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