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so-vits-svc
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Increase convenience for colab training

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謬紗特 2023-03-12 15:59:29 +08:00 committed by GitHub
parent d40ae694fe
commit c092d25716
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3 changed files with 189 additions and 189 deletions
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106
configs/config.json
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@ -1,106 +0,0 @@
{
"train": {
"log_interval": 50,
"eval_interval": 1000,
"seed": 1234,
"port": 8001,
"epochs": 10000,
"learning_rate": 0.0002,
"betas": [
0.8,
0.99
],
"eps": 1e-09,
"batch_size": 6,
"accumulation_steps": 1,
"fp16_run": false,
"lr_decay": 0.998,
"segment_size": 10240,
"init_lr_ratio": 1,
"warmup_epochs": 0,
"c_mel": 45,
"keep_ckpts":4
},
"data": {
"data_dir": "dataset",
"dataset_type": "SingDataset",
"collate_type": "SingCollate",
"training_filelist": "filelists/train.txt",
"validation_filelist": "filelists/val.txt",
"max_wav_value": 32768.0,
"sampling_rate": 44100,
"n_fft": 2048,
"fmin": 0,
"fmax": 22050,
"hop_length": 512,
"win_size": 2048,
"acoustic_dim": 80,
"c_dim": 256,
"min_level_db": -115,
"ref_level_db": 20,
"min_db": -115,
"max_abs_value": 4.0,
"n_speakers": 200
},
"model": {
"hidden_channels": 192,
"spk_channels": 192,
"filter_channels": 768,
"n_heads": 2,
"n_layers": 4,
"kernel_size": 3,
"p_dropout": 0.1,
"prior_hidden_channels": 192,
"prior_filter_channels": 768,
"prior_n_heads": 2,
"prior_n_layers": 4,
"prior_kernel_size": 3,
"prior_p_dropout": 0.1,
"resblock": "1",
"use_spectral_norm": false,
"resblock_kernel_sizes": [
3,
7,
11
],
"resblock_dilation_sizes": [
[
1,
3,
5
],
[
1,
3,
5
],
[
1,
3,
5
]
],
"upsample_rates": [
8,
8,
4,
2
],
"upsample_initial_channel": 256,
"upsample_kernel_sizes": [
16,
16,
8,
4
],
"n_harmonic": 64,
"n_bands": 65
},
"spk": {
"jishuang": 0,
"huiyu": 1,
"nen": 2,
"paimon": 3,
"yunhao": 4
}
}

106
configs_template/config_template.json Normal file
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@ -0,0 +1,106 @@
{
"train": {
"log_interval": 50,
"eval_interval": 1000,
"seed": 1234,
"port": 8001,
"epochs": 10000,
"learning_rate": 0.0002,
"betas": [
0.8,
0.99
],
"eps": 1e-09,
"batch_size": 6,
"accumulation_steps": 1,
"fp16_run": false,
"lr_decay": 0.998,
"segment_size": 10240,
"init_lr_ratio": 1,
"warmup_epochs": 0,
"c_mel": 45,
"keep_ckpts":4
},
"data": {
"data_dir": "dataset",
"dataset_type": "SingDataset",
"collate_type": "SingCollate",
"training_filelist": "filelists/train.txt",
"validation_filelist": "filelists/val.txt",
"max_wav_value": 32768.0,
"sampling_rate": 44100,
"n_fft": 2048,
"fmin": 0,
"fmax": 22050,
"hop_length": 512,
"win_size": 2048,
"acoustic_dim": 80,
"c_dim": 256,
"min_level_db": -115,
"ref_level_db": 20,
"min_db": -115,
"max_abs_value": 4.0,
"n_speakers": 200
},
"model": {
"hidden_channels": 192,
"spk_channels": 192,
"filter_channels": 768,
"n_heads": 2,
"n_layers": 4,
"kernel_size": 3,
"p_dropout": 0.1,
"prior_hidden_channels": 192,
"prior_filter_channels": 768,
"prior_n_heads": 2,
"prior_n_layers": 4,
"prior_kernel_size": 3,
"prior_p_dropout": 0.1,
"resblock": "1",
"use_spectral_norm": false,
"resblock_kernel_sizes": [
3,
7,
11
],
"resblock_dilation_sizes": [
[
1,
3,
5
],
[
1,
3,
5
],
[
1,
3,
5
]
],
"upsample_rates": [
8,
8,
4,
2
],
"upsample_initial_channel": 256,
"upsample_kernel_sizes": [
16,
16,
8,
4
],
"n_harmonic": 64,
"n_bands": 65
},
"spk": {
"jishuang": 0,
"huiyu": 1,
"nen": 2,
"paimon": 3,
"yunhao": 4
}
}

166
preprocess_flist_config.py
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@ -1,83 +1,83 @@
import os
import argparse
import re
from tqdm import tqdm
from random import shuffle
import json
import wave
config_template = json.load(open("configs/config.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("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
parser.add_argument("--source_dir", type=str, default="./dataset/44k", 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))]
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:-2]
val += wavs[:2]
test += wavs[-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["spk"] = spk_dict
print("Writing configs/config.json")
with open("configs/config.json", "w") as f:
json.dump(config_template, f, indent=2)
import os
import argparse
import re
from tqdm import tqdm
from random import shuffle
import json
import wave
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("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
parser.add_argument("--source_dir", type=str, default="./dataset/44k", 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))]
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:-2]
val += wavs[:2]
test += wavs[-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["spk"] = spk_dict
print("Writing configs/config.json")
with open("configs/config.json", "w") as f:
json.dump(config_template, f, indent=2)