# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os from pathlib import Path from typing import Union import yaml from paddle import distributed as dist from yacs.config import CfgNode from paddlespeech.t2s.exps.fastspeech2.train import train_sp from local.check_oov import get_check_result from local.extract import extract_feature from local.label_process import get_single_label from local.prepare_env import generate_finetune_env from utils.gen_duration_from_textgrid import gen_duration_from_textgrid DICT_EN = 'tools/aligner/cmudict-0.7b' DICT_ZH = 'tools/aligner/simple.lexicon' MODEL_DIR_EN = 'tools/aligner/vctk_model.zip' MODEL_DIR_ZH = 'tools/aligner/aishell3_model.zip' MFA_PHONE_EN = 'tools/aligner/vctk_model/meta.yaml' MFA_PHONE_ZH = 'tools/aligner/aishell3_model/meta.yaml' MFA_PATH = 'tools/montreal-forced-aligner/bin' os.environ['PATH'] = MFA_PATH + '/:' + os.environ['PATH'] class TrainArgs(): def __init__(self, ngpu, config_file, dump_dir: Path, output_dir: Path): self.config = str(config_file) self.train_metadata = str(dump_dir / "train/norm/metadata.jsonl") self.dev_metadata = str(dump_dir / "dev/norm/metadata.jsonl") self.output_dir = str(output_dir) self.ngpu = ngpu self.phones_dict = str(dump_dir / "phone_id_map.txt") self.speaker_dict = str(dump_dir / "speaker_id_map.txt") self.voice_cloning = False def get_mfa_result( input_dir: Union[str, Path], mfa_dir: Union[str, Path], lang: str='en', ): """get mfa result Args: input_dir (Union[str, Path]): input dir including wav file and label mfa_dir (Union[str, Path]): mfa result dir lang (str, optional): input audio language. Defaults to 'en'. """ # MFA if lang == 'en': DICT = DICT_EN MODEL_DIR = MODEL_DIR_EN elif lang == 'zh': DICT = DICT_ZH MODEL_DIR = MODEL_DIR_ZH else: print('please input right lang!!') CMD = 'mfa_align' + ' ' + str( input_dir) + ' ' + DICT + ' ' + MODEL_DIR + ' ' + str(mfa_dir) os.system(CMD) if __name__ == '__main__': # parse config and args parser = argparse.ArgumentParser( description="Preprocess audio and then extract features.") parser.add_argument( "--input_dir", type=str, default="./input/baker_mini", help="directory containing audio and label file") parser.add_argument( "--pretrained_model_dir", type=str, default="./pretrained_models/fastspeech2_aishell3_ckpt_1.1.0", help="Path to pretrained model") parser.add_argument( "--mfa_dir", type=str, default="./mfa_result", help="directory to save aligned files") parser.add_argument( "--dump_dir", type=str, default="./dump", help="directory to save feature files and metadata.") parser.add_argument( "--output_dir", type=str, default="./exp/default/", help="directory to save finetune model.") parser.add_argument( '--lang', type=str, default='zh', choices=['zh', 'en'], help='Choose input audio language. zh or en') parser.add_argument( "--ngpu", type=int, default=2, help="if ngpu=0, use cpu.") parser.add_argument("--epoch", type=int, default=100, help="finetune epoch") parser.add_argument( "--batch_size", type=int, default=-1, help="batch size, default -1 means same as pretrained model") args = parser.parse_args() fs = 24000 n_shift = 300 input_dir = Path(args.input_dir).expanduser() mfa_dir = Path(args.mfa_dir).expanduser() mfa_dir.mkdir(parents=True, exist_ok=True) dump_dir = Path(args.dump_dir).expanduser() dump_dir.mkdir(parents=True, exist_ok=True) output_dir = Path(args.output_dir).expanduser() output_dir.mkdir(parents=True, exist_ok=True) pretrained_model_dir = Path(args.pretrained_model_dir).expanduser() # read config config_file = pretrained_model_dir / "default.yaml" with open(config_file) as f: config = CfgNode(yaml.safe_load(f)) config.max_epoch = config.max_epoch + args.epoch if args.batch_size > 0: config.batch_size = args.batch_size if args.lang == 'en': lexicon_file = DICT_EN mfa_phone_file = MFA_PHONE_EN elif args.lang == 'zh': lexicon_file = DICT_ZH mfa_phone_file = MFA_PHONE_ZH else: print('please input right lang!!') am_phone_file = pretrained_model_dir / "phone_id_map.txt" label_file = input_dir / "labels.txt" #check phone for mfa and am finetune oov_words, oov_files, oov_file_words = get_check_result( label_file, lexicon_file, mfa_phone_file, am_phone_file) input_dir = get_single_label(label_file, oov_files, input_dir) # get mfa result get_mfa_result(input_dir, mfa_dir, args.lang) # # generate durations.txt duration_file = "./durations.txt" gen_duration_from_textgrid(mfa_dir, duration_file, fs, n_shift) # generate phone and speaker map files extract_feature(duration_file, config, input_dir, dump_dir, pretrained_model_dir) # create finetune env generate_finetune_env(output_dir, pretrained_model_dir) # create a new args for training train_args = TrainArgs(args.ngpu, config_file, dump_dir, output_dir) # finetune models # dispatch if args.ngpu > 1: dist.spawn(train_sp, (train_args, config), nprocs=args.ngpu) else: train_sp(train_args, config)