| | import gradio as gr |
| | from original import * |
| |
|
| |
|
| |
|
| | with gr.Blocks(title="RVC UI") as app: |
| | gr.Label("RVC UI") |
| | gr.Markdown( |
| | value=i18n( |
| | "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>." |
| | ) |
| | ) |
| | with gr.Tabs(): |
| | with gr.TabItem(i18n("模型推理")): |
| | with gr.Row(): |
| | sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names)) |
| | with gr.Column(): |
| | refresh_button = gr.Button( |
| | i18n("刷新音色列表和索引路径"), variant="primary" |
| | ) |
| | clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") |
| | spk_item = gr.Slider( |
| | minimum=0, |
| | maximum=2333, |
| | step=1, |
| | label=i18n("请选择说话人id"), |
| | value=0, |
| | visible=False, |
| | interactive=True, |
| | ) |
| | clean_button.click( |
| | fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean" |
| | ) |
| | with gr.TabItem(i18n("单次推理")): |
| | with gr.Group(): |
| | with gr.Row(): |
| | with gr.Column(): |
| | vc_transform0 = gr.Number( |
| | label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), |
| | value=0, |
| | ) |
| | input_audio0 = gr.Textbox( |
| | label=i18n( |
| | "输入待处理音频文件路径(默认是正确格式示例)" |
| | ), |
| | placeholder="C:\\Users\\Desktop\\audio_example.wav", |
| | ) |
| | file_index1 = gr.Textbox( |
| | label=i18n( |
| | "特征检索库文件路径,为空则使用下拉的选择结果" |
| | ), |
| | placeholder="C:\\Users\\Desktop\\model_example.index", |
| | interactive=True, |
| | ) |
| | file_index2 = gr.Dropdown( |
| | label=i18n("自动检测index路径,下拉式选择(dropdown)"), |
| | choices=sorted(index_paths), |
| | interactive=True, |
| | ) |
| | f0method0 = gr.Radio( |
| | label=i18n( |
| | "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" |
| | ), |
| | choices=( |
| | ["pm", "harvest", "crepe", "rmvpe"] |
| | if config.dml == False |
| | else ["pm", "harvest", "rmvpe"] |
| | ), |
| | value="rmvpe", |
| | interactive=True, |
| | ) |
| |
|
| | with gr.Column(): |
| | resample_sr0 = gr.Slider( |
| | minimum=0, |
| | maximum=48000, |
| | label=i18n("后处理重采样至最终采样率，0为不进行重采样"), |
| | value=0, |
| | step=1, |
| | interactive=True, |
| | ) |
| | rms_mix_rate0 = gr.Slider( |
| | minimum=0, |
| | maximum=1, |
| | label=i18n( |
| | "输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络" |
| | ), |
| | value=0.25, |
| | interactive=True, |
| | ) |
| | protect0 = gr.Slider( |
| | minimum=0, |
| | maximum=0.5, |
| | label=i18n( |
| | "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" |
| | ), |
| | value=0.33, |
| | step=0.01, |
| | interactive=True, |
| | ) |
| | filter_radius0 = gr.Slider( |
| | minimum=0, |
| | maximum=7, |
| | label=i18n( |
| | ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音" |
| | ), |
| | value=3, |
| | step=1, |
| | interactive=True, |
| | ) |
| | index_rate1 = gr.Slider( |
| | minimum=0, |
| | maximum=1, |
| | label=i18n("检索特征占比"), |
| | value=0.75, |
| | interactive=True, |
| | ) |
| | f0_file = gr.File( |
| | label=i18n( |
| | "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调" |
| | ), |
| | visible=False, |
| | ) |
| |
|
| | refresh_button.click( |
| | fn=change_choices, |
| | inputs=[], |
| | outputs=[sid0, file_index2], |
| | api_name="infer_refresh", |
| | ) |
| | |
| | |
| | |
| | |
| | |
| | with gr.Group(): |
| | with gr.Column(): |
| | but0 = gr.Button(i18n("转换"), variant="primary") |
