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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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config_template = {
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"train": {
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"log_interval": 100,
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"eval_interval": 100,
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"seed": 1234,
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"epochs": 10000,
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"learning_rate": 1e-4,
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"betas": [0.8, 0.99],
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"eps": 1e-9,
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"batch_size": 6,
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"fp16_run": False,
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"lr_decay": 0.999875,
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"segment_size": 17920,
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"init_lr_ratio": 1,
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"warmup_epochs": 0,
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"c_mel": 45,
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"c_kl": 1.0,
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"use_sr": True,
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"max_speclen": 384,
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"port": "8001"
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},
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"data": {
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"training_files":"filelists/train.txt",
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"validation_files":"filelists/val.txt",
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"max_wav_value": 32768.0,
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"sampling_rate": 32000,
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"filter_length": 1024,
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"hop_length": 320,
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"win_length": 1024,
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"n_mel_channels": 80,
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"mel_fmin": 0.0,
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"mel_fmax": None
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},
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"model": {
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"inter_channels": 192,
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"hidden_channels": 192,
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"filter_channels": 768,
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"n_heads": 2,
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"n_layers": 6,
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"kernel_size": 3,
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"p_dropout": 0.1,
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"resblock": "1",
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"resblock_kernel_sizes": [3,7,11],
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"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
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"upsample_rates": [10,8,2,2],
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"upsample_initial_channel": 512,
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"upsample_kernel_sizes": [16,16,4,4],
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"n_layers_q": 3,
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"use_spectral_norm": False,
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"gin_channels": 256,
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"ssl_dim": 256,
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"n_speakers": 0,
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},
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"spk":{
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"nen": 0,
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"paimon": 1,
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"yunhao": 2
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}
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}
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pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
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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("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
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parser.add_argument("--source_dir", type=str, default="./dataset/32k", help="path to source dir")
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args = parser.parse_args()
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train = []
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val = []
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test = []
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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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for wavpath in wavs:
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if not pattern.match(wavpath):
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print(f"warning：文件名{wavpath}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 len(wavs) < 10:
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print(f"warning：{speaker}数据集数量小于10条，请补充数据")
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wavs = [i for i in wavs if i.endswith("wav")]
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shuffle(wavs)
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sp=max(2,int(len(wavs)/20))
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train += wavs[sp:-sp]
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val += wavs[:sp]
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test += wavs[-sp:]
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n_speakers = len(spk_dict.keys())*2
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shuffle(train)
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shuffle(val)
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shuffle(test)
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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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print("Writing", args.test_list)
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with open(args.test_list, "w") as f:
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for fname in tqdm(test):
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wavpath = fname
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f.write(wavpath + "\n")
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config_template["model"]["n_speakers"] = n_speakers
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config_template["spk"] = spk_dict
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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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