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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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n_frames = wav_file.getnframes() |
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framerate = wav_file.getframerate() |
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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_template.yaml") |
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du.save_config("configs/diffusion.yaml",d_config_template) |
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