| | with gr.Row(): |
| | vc_output1 = gr.Textbox(label=i18n("输出信息")) |
| | vc_output2 = gr.Audio( |
| | label=i18n("输出音频(右下角三个点,点了可以下载)") |
| | ) |
| |
|
| | but0.click( |
| | vc.vc_single, |
| | [ |
| | spk_item, |
| | input_audio0, |
| | vc_transform0, |
| | f0_file, |
| | f0method0, |
| | file_index1, |
| | file_index2, |
| | |
| | index_rate1, |
| | filter_radius0, |
| | resample_sr0, |
| | rms_mix_rate0, |
| | protect0, |
| | ], |
| | [vc_output1, vc_output2], |
| | api_name="infer_convert", |
| | ) |
| | with gr.TabItem(i18n("批量推理")): |
| | gr.Markdown( |
| | value=i18n( |
| | "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. " |
| | ) |
| | ) |
| | with gr.Row(): |
| | with gr.Column(): |
| | vc_transform1 = gr.Number( |
| | label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), |
| | value=0, |
| | ) |
| | opt_input = gr.Textbox( |
| | label=i18n("指定输出文件夹"), value="opt" |
| | ) |
| | file_index3 = gr.Textbox( |
| | label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), |
| | value="", |
| | interactive=True, |
| | ) |
| | file_index4 = gr.Dropdown( |
| | label=i18n("自动检测index路径,下拉式选择(dropdown)"), |
| | choices=sorted(index_paths), |
| | interactive=True, |
| | ) |
| | f0method1 = gr.Radio( |
| | label=i18n( |
| | "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" |
| | ), |
| | choices=( |
| | ["pm", "harvest", "crepe", "rmvpe"] |
| | if config.dml == False |
| | else ["pm", "harvest", "rmvpe"] |
| | ), |
| | value="rmvpe", |
| | interactive=True, |
| | ) |
| | format1 = gr.Radio( |
| | label=i18n("导出文件格式"), |
| | choices=["wav", "flac", "mp3", "m4a"], |
| | value="wav", |
| | interactive=True, |
| | ) |
| |
|
| | refresh_button.click( |
| | fn=lambda: change_choices()[1], |
| | inputs=[], |
| | outputs=file_index4, |
| | api_name="infer_refresh_batch", |
| | ) |
| | |
| | |
| | |
| | |
| | |
| |
|
| | with gr.Column(): |
| | resample_sr1 = gr.Slider( |
| | minimum=0, |
| | maximum=48000, |
| | label=i18n("后处理重采样至最终采样率，0为不进行重采样"), |
| | value=0, |
| | step=1, |
| | interactive=True, |
| | ) |
| | rms_mix_rate1 = gr.Slider( |
| | minimum=0, |
| | maximum=1, |
| | label=i18n( |
| | "输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络" |
| | ), |
| | value=1, |
| | interactive=True, |
| | ) |
| | protect1 = gr.Slider( |
| | minimum=0, |
| | maximum=0.5, |
| | label=i18n( |
| | "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" |
| | ), |
| | value=0.33, |
| | step=0.01, |
| | interactive=True, |
| | ) |
| | filter_radius1 = gr.Slider( |
| | minimum=0, |
| | maximum=7, |
| | label=i18n( |
| | ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音" |
| | ), |
| | value=3, |
| | step=1, |
| | interactive=True, |
| | ) |
| | index_rate2 = gr.Slider( |
| | minimum=0, |
| | maximum=1, |
| | label=i18n("检索特征占比"), |
| | value=1, |
| | interactive=True, |
| | ) |
| | with gr.Row(): |
| | dir_input = gr.Textbox( |
| | label=i18n( |
| | "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)" |
| | ), |
| | placeholder="C:\\Users\\Desktop\\input_vocal_dir", |
| | ) |
| | inputs = gr.File( |
| | file_count="multiple", |
| | label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"), |
| | ) |
| |
|
| | with gr.Row(): |
| | but1 = gr.Button(i18n("转换"), variant="primary") |
| | vc_output3 = gr.Textbox(label=i18n("输出信息")) |
