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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)
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