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import os
import argparse
import re
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": 32000,
"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
}
}
pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
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/32k", 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 = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))]
for wavpath in wavs:
if not pattern.match(wavpath):
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）")
if len(wavs) < 10:
print(f"warning：{speaker}数据集数量小于10条，请补充数据")
wavs = [i for i in wavs if i.endswith("wav")]
shuffle(wavs)
train += wavs[2:-2]
val += wavs[:2]
test += wavs[-2:]
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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