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- import os
- import argparse
- from tqdm import tqdm
- from random import shuffle
- import json
- config_template = {
- "train": {
- "log_interval": 200,
- "eval_interval": 1000,
- "seed": 1234,
- "epochs": 10000,
- "learning_rate": 1e-4,
- "betas": [0.8, 0.99],
- "eps": 1e-9,
- "batch_size": 12,
- "fp16_run": False,
- "lr_decay": 0.999875,
- "segment_size": 17920,
- "init_lr_ratio": 1,
- "warmup_epochs": 0,
- "c_mel": 45,
- "c_kl": 1.0,
- "use_sr": True,
- "max_speclen": 384,
- "port": "8001"
- },
- "data": {
- "training_files":"filelists/train.txt",
- "validation_files":"filelists/val.txt",
- "max_wav_value": 32768.0,
- "sampling_rate": 48000,
- "filter_length": 1280,
- "hop_length": 320,
- "win_length": 1280,
- "n_mel_channels": 80,
- "mel_fmin": 0.0,
- "mel_fmax": None
- },
- "model": {
- "inter_channels": 192,
- "hidden_channels": 192,
- "filter_channels": 768,
- "n_heads": 2,
- "n_layers": 6,
- "kernel_size": 3,
- "p_dropout": 0.1,
- "resblock": "1",
- "resblock_kernel_sizes": [3,7,11],
- "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
- "upsample_rates": [10,8,2,2],
- "upsample_initial_channel": 512,
- "upsample_kernel_sizes": [16,16,4,4],
- "n_layers_q": 3,
- "use_spectral_norm": False,
- "gin_channels": 256,
- "ssl_dim": 256,
- "n_speakers": 0,
- },
- "spk":{
- "nen": 0,
- "paimon": 1,
- "yunhao": 2
- }
- }
-
-
- 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("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
- parser.add_argument("--source_dir", type=str, default="./dataset/48k", help="path to source dir")
- args = parser.parse_args()
-
- train = []
- val = []
- test = []
- 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 = [os.path.join(args.source_dir, speaker, i)for i in os.listdir(os.path.join(args.source_dir, speaker))]
- wavs = [i for i in wavs if i.endswith("wav")]
- shuffle(wavs)
- train += wavs[2:-10]
- val += wavs[:2]
- test += wavs[-10:]
- n_speakers = len(spk_dict.keys())*2
- shuffle(train)
- shuffle(val)
- shuffle(test)
-
- 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")
-
- print("Writing", args.test_list)
- with open(args.test_list, "w") as f:
- for fname in tqdm(test):
- wavpath = fname
- f.write(wavpath + "\n")
-
- config_template["model"]["n_speakers"] = n_speakers
- config_template["spk"] = spk_dict
- print("Writing configs/config.json")
- with open("configs/config.json", "w") as f:
- json.dump(config_template, f, indent=2)
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