Spaces:
Running
Running
File size: 8,200 Bytes
2e9bf0c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
import gradio as gr
import webbrowser
import os
import json
import subprocess
import shutil
def get_path(data_dir):
start_path = os.path.join("./data", data_dir)
lbl_path = os.path.join(start_path, "esd.list")
train_path = os.path.join(start_path, "train.list")
val_path = os.path.join(start_path, "val.list")
config_path = os.path.join(start_path, "configs", "config.json")
return start_path, lbl_path, train_path, val_path, config_path
def generate_config(data_dir, batch_size):
assert data_dir != "", "数据集名称不能为空"
start_path, _, train_path, val_path, config_path = get_path(data_dir)
if os.path.isfile(config_path):
config = json.load(open(config_path))
else:
config = json.load(open("configs/config.json"))
config["data"]["training_files"] = train_path
config["data"]["validation_files"] = val_path
config["train"]["batch_size"] = batch_size
out_path = os.path.join(start_path, "configs")
if not os.path.isdir(out_path):
os.mkdir(out_path)
model_path = os.path.join(start_path, "models")
if not os.path.isdir(model_path):
os.mkdir(model_path)
with open(config_path, "w", encoding="utf-8") as f:
json.dump(config, f, indent=4)
if not os.path.exists("config.yml"):
shutil.copy(src="default_config.yml", dst="config.yml")
return "配置文件生成完成"
def resample(data_dir):
assert data_dir != "", "数据集名称不能为空"
start_path, _, _, _, config_path = get_path(data_dir)
in_dir = os.path.join(start_path, "raw")
out_dir = os.path.join(start_path, "wavs")
subprocess.run(
f"python resample.py "
f"--sr 44100 "
f"--in_dir {in_dir} "
f"--out_dir {out_dir} ",
shell=True,
)
return "音频文件预处理完成"
def preprocess_text(data_dir):
assert data_dir != "", "数据集名称不能为空"
start_path, lbl_path, train_path, val_path, config_path = get_path(data_dir)
lines = open(lbl_path, "r", encoding="utf-8").readlines()
with open(lbl_path, "w", encoding="utf-8") as f:
for line in lines:
path, spk, language, text = line.strip().split("|")
path = os.path.join(start_path, "wavs", os.path.basename(path))
f.writelines(f"{path}|{spk}|{language}|{text}\n")
subprocess.run(
f"python preprocess_text.py "
f"--transcription-path {lbl_path} "
f"--train-path {train_path} "
f"--val-path {val_path} "
f"--config-path {config_path}",
shell=True,
)
return "标签文件预处理完成"
def bert_gen(data_dir):
assert data_dir != "", "数据集名称不能为空"
_, _, _, _, config_path = get_path(data_dir)
subprocess.run(
f"python bert_gen.py " f"--config {config_path}",
shell=True,
)
return "BERT 特征文件生成完成"
def clap_gen(data_dir):
assert data_dir != "", "数据集名称不能为空"
_, _, _, _, config_path = get_path(data_dir)
subprocess.run(
f"python clap_gen.py " f"--config {config_path}",
shell=True,
)
return "CLAP 特征文件生成完成"
if __name__ == "__main__":
with gr.Blocks() as app:
with gr.Row():
with gr.Column():
_ = gr.Markdown(
value="# Bert-VITS2 数据预处理\n"
"## 预先准备：\n"
"下载 BERT 和 CLAP 模型：\n"
"- [中文 RoBERTa](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)\n"
"- [日文 DeBERTa](https://huggingface.co/ku-nlp/deberta-v2-large-japanese-char-wwm)\n"
"- [英文 DeBERTa](https://huggingface.co/microsoft/deberta-v3-large)\n"
"- [CLAP](https://huggingface.co/laion/clap-htsat-fused)\n"
"\n"
"将 BERT 模型放置到 `bert` 文件夹下，CLAP 模型放置到 `emotional` 文件夹下，覆盖同名文件夹。\n"
"\n"
"数据准备：\n"
"将数据放置在 data 文件夹下，按照如下结构组织：\n"
"\n"
"```\n"
"├── data\n"
"│ ├── {你的数据集名称}\n"
"│ │ ├── esd.list\n"
"│ │ ├── raw\n"
"│ │ │ ├── ****.wav\n"
"│ │ │ ├── ****.wav\n"
"│ │ │ ├── ...\n"
"```\n"
"\n"
"其中，`raw` 文件夹下保存所有的音频文件，`esd.list` 文件为标签文本，格式为\n"
"\n"
"```\n"
"****.wav|{说话人名}|{语言 ID}|{标签文本}\n"
"```\n"
"\n"
"例如：\n"
"```\n"
"vo_ABDLQ001_1_paimon_02.wav|派蒙|ZH|没什么没什么，只是平时他总是站在这里，有点奇怪而已。\n"
"noa_501_0001.wav|NOA|JP|そうだね、油断しないのはとても大事なことだと思う\n"
"Albedo_vo_ABDLQ002_4_albedo_01.wav|Albedo|EN|Who are you? Why did you alarm them?\n"
"...\n"
"```\n"
)
data_dir = gr.Textbox(
label="数据集名称",
placeholder="你放置在 data 文件夹下的数据集所在文件夹的名称，如 data/genshin 则填 genshin",
)
info = gr.Textbox(label="状态信息")
_ = gr.Markdown(value="## 第一步：生成配置文件")
with gr.Row():
batch_size = gr.Slider(
label="批大小（Batch size）：24 GB 显存可用 12",
value=8,
minimum=1,
maximum=64,
step=1,
)
generate_config_btn = gr.Button(value="执行", variant="primary")
_ = gr.Markdown(value="## 第二步：预处理音频文件")
resample_btn = gr.Button(value="执行", variant="primary")
_ = gr.Markdown(value="## 第三步：预处理标签文件")
preprocess_text_btn = gr.Button(value="执行", variant="primary")
_ = gr.Markdown(value="## 第四步：生成 BERT 特征文件")
bert_gen_btn = gr.Button(value="执行", variant="primary")
_ = gr.Markdown(value="## 第五步：生成 CLAP 特征文件")
clap_gen_btn = gr.Button(value="执行", variant="primary")
_ = gr.Markdown(
value="## 训练模型及部署：\n"
"修改根目录下的 `config.yml` 中 `dataset_path` 一项为 `data/{你的数据集名称}`\n"
"- 训练：将[预训练模型文件](https://openi.pcl.ac.cn/Stardust_minus/Bert-VITS2/modelmanage/show_model)（`D_0.pth`、`DUR_0.pth` 和 `G_0.pth`）放到 `data/{你的数据集名称}/models` 文件夹下，执行 `torchrun --nproc_per_node=1 train_ms.py` 命令（多卡运行可参考 `run_MnodesAndMgpus.sh` 中的命令。\n"
"- 部署：修改根目录下的 `config.yml` 中 `webui` 下 `model` 一项为 `models/{权重文件名}.pth` （如 G_10000.pth），然后执行 `python webui.py`"
)
generate_config_btn.click(
generate_config, inputs=[data_dir, batch_size], outputs=[info]
)
resample_btn.click(resample, inputs=[data_dir], outputs=[info])
preprocess_text_btn.click(preprocess_text, inputs=[data_dir], outputs=[info])
bert_gen_btn.click(bert_gen, inputs=[data_dir], outputs=[info])
clap_gen_btn.click(clap_gen, inputs=[data_dir], outputs=[info])
webbrowser.open("http://127.0.0.1:7860")
app.launch(share=False, server_port=7860)
|