| |
|
| | but1.click( |
| | vc.vc_multi, |
| | [ |
| | spk_item, |
| | dir_input, |
| | opt_input, |
| | inputs, |
| | vc_transform1, |
| | f0method1, |
| | file_index3, |
| | file_index4, |
| | |
| | index_rate2, |
| | filter_radius1, |
| | resample_sr1, |
| | rms_mix_rate1, |
| | protect1, |
| | format1, |
| | ], |
| | [vc_output3], |
| | api_name="infer_convert_batch", |
| | ) |
| | sid0.change( |
| | fn=vc.get_vc, |
| | inputs=[sid0, protect0, protect1], |
| | outputs=[spk_item, protect0, protect1, file_index2, file_index4], |
| | api_name="infer_change_voice", |
| | ) |
| | with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): |
| | with gr.Group(): |
| | gr.Markdown( |
| | value=i18n( |
| | "人声伴奏分离批量处理， 使用UVR5模型。 <br>合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。 <br>模型分为三类： <br>1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点； <br>2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型； <br> 3、去混响、去延迟模型（by FoxJoy）：<br> (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；<br> (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。<br>去混响/去延迟，附：<br>1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>2、MDX-Net-Dereverb模型挺慢的；<br>3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。" |
| | ) |
| | ) |
| | with gr.Row(): |
| | with gr.Column(): |
| | dir_wav_input = gr.Textbox( |
| | label=i18n("输入待处理音频文件夹路径"), |
| | placeholder="C:\\Users\\Desktop\\todo-songs", |
| | ) |
| | wav_inputs = gr.File( |
| | file_count="multiple", |
| | label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"), |
| | ) |
| | with gr.Column(): |
| | model_choose = gr.Dropdown( |
| | label=i18n("模型"), choices=uvr5_names |
| | ) |
| | agg = gr.Slider( |
| | minimum=0, |
| | maximum=20, |
| | step=1, |
| | label="人声提取激进程度", |
| | value=10, |
| | interactive=True, |
| | visible=False, |
| | ) |
| | opt_vocal_root = gr.Textbox( |
| | label=i18n("指定输出主人声文件夹"), value="opt" |
| | ) |
| | opt_ins_root = gr.Textbox( |
| | label=i18n("指定输出非主人声文件夹"), value="opt" |
| | ) |
| | format0 = gr.Radio( |
| | label=i18n("导出文件格式"), |
| | choices=["wav", "flac", "mp3", "m4a"], |
| | value="flac", |
| | interactive=True, |
| | ) |
| | but2 = gr.Button(i18n("转换"), variant="primary") |
| | vc_output4 = gr.Textbox(label=i18n("输出信息")) |
| | but2.click( |
| | uvr, |
| | [ |
| | model_choose, |
| | dir_wav_input, |
| | opt_vocal_root, |
| | wav_inputs, |
| | opt_ins_root, |
| | agg, |
| | format0, |
| | ], |
| | [vc_output4], |
| | api_name="uvr_convert", |
| | ) |
| | with gr.TabItem(i18n("训练")): |
| | gr.Markdown( |
| | value=i18n( |
| | "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. " |
| | ) |
| | ) |
| | with gr.Row(): |
| | exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test") |
| | sr2 = gr.Radio( |
| | label=i18n("目标采样率"), |
| | choices=["40k", "48k"], |
| | value="40k", |
| | interactive=True, |
| | ) |
| | if_f0_3 = gr.Radio( |
| | label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), |
| | choices=[i18n("是"), i18n("否")], |
| | value=i18n("是"), |
| | interactive=True, |
| | ) |
| | version19 = gr.Radio( |
| | label=i18n("版本"), |
| | choices=["v1", "v2"], |
| | value="v2", |
| | interactive=True, |
| | visible=True, |
| | ) |
| | np7 = gr.Slider( |
| | minimum=0, |
| | maximum=config.n_cpu, |
| | step=1, |
