Use clap to achieve prompt controlled generation (#223)
* 快速分类音频并把yml格式结果存在训练根目录里 (#190) * Add files via upload * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Update models.py * Update webui.py * Update infer.py * Create compress_model.py * 重新提交，更新Gradio推理UI (#193) * Update webui.py * Update webui.py * 更新 train_ms.py * 更新 models.py * 更新 models.py * 更新 models.py * 更新 train_ms.py * 更新 train_ms.py * 更新 models.py * Update preprocess_text.py * Update config.json * Update train_ms.py * Update webui.py (#206) * Add files via upload (#209) * Update train_ms.py * Update train_ms.py * Update preprocess_text.py * Update train_ms.py * fix (#211) * Update emotion_clustering.py * Add files via upload * Update emotion_clustering.py * add cluster center save * Add files via upload * Update config.py * Update default_config.yml * Update config.py * Update config.py * Update emotion_clustering.py * Update emotion_clustering.py * Update config.py * Update emotion_clustering.py * Update emotion_clustering.py * Update webui.py * Update emotion_clustering.py * Update commons.py * Update emotion_clustering.py * Update webui.py * Update webui.py * Add files via upload * Update train_ms.py * Update train_ms.py * Update train_ms.py * Update train_ms.py * Update train_ms.py * Update webui.py * Update emotion_clustering.py * Update emotion_clustering.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix default_config.yml. * Update infer.py * feat: support infer 2.1 models * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix: support infer 2.1 models 兼容bug修复 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train_ms.py * Add CLAP * Fix data loader * Fix infer.py * Fix webui.py * Add prompt template * Update clap_gen.py * Fix wrong environ value * Add g for dur disc * Update clap_gen.py * Fix multilang generation * Update config.json * Prompt mode * Improve slice segments performance * Add preprocess webui * Update webui_preprocess.py * Update webui_preprocess.py * Update config.py * Update default_config.yml * Update config.py * Update clap_gen.py * Delete emo_gen.py * Delete get_emo.py * Delete emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim directory * Update README.md * Update README * Split val per lang * Delete emotion_clustering.py * Update default_config.yml * Update default_config.yml * Update config.py * Update preprocess_text.py * Update webui_preprocess.py * Update defalut_config.yml * Update webui_preprocess.py * Update preprocess_text.py * Random augmentation for CLAP * Update data_utils.py * Update preprocess_text.py * Add vq for CLAP features to avoid overfitting * Random dummy inputs * Update webui.py * Update models.py * Update infer.py * Apply Code Formatter Change * Update config.json * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: YYuX-1145 <138500330+YYuX-1145@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Sora <654163754@qq.com> Co-authored-by: Sihan Wang <wangsihan1995@gmail.com> Co-authored-by: Stardust-minus <Stardust-minus@users.noreply.github.com>
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Stardust·减
177
webui_preprocess.py
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webui_preprocess.py
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import gradio as gr
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import webbrowser
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import os
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import json
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import subprocess
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import shutil
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def get_path(data_dir):
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start_path = os.path.join("./data", data_dir)
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lbl_path = os.path.join(start_path, "esd.list")
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train_path = os.path.join(start_path, "train.list")
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val_path = os.path.join(start_path, "val.list")
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config_path = os.path.join(start_path, "configs", "config.json")
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return start_path, lbl_path, train_path, val_path, config_path
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def generate_config(data_dir, batch_size):
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assert data_dir != "", "数据集名称不能为空"
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start_path, _, train_path, val_path, config_path = get_path(data_dir)
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if os.path.isfile(config_path):
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config = json.load(open(config_path))
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else:
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config = json.load(open("configs/config.json"))
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config["data"]["training_files"] = train_path
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config["data"]["validation_files"] = val_path
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config["train"]["batch_size"] = batch_size
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out_path = os.path.join(start_path, "configs")
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if not os.path.isdir(out_path):
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os.mkdir(out_path)
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model_path = os.path.join(start_path, "models")
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if not os.path.isdir(model_path):
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os.mkdir(model_path)
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with open(config_path, "w", encoding="utf-8") as f:
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json.dump(config, f, indent=4)
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if not os.path.exists("config.yml"):
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shutil.copy(src="default_config.yml", dst="config.yml")
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return "配置文件生成完成"
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def resample(data_dir):
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assert data_dir != "", "数据集名称不能为空"
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start_path, _, _, _, config_path = get_path(data_dir)
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in_dir = os.path.join(start_path, "raw")
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out_dir = os.path.join(start_path, "wavs")
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subprocess.run(
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f"python resample.py "
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f"--sr 44100 "
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f"--in_dir {in_dir} "
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f"--out_dir {out_dir} ",
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shell=True,
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)
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return "音频文件预处理完成"
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def preprocess_text(data_dir):
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assert data_dir != "", "数据集名称不能为空"
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start_path, lbl_path, train_path, val_path, config_path = get_path(data_dir)
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lines = open(lbl_path, "r", encoding="utf-8").readlines()
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with open(lbl_path, "w", encoding="utf-8") as f:
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for line in lines:
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path, spk, language, text = line.strip().split("|")
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path = os.path.join(start_path, "wavs", os.path.basename(path))
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f.writelines(f"{path}|{spk}|{language}|{text}\n")
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subprocess.run(
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f"python preprocess_text.py "
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f"--transcription-path {lbl_path} "
