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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, "r", encoding="utf-8")) | |
| else: | |
| config = json.load(open("configs/config.json", "r", encoding="utf-8")) | |
| 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_legacy.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)).replace( | |
| "\\", "/" | |
| ) | |
| 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" | |
| 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 WavLM 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" | |
| "- [WavLM](https://huggingface.co/microsoft/wavlm-base-plus)\n" | |
| "\n" | |
| "将 BERT 模型放置到 `bert` 文件夹下，WavLM 模型放置到 `slm` 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="## 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`、`WD_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]) | |
| webbrowser.open("http://127.0.0.1:7860") | |
| app.launch(share=False, server_port=7860) | |