None,
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help="tone vocabulary file.")
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self.parser.add_argument(
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"--speaker_dict",
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type=str,
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default=None,
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help="speaker id map file.")
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self.parser.add_argument(
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'--spk_id',
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type=int,
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default=0,
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help='spk id for multi speaker acoustic model')
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# vocoder
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self.parser.add_argument(
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'--voc',
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type=str,
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default='pwgan_csmsc',
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choices=[
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'pwgan_csmsc',
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'pwgan_ljspeech',
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'pwgan_aishell3',
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'pwgan_vctk',
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'mb_melgan_csmsc',
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'style_melgan_csmsc',
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'hifigan_csmsc',
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'hifigan_ljspeech',
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'hifigan_aishell3',
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'hifigan_vctk',
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'wavernn_csmsc',
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],
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help='Choose vocoder type of tts task.')
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self.parser.add_argument(
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'--voc_config',
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type=str,
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default=None,
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help='Config of voc. Use deault config when it is None.')
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self.parser.add_argument(
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'--voc_ckpt',
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type=str,
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default=None,
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help='Checkpoint file of voc.')
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self.parser.add_argument(
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"--voc_stat",
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type=str,
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default=None,
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help="mean and standard deviation used to normalize spectrogram when training voc."
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)
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# other
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self.parser.add_argument(
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'--lang',
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type=str,
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default='zh',
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help='Choose model language. zh or en')
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self.parser.add_argument(
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'--device',
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type=str,
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default=paddle.get_device(),
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help='Choose device to execute model inference.')
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self.parser.add_argument(
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'--output', type=str, default='output.wav', help='output file name')
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self.parser.add_argument(
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'-d',
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'--job_dump_result',
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action='store_true',
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help='Save job result into file.')
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self.parser.add_argument(
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'-v',
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'--verbose',
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action='store_true',
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help='Increase logger verbosity of current task.')
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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_config: Optional[os.PathLike]=None,
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am_ckpt: Optional[os.PathLike]=None,
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am_stat: Optional[os.PathLike]=None,
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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_config: Optional[os.PathLike]=None,
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voc_ckpt: Optional[os.PathLike]=None,
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voc_stat: Optional[os.PathLike]=None,
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lang: str='zh', ):
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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_inference') and hasattr(self, 'voc_inference'):
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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_ckpt is None or am_config is None or am_stat 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_config = os.path.join(
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am_res_path, self.pretrained_models[am_tag]['config'])
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self.am_ckpt = os.path.join(am_res_path,
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self.pretrained_models[am_tag]['ckpt'])
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self.am_stat = os.path.join(
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am_res_path, self.pretrained_models[am_tag]['speech_stats'])
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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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logger.info(am_res_path)
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logger.info(self.am_config)
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logger.info(self.am_ckpt)
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else:
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self.am_config = os.path.abspath(am_config)
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self.am_ckpt = os.path.abspath(am_ckpt)
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self.am_stat = os.path.abspath(am_stat)
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self.phones_dict = os.path.abspath(phones_dict)
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self.am_res_path = os.path.dirname(os.path.abspath(self.am_config))
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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_ckpt is None or voc_config is None or voc_stat 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_config = os.path.join(
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voc_res_path, self.pretrained_models[voc_tag]['config'])
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self.voc_ckpt = os.path.join(
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voc_res_path, self.pretrained_models[voc_tag]['ckpt'])
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self.voc_stat = os.path.join(
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voc_res_path, self.pretrained_models[voc_tag]['speech_stats'])
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logger.info(voc_res_path)
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logger.info(self.voc_config)
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logger.info(self.voc_ckpt)
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else:
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self.voc_config = os.path.abspath(voc_config)
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self.voc_ckpt = os.path.abspath(voc_ckpt)
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self.voc_stat = os.path.abspath(voc_stat)
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self.voc_res_path = os.path.dirname(
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os.path.abspath(self.voc_config))
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# Init body.
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with open(self.am_config) as f:
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self.am_config = CfgNode(yaml.safe_load(f))
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with open(self.voc_config) as f:
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self.voc_config = CfgNode(yaml.safe_load(f))
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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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print("vocab_size:", 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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print("tone_size:", 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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print("spk_num:", 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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print("frontend done!")
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# acoustic model
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odim = self.am_config.n_mels
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# model: {model_name}_{dataset}
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am_name = am[:am.rindex('_')]
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am_class = dynamic_import(am_name, self.model_alias)
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am_inference_class = dynamic_import(am_name + '_inference',
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self.model_alias)
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if am_name == 'fastspeech2':
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am = am_class(
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idim=vocab_size,
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odim=odim,
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spk_num=spk_num,
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**self.am_config["model"])
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elif am_name == 'speedyspeech':
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am = am_class(
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vocab_size=vocab_size,
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tone_size=tone_size,
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**self.am_config["model"])
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elif am_name == 'tacotron2':
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am = am_class(idim=vocab_size, odim=odim, **self.am_config["model"])
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am.set_state_dict(paddle.load(self.am_ckpt)["main_params"])
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am.eval()
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am_mu, am_std = np.load(self.am_stat)
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am_mu = paddle.to_tensor(am_mu)
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am_std = paddle.to_tensor(am_std)
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am_normalizer = ZScore(am_mu, am_std)
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self.am_inference = am_inference_class(am_normalizer, am)
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self.am_inference.eval()
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print("acoustic model done!")