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f"--train-path {train_path} "
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f"--val-path {val_path} "
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f"--config-path {config_path}",
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shell=True,
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)
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return "标签文件预处理完成"
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def bert_gen(data_dir):
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assert data_dir != "", "数据集名称不能为空"
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_, _, _, _, config_path = get_path(data_dir)
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subprocess.run(
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f"python bert_gen.py " f"--config {config_path}",
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shell=True,
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)
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return "BERT 特征文件生成完成"
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def clap_gen(data_dir):
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assert data_dir != "", "数据集名称不能为空"
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_, _, _, _, config_path = get_path(data_dir)
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subprocess.run(
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f"python clap_gen.py " f"--config {config_path}",
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shell=True,
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)
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return "CLAP 特征文件生成完成"
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if __name__ == "__main__":
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with gr.Blocks() as app:
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with gr.Row():
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with gr.Column():
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_ = gr.Markdown(
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value="# Bert-VITS2 数据预处理\n"
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"## 预先准备：\n"
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"下载 BERT 和 CLAP 模型：\n"
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"- [中文 RoBERTa](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)\n"
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"- [日文 DeBERTa](https://huggingface.co/ku-nlp/deberta-v2-large-japanese-char-wwm)\n"
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"- [英文 DeBERTa](https://huggingface.co/microsoft/deberta-v3-large)\n"
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"- [CLAP](https://huggingface.co/laion/clap-htsat-fused)\n"
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"\n"
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"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"
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"\n"
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"数据准备：\n"
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"将数据放置在 data 文件夹下，按照如下结构组织：\n"
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"\n"
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"```\n"
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"├── data\n"
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"│ ├── {你的数据集名称}\n"
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"│ │ ├── esd.list\n"
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"│ │ ├── raw\n"
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"│ │ │ ├── ****.wav\n"
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"│ │ │ ├── ****.wav\n"
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"│ │ │ ├── ...\n"
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"```\n"
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"\n"
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"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"
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"\n"
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"```\n"
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"****.wav|{说话人名}|{语言 ID}|{标签文本}\n"
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"```\n"
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"\n"
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"例如：\n"
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"```\n"
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"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"
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"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"
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"Albedo_vo_ABDLQ002_4_albedo_01.wav|Albedo|EN|Who are you? Why did you alarm them?\n"
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"...\n"
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"```\n"
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)
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data_dir = gr.Textbox(
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label="数据集名称",
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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",
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)
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info = gr.Textbox(label="状态信息")
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_ = gr.Markdown(value="## 第一步：生成配置文件")
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with gr.Row():
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batch_size = gr.Slider(
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label="批大小（Batch size）：24 GB 显存可用 12",
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value=8,
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minimum=1,
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maximum=64,
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step=1,
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)
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generate_config_btn = gr.Button(value="执行", variant="primary")
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_ = gr.Markdown(value="## 第二步：预处理音频文件")
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resample_btn = gr.Button(value="执行", variant="primary")
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_ = gr.Markdown(value="## 第三步：预处理标签文件")
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preprocess_text_btn = gr.Button(value="执行", variant="primary")
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_ = gr.Markdown(value="## 第四步：生成 BERT 特征文件")
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bert_gen_btn = gr.Button(value="执行", variant="primary")
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_ = gr.Markdown(value="## 第五步：生成 CLAP 特征文件")
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clap_gen_btn = gr.Button(value="执行", variant="primary")
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_ = gr.Markdown(
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value="## 训练模型及部署：\n"
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"修改根目录下的 `config.yml` 中 `dataset_path` 一项为 `data/{你的数据集名称}`\n"
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"- 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"
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"- 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`"
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)
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generate_config_btn.click(
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generate_config, inputs=[data_dir, batch_size], outputs=[info]
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)
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resample_btn.click(resample, inputs=[data_dir], outputs=[info])
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preprocess_text_btn.click(preprocess_text, inputs=[data_dir], outputs=[info])
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bert_gen_btn.click(bert_gen, inputs=[data_dir], outputs=[info])
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clap_gen_btn.click(clap_gen, inputs=[data_dir], outputs=[info])
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webbrowser.open("http://127.0.0.1:7860")
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app.launch(share=False, server_port=7860)
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