105 lines
4.4 KiB
Python
105 lines
4.4 KiB
Python
import os
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import argparse
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import re
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from tqdm import tqdm
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from random import shuffle
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import json
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import wave
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import diffusion.logger.utils as du
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config_template = json.load(open("configs_template/config_template.json"))
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pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
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def get_wav_duration(file_path):
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with wave.open(file_path, 'rb') as wav_file:
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# 获取音频帧数
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n_frames = wav_file.getnframes()
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# 获取采样率
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framerate = wav_file.getframerate()
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# 计算时长（秒）
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duration = n_frames / float(framerate)
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return duration
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
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parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
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parser.add_argument("--source_dir", type=str, default="./dataset/44k", help="path to source dir")
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parser.add_argument("--speech_encoder", type=str, default="vec768l12", help="choice a speech encoder|'vec768l12','vec256l9','hubertsoft','whisper-ppg','cnhubertlarge','dphubert','whisper-ppg-large','wavlmbase+'")
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parser.add_argument("--vol_aug", action="store_true", help="Whether to use volume embedding and volume augmentation")
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args = parser.parse_args()
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train = []
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val = []
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idx = 0
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spk_dict = {}
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spk_id = 0
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for speaker in tqdm(os.listdir(args.source_dir)):
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spk_dict[speaker] = spk_id
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spk_id += 1
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wavs = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))]
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new_wavs = []
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for file in wavs:
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if not file.endswith("wav"):
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continue
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if not pattern.match(file):
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print(f"warning：文件名{file}中包含非字母数字下划线，可能会导致错误。（也可能不会）")
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if get_wav_duration(file) < 0.3:
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print("skip too short audio:", file)
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continue
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new_wavs.append(file)
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wavs = new_wavs
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shuffle(wavs)
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train += wavs[2:]
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val += wavs[:2]
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shuffle(train)
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shuffle(val)
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print("Writing", args.train_list)
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with open(args.train_list, "w") as f:
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for fname in tqdm(train):
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wavpath = fname
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f.write(wavpath + "\n")
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print("Writing", args.val_list)
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with open(args.val_list, "w") as f:
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for fname in tqdm(val):
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wavpath = fname
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f.write(wavpath + "\n")
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d_config_template = du.load_config("configs_template/diffusion_template.yaml")
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d_config_template["model"]["n_spk"] = spk_id
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d_config_template["data"]["encoder"] = args.speech_encoder
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d_config_template["spk"] = spk_dict
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config_template["spk"] = spk_dict
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config_template["model"]["n_speakers"] = spk_id
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config_template["model"]["speech_encoder"] = args.speech_encoder
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if args.speech_encoder == "vec768l12" or args.speech_encoder == "dphubert" or args.speech_encoder == "wavlmbase+":
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config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 768
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d_config_template["data"]["encoder_out_channels"] = 768
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elif args.speech_encoder == "vec256l9" or args.speech_encoder == 'hubertsoft':
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config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 256
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d_config_template["data"]["encoder_out_channels"] = 256
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elif args.speech_encoder == "whisper-ppg" or args.speech_encoder == 'cnhubertlarge':
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config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 1024
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d_config_template["data"]["encoder_out_channels"] = 1024
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elif args.speech_encoder == "whisper-ppg-large":
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config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 1280
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d_config_template["data"]["encoder_out_channels"] = 1280
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if args.vol_aug:
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config_template["train"]["vol_aug"] = config_template["model"]["vol_embedding"] = True
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print("Writing configs/config.json")
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with open("configs/config.json", "w") as f:
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json.dump(config_template, f, indent=2)
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print("Writing configs/diffusion.yaml")
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du.save_config("configs/diffusion.yaml",d_config_template)
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