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import gradio as gr |
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from app.original import * |
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def train_tabs(): |
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with gr.TabItem(i18n("训练")): |
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gr.Markdown( |
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value=i18n( |
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"step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. " |
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) |
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) |
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with gr.Row(): |
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exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test") |
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sr2 = gr.Radio( |
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label=i18n("目标采样率"), |
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choices=["40k", "48k"], |
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value="40k", |
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interactive=True, |
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) |
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if_f0_3 = gr.Radio( |
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label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), |
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choices=[i18n("是"), i18n("否")], |
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value=i18n("是"), |
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interactive=True, |
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) |
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version19 = gr.Radio( |
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label=i18n("版本"), |
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choices=["v1", "v2"], |
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value="v2", |
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interactive=True, |
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visible=True, |
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) |
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np7 = gr.Slider( |
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minimum=0, |
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maximum=config.n_cpu, |
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step=1, |
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label=i18n("提取音高和处理数据使用的CPU进程数"), |
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value=int(np.ceil(config.n_cpu / 1.5)), |
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interactive=True, |
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) |
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with gr.Group(): |
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gr.Markdown( |
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value=i18n( |
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"step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. " |
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) |
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) |
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with gr.Row(): |
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trainset_dir4 = gr.Textbox( |
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label=i18n("输入训练文件夹路径"), |
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value=i18n("E:\\语音音频+标注\\米津玄师\\src"), |
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) |
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spk_id5 = gr.Slider( |
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minimum=0, |
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maximum=4, |
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step=1, |
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label=i18n("请指定说话人id"), |
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value=0, |
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interactive=True, |
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) |
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but1 = gr.Button(i18n("处理数据"), variant="primary") |
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info1 = gr.Textbox(label=i18n("输出信息"), value="") |
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but1.click( |
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preprocess_dataset, |
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[trainset_dir4, exp_dir1, sr2, np7], |
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[info1], |
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api_name="train_preprocess", |
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) |
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with gr.Group(): |
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gr.Markdown( |
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value=i18n( |
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"step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)" |
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) |
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) |
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with gr.Row(): |
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with gr.Column(): |
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gpus6 = gr.Textbox( |
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label=i18n( |
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"以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2" |
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), |
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value=gpus, |
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interactive=True, |
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visible=F0GPUVisible, |
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) |
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gpu_info9 = gr.Textbox( |
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label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible |
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) |
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with gr.Column(): |
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f0method8 = gr.Radio( |
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label=i18n( |
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"选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU" |
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), |
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choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], |
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value="rmvpe_gpu", |
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interactive=True, |
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) |
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gpus_rmvpe = gr.Textbox( |
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label=i18n( |
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"rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程" |
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), |
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value="%s-%s" % (gpus, gpus), |
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interactive=True, |
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visible=F0GPUVisible, |
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) |
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but2 = gr.Button(i18n("特征提取"), variant="primary") |
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info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) |
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f0method8.change( |
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fn=change_f0_method, |
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inputs=[f0method8], |
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outputs=[gpus_rmvpe], |
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) |
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but2.click( |
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extract_f0_feature, |
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[ |
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gpus6, |
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np7, |
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f0method8, |
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if_f0_3, |
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exp_dir1, |
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version19, |
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gpus_rmvpe, |
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], |
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[info2], |
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api_name="train_extract_f0_feature", |
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) |
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with gr.Group(): |
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gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) |
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with gr.Row(): |
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save_epoch10 = gr.Slider( |
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minimum=1, |
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maximum=50, |
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step=1, |
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label=i18n("保存频率save_every_epoch"), |
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value=5, |
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interactive=True, |
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) |
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total_epoch11 = gr.Slider( |
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minimum=2, |
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maximum=1000, |
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step=1, |
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label=i18n("总训练轮数total_epoch"), |
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value=20, |
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interactive=True, |
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) |
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batch_size12 = gr.Slider( |
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minimum=1, |
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maximum=40, |
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step=1, |
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label=i18n("每张显卡的batch_size"), |
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value=default_batch_size, |
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interactive=True, |
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) |
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if_save_latest13 = gr.Radio( |
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label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), |
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choices=[i18n("是"), i18n("否")], |
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value=i18n("否"), |
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interactive=True, |
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) |
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if_cache_gpu17 = gr.Radio( |
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label=i18n( |
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"是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速" |
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), |
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choices=[i18n("是"), i18n("否")], |
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value=i18n("否"), |
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interactive=True, |
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) |
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if_save_every_weights18 = gr.Radio( |
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label=i18n( |
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"是否在每次保存时间点将最终小模型保存至weights文件夹" |
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), |
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choices=[i18n("是"), i18n("否")], |
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value=i18n("否"), |
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interactive=True, |
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) |
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with gr.Row(): |
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pretrained_G14 = gr.Textbox( |
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label=i18n("加载预训练底模G路径"), |
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value="assets/pretrained_v2/f0G40k.pth", |
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interactive=True, |
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) |
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pretrained_D15 = gr.Textbox( |
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label=i18n("加载预训练底模D路径"), |
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value="assets/pretrained_v2/f0D40k.pth", |
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interactive=True, |
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) |
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sr2.change( |
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change_sr2, |
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[sr2, if_f0_3, version19], |
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[pretrained_G14, pretrained_D15], |
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) |
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version19.change( |
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change_version19, |
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[sr2, if_f0_3, version19], |
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[pretrained_G14, pretrained_D15, sr2], |
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) |
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if_f0_3.change( |
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change_f0, |
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[if_f0_3, sr2, version19], |
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[f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15], |
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) |
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gpus16 = gr.Textbox( |
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label=i18n( |
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"以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2" |
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), |
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value=gpus, |
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interactive=True, |
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) |
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but3 = gr.Button(i18n("训练模型"), variant="primary") |
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but4 = gr.Button(i18n("训练特征索引"), variant="primary") |
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but5 = gr.Button(i18n("一键训练"), variant="primary") |
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info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) |
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but3.click( |
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click_train, |
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[ |
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exp_dir1, |
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sr2, |
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if_f0_3, |
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spk_id5, |
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save_epoch10, |
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total_epoch11, |
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batch_size12, |
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if_save_latest13, |
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pretrained_G14, |
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pretrained_D15, |
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gpus16, |
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if_cache_gpu17, |
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if_save_every_weights18, |
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version19, |
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], |
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info3, |
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api_name="train_start", |
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) |
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but4.click(train_index, [exp_dir1, version19], info3) |
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but5.click( |
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train1key, |
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[ |
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exp_dir1, |
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sr2, |
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if_f0_3, |
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trainset_dir4, |
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spk_id5, |
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np7, |
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f0method8, |
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save_epoch10, |
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total_epoch11, |
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batch_size12, |
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if_save_latest13, |
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pretrained_G14, |
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pretrained_D15, |
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gpus16, |
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if_cache_gpu17, |
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if_save_every_weights18, |
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version19, |
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gpus_rmvpe, |
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], |
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info3, |
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api_name="train_start_all", |
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) |
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