| | label=i18n("提取音高和处理数据使用的CPU进程数"), |
| | value=int(np.ceil(config.n_cpu / 1.5)), |
| | interactive=True, |
| | ) |
| | with gr.Group(): |
| | gr.Markdown( |
| | value=i18n( |
| | "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. " |
| | ) |
| | ) |
| | with gr.Row(): |
| | trainset_dir4 = gr.Textbox( |
| | label=i18n("输入训练文件夹路径"), |
| | value=i18n("E:\\语音音频+标注\\米津玄师\\src"), |
| | ) |
| | spk_id5 = gr.Slider( |
| | minimum=0, |
| | maximum=4, |
| | step=1, |
| | label=i18n("请指定说话人id"), |
| | value=0, |
| | interactive=True, |
| | ) |
| | but1 = gr.Button(i18n("处理数据"), variant="primary") |
| | info1 = gr.Textbox(label=i18n("输出信息"), value="") |
| | but1.click( |
| | preprocess_dataset, |
| | [trainset_dir4, exp_dir1, sr2, np7], |
| | [info1], |
| | api_name="train_preprocess", |
| | ) |
| | with gr.Group(): |
| | gr.Markdown( |
| | value=i18n( |
| | "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)" |
| | ) |
| | ) |
| | with gr.Row(): |
| | with gr.Column(): |
| | gpus6 = gr.Textbox( |
| | label=i18n( |
| | "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" |
| | ), |
| | value=gpus, |
| | interactive=True, |
| | visible=F0GPUVisible, |
| | ) |
| | gpu_info9 = gr.Textbox( |
| | label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible |
| | ) |
| | with gr.Column(): |
| | f0method8 = gr.Radio( |
| | label=i18n( |
| | "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" |
| | ), |
| | choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], |
| | value="rmvpe_gpu", |
| | interactive=True, |
| | ) |
| | gpus_rmvpe = gr.Textbox( |
| | label=i18n( |
| | "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" |
| | ), |
| | value="%s-%s" % (gpus, gpus), |
| | interactive=True, |
| | visible=F0GPUVisible, |
| | ) |
| | but2 = gr.Button(i18n("特征提取"), variant="primary") |
| | info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) |
| | f0method8.change( |
| | fn=change_f0_method, |
| | inputs=[f0method8], |
| | outputs=[gpus_rmvpe], |
| | ) |
| | but2.click( |
| | extract_f0_feature, |
| | [ |
| | gpus6, |
| | np7, |
| | f0method8, |
| | if_f0_3, |
| | exp_dir1, |
| | version19, |
| | gpus_rmvpe, |
| | ], |
| | [info2], |
| | api_name="train_extract_f0_feature", |
| | ) |
| | with gr.Group(): |
| | gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) |
| | with gr.Row(): |
| | save_epoch10 = gr.Slider( |
| | minimum=1, |
| | maximum=50, |
| | step=1, |
| | label=i18n("保存频率save_every_epoch"), |
| | value=5, |
| | interactive=True, |
| | ) |
| | total_epoch11 = gr.Slider( |
| | minimum=2, |
| | maximum=1000, |
| | step=1, |
| | label=i18n("总训练轮数total_epoch"), |
| | value=20, |
| | interactive=True, |
| | ) |
| | batch_size12 = gr.Slider( |
| | minimum=1, |
| | maximum=40, |
| | step=1, |
| | label=i18n("每张显卡的batch_size"), |
| | value=default_batch_size, |
| | interactive=True, |
| | ) |
| | if_save_latest13 = gr.Radio( |
| | label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), |
| | choices=[i18n("是"), i18n("否")], |
| | value=i18n("否"), |
| | interactive=True, |
| | ) |
| | if_cache_gpu17 = gr.Radio( |
| | label=i18n( |
| | "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" |
| | ), |
| | choices=[i18n("是"), i18n("否")], |
| | value=i18n("否"), |
| | interactive=True, |
| | ) |
| | if_save_every_weights18 = gr.Radio( |
