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192 lines
6.1 KiB
192 lines
6.1 KiB
# Copyright (c) 2022 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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from pathlib import Path
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from typing import Union
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import yaml
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from local.check_oov import get_check_result
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from local.extract import extract_feature
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from local.label_process import get_single_label
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from local.prepare_env import generate_finetune_env
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from paddle import distributed as dist
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from yacs.config import CfgNode
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from paddlespeech.t2s.exps.fastspeech2.train import train_sp
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from utils.gen_duration_from_textgrid import gen_duration_from_textgrid
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DICT_EN = 'tools/aligner/cmudict-0.7b'
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DICT_ZH = 'tools/aligner/simple.lexicon'
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MODEL_DIR_EN = 'tools/aligner/vctk_model.zip'
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MODEL_DIR_ZH = 'tools/aligner/aishell3_model.zip'
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MFA_PHONE_EN = 'tools/aligner/vctk_model/meta.yaml'
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MFA_PHONE_ZH = 'tools/aligner/aishell3_model/meta.yaml'
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MFA_PATH = 'tools/montreal-forced-aligner/bin'
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os.environ['PATH'] = MFA_PATH + '/:' + os.environ['PATH']
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class TrainArgs():
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def __init__(self, ngpu, config_file, dump_dir: Path, output_dir: Path):
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self.config = str(config_file)
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self.train_metadata = str(dump_dir / "train/norm/metadata.jsonl")
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self.dev_metadata = str(dump_dir / "dev/norm/metadata.jsonl")
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self.output_dir = str(output_dir)
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self.ngpu = ngpu
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self.phones_dict = str(dump_dir / "phone_id_map.txt")
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self.speaker_dict = str(dump_dir / "speaker_id_map.txt")
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self.voice_cloning = False
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def get_mfa_result(
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input_dir: Union[str, Path],
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mfa_dir: Union[str, Path],
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lang: str='en', ):
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"""get mfa result
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Args:
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input_dir (Union[str, Path]): input dir including wav file and label
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mfa_dir (Union[str, Path]): mfa result dir
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lang (str, optional): input audio language. Defaults to 'en'.
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"""
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# MFA
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if lang == 'en':
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DICT = DICT_EN
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MODEL_DIR = MODEL_DIR_EN
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elif lang == 'zh':
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DICT = DICT_ZH
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MODEL_DIR = MODEL_DIR_ZH
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else:
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print('please input right lang!!')
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CMD = 'mfa_align' + ' ' + str(
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input_dir) + ' ' + DICT + ' ' + MODEL_DIR + ' ' + str(mfa_dir)
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os.system(CMD)
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if __name__ == '__main__':
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# parse config and args
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parser = argparse.ArgumentParser(
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description="Preprocess audio and then extract features.")
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parser.add_argument(
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"--input_dir",
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type=str,
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default="./input/baker_mini",
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help="directory containing audio and label file")
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parser.add_argument(
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"--pretrained_model_dir",
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type=str,
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default="./pretrained_models/fastspeech2_aishell3_ckpt_1.1.0",
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help="Path to pretrained model")
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parser.add_argument(
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"--mfa_dir",
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type=str,
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default="./mfa_result",
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help="directory to save aligned files")
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parser.add_argument(
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"--dump_dir",
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type=str,
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default="./dump",
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help="directory to save feature files and metadata.")
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parser.add_argument(
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"--output_dir",
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type=str,
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default="./exp/default/",
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help="directory to save finetune model.")
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parser.add_argument(
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'--lang',
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type=str,
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default='zh',
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choices=['zh', 'en'],
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help='Choose input audio language. zh or en')
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parser.add_argument(
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"--ngpu", type=int, default=2, help="if ngpu=0, use cpu.")
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parser.add_argument("--epoch", type=int, default=100, help="finetune epoch")
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parser.add_argument(
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"--batch_size",
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type=int,
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default=-1,
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help="batch size, default -1 means same as pretrained model")
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args = parser.parse_args()
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fs = 24000
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n_shift = 300
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input_dir = Path(args.input_dir).expanduser()
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mfa_dir = Path(args.mfa_dir).expanduser()
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mfa_dir.mkdir(parents=True, exist_ok=True)
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dump_dir = Path(args.dump_dir).expanduser()
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dump_dir.mkdir(parents=True, exist_ok=True)
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output_dir = Path(args.output_dir).expanduser()
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output_dir.mkdir(parents=True, exist_ok=True)
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pretrained_model_dir = Path(args.pretrained_model_dir).expanduser()
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# read config
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config_file = pretrained_model_dir / "default.yaml"
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with open(config_file) as f:
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config = CfgNode(yaml.safe_load(f))
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config.max_epoch = config.max_epoch + args.epoch
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if args.batch_size > 0:
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config.batch_size = args.batch_size
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if args.lang == 'en':
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lexicon_file = DICT_EN
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mfa_phone_file = MFA_PHONE_EN
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elif args.lang == 'zh':
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lexicon_file = DICT_ZH
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mfa_phone_file = MFA_PHONE_ZH
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else:
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print('please input right lang!!')
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am_phone_file = pretrained_model_dir / "phone_id_map.txt"
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label_file = input_dir / "labels.txt"
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#check phone for mfa and am finetune
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oov_words, oov_files, oov_file_words = get_check_result(
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label_file, lexicon_file, mfa_phone_file, am_phone_file)
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input_dir = get_single_label(label_file, oov_files, input_dir)
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# get mfa result
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get_mfa_result(input_dir, mfa_dir, args.lang)
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# # generate durations.txt
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duration_file = "./durations.txt"
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gen_duration_from_textgrid(mfa_dir, duration_file, fs, n_shift)
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# generate phone and speaker map files
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extract_feature(duration_file, config, input_dir, dump_dir,
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pretrained_model_dir)
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# create finetune env
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generate_finetune_env(output_dir, pretrained_model_dir)
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# create a new args for training
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train_args = TrainArgs(args.ngpu, config_file, dump_dir, output_dir)
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# finetune models
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# dispatch
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if args.ngpu > 1:
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dist.spawn(train_sp, (train_args, config), nprocs=args.ngpu)
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else:
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train_sp(train_args, config)
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