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)