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# vocoder
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# model: {model_name}_{dataset}
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voc_name = voc[:voc.rindex('_')]
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voc_class = dynamic_import(voc_name, self.model_alias)
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voc_inference_class = dynamic_import(voc_name + '_inference',
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self.model_alias)
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if voc_name != 'wavernn':
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voc = voc_class(**self.voc_config["generator_params"])
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voc.set_state_dict(paddle.load(self.voc_ckpt)["generator_params"])
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voc.remove_weight_norm()
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voc.eval()
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else:
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voc = voc_class(**self.voc_config["model"])
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voc.set_state_dict(paddle.load(self.voc_ckpt)["main_params"])
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voc.eval()
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voc_mu, voc_std = np.load(self.voc_stat)
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voc_mu = paddle.to_tensor(voc_mu)
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voc_std = paddle.to_tensor(voc_std)
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voc_normalizer = ZScore(voc_mu, voc_std)
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self.voc_inference = voc_inference_class(voc_normalizer, voc)
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self.voc_inference.eval()
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print("voc done!")
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def preprocess(self, input: Any, *args, **kwargs):
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"""
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Input preprocess and return paddle.Tensor stored in self._inputs.
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Input content can be a text(tts), a file(asr, cls), a stream(not supported yet) or anything needed.
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Args:
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input (Any): Input text/file/stream or other content.
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"""
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pass
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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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print("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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mel = self.am_inference(part_phone_ids, part_tone_ids)
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# fastspeech2
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else:
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# multi speaker
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if am_dataset in {"aishell3", "vctk"}:
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mel = self.am_inference(
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part_phone_ids, spk_id=paddle.to_tensor(spk_id))
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else:
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mel = self.am_inference(part_phone_ids)
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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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wav = self.voc_inference(mel)
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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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def postprocess(self, output: str='output.wav') -> Union[str, os.PathLike]:
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"""
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Output postprocess and return results.
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This method get model output from self._outputs and convert it into human-readable results.
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Returns:
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Union[str, os.PathLike]: Human-readable results such as texts and audio files.
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"""
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output = os.path.abspath(os.path.expanduser(output))
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sf.write(
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output, self._outputs['wav'].numpy(), samplerate=self.am_config.fs)
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return output
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def execute(self, argv: List[str]) -> bool:
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"""
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Command line entry.
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"""
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args = self.parser.parse_args(argv)
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am = args.am
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am_config = args.am_config
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am_ckpt = args.am_ckpt
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am_stat = args.am_stat
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phones_dict = args.phones_dict
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tones_dict = args.tones_dict
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speaker_dict = args.speaker_dict
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voc = args.voc
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voc_config = args.voc_config
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voc_ckpt = args.voc_ckpt
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voc_stat = args.voc_stat
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lang = args.lang
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device = args.device
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spk_id = args.spk_id
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if not args.verbose:
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self.disable_task_loggers()
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task_source = self.get_task_source(args.input)
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task_results = OrderedDict()
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has_exceptions = False
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for id_, input_ in task_source.items():
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if len(task_source) > 1:
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assert isinstance(args.output,
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str) and args.output.endswith('.wav')
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output = args.output.replace('.wav', f'_{id_}.wav')
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else:
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output = args.output
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try:
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res = self(
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text=input_,
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# acoustic model related
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am=am,
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am_config=am_config,
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am_ckpt=am_ckpt,
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am_stat=am_stat,
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phones_dict=phones_dict,
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tones_dict=tones_dict,
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speaker_dict=speaker_dict,
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spk_id=spk_id,
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# vocoder related
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voc=voc,
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voc_config=voc_config,
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voc_ckpt=voc_ckpt,
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voc_stat=voc_stat,
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# other
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lang=lang,
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|
device=device,
|
|
output=output)
|
|
task_results[id_] = res
|
|
except Exception as e:
|
|
has_exceptions = True
|
|
task_results[id_] = f'{e.__class__.__name__}: {e}'
|
|
|
|
self.process_task_results(args.input, task_results,
|
|
args.job_dump_result)
|
|
|
|
if has_exceptions:
|
|
return False
|
|
else:
|
|
return True
|
|
|
|
@stats_wrapper
|
|
def __call__(self,
|
|
text: str,
|
|
am: str='fastspeech2_csmsc',
|
|
am_config: Optional[os.PathLike]=None,
|
|
am_ckpt: Optional[os.PathLike]=None,
|
|
am_stat: Optional[os.PathLike]=None,
|
|
spk_id: int=0,
|
|
phones_dict: Optional[os.PathLike]=None,
|
|
tones_dict: Optional[os.PathLike]=None,
|
|
speaker_dict: Optional[os.PathLike]=None,
|
|
voc: str='pwgan_csmsc',
|
|
voc_config: Optional[os.PathLike]=None,
|
|
voc_ckpt: Optional[os.PathLike]=None,
|
|
voc_stat: Optional[os.PathLike]=None,
|
|
lang: str='zh',
|
|
device: str=paddle.get_device(),
|
|
output: str='output.wav'):
|
|
"""
|
|
Python API to call an executor.
|
|
"""
|
|
paddle.set_device(device)
|
|
self._init_from_path(
|
|
am=am,
|
|
am_config=am_config,
|
|
am_ckpt=am_ckpt,
|
|
am_stat=am_stat,
|
|
phones_dict=phones_dict,
|
|
tones_dict=tones_dict,
|
|
speaker_dict=speaker_dict,
|
|
voc=voc,
|
|
voc_config=voc_config,
|
|
voc_ckpt=voc_ckpt,
|
|
voc_stat=voc_stat,
|
|
lang=lang)
|
|
|
|
self.infer(text=text, lang=lang, am=am, spk_id=spk_id)
|
|
|
|
res = self.postprocess(output=output)
|
|
|
|
return res
|