| | label=i18n( |
| | "是否在每次保存时间点将最终小模型保存至weights文件夹" |
| | ), |
| | choices=[i18n("是"), i18n("否")], |
| | value=i18n("否"), |
| | interactive=True, |
| | ) |
| | with gr.Row(): |
| | pretrained_G14 = gr.Textbox( |
| | label=i18n("加载预训练底模G路径"), |
| | value="assets/pretrained_v2/f0G40k.pth", |
| | interactive=True, |
| | ) |
| | pretrained_D15 = gr.Textbox( |
| | label=i18n("加载预训练底模D路径"), |
| | value="assets/pretrained_v2/f0D40k.pth", |
| | interactive=True, |
| | ) |
| | sr2.change( |
| | change_sr2, |
| | [sr2, if_f0_3, version19], |
| | [pretrained_G14, pretrained_D15], |
| | ) |
| | version19.change( |
| | change_version19, |
| | [sr2, if_f0_3, version19], |
| | [pretrained_G14, pretrained_D15, sr2], |
| | ) |
| | if_f0_3.change( |
| | change_f0, |
| | [if_f0_3, sr2, version19], |
| | [f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15], |
| | ) |
| | gpus16 = gr.Textbox( |
| | label=i18n( |
| | "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" |
| | ), |
| | value=gpus, |
| | interactive=True, |
| | ) |
| | but3 = gr.Button(i18n("训练模型"), variant="primary") |
| | but4 = gr.Button(i18n("训练特征索引"), variant="primary") |
| | but5 = gr.Button(i18n("一键训练"), variant="primary") |
| | info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) |
| | but3.click( |
| | click_train, |
| | [ |
| | exp_dir1, |
| | sr2, |
| | if_f0_3, |
| | spk_id5, |
| | save_epoch10, |
| | total_epoch11, |
| | batch_size12, |
| | if_save_latest13, |
| | pretrained_G14, |
| | pretrained_D15, |
| | gpus16, |
| | if_cache_gpu17, |
| | if_save_every_weights18, |
| | version19, |
| | ], |
| | info3, |
| | api_name="train_start", |
| | ) |
| | but4.click(train_index, [exp_dir1, version19], info3) |
| | but5.click( |
| | train1key, |
| | [ |
| | exp_dir1, |
| | sr2, |
| | if_f0_3, |
| | trainset_dir4, |
| | spk_id5, |
| | np7, |
| | f0method8, |
| | save_epoch10, |
| | total_epoch11, |
| | batch_size12, |
| | if_save_latest13, |
| | pretrained_G14, |
| | pretrained_D15, |
| | gpus16, |
| | if_cache_gpu17, |
| | if_save_every_weights18, |
| | version19, |
| | gpus_rmvpe, |
| | ], |
| | info3, |
| | api_name="train_start_all", |
| | ) |
| |
|
| | with gr.TabItem(i18n("ckpt处理")): |
| | with gr.Group(): |
| | gr.Markdown(value=i18n("模型融合, 可用于测试音色融合")) |
| | with gr.Row(): |
| | ckpt_a = gr.Textbox( |
| | label=i18n("A模型路径"), value="", interactive=True |
| | ) |
| | ckpt_b = gr.Textbox( |
| | label=i18n("B模型路径"), value="", interactive=True |
| | ) |
| | alpha_a = gr.Slider( |
| | minimum=0, |
| | maximum=1, |
| | label=i18n("A模型权重"), |
| | value=0.5, |
| | interactive=True, |
| | ) |
| | with gr.Row(): |
| | sr_ = gr.Radio( |
| | label=i18n("目标采样率"), |
| | choices=["40k", "48k"], |
| | value="40k", |
| | interactive=True, |
| | ) |
| | if_f0_ = gr.Radio( |
| | label=i18n("模型是否带音高指导"), |
| | choices=[i18n("是"), i18n("否")], |
| | value=i18n("是"), |
| | interactive=True, |
| | ) |
| | info__ = gr.Textbox( |
| | label=i18n("要置入的模型信息"), |
| | value="", |
| | max_lines=8, |
| | interactive=True, |
| | ) |
| | name_to_save0 = gr.Textbox( |
| | label=i18n("保存的模型名不带后缀"), |
| | value="", |
| | max_lines=1, |
| | interactive=True, |
| | ) |
| | version_2 = gr.Radio( |
| | label=i18n("模型版本型号"), |
| | choices=["v1", "v2"], |
| | value="v1", |
| | interactive=True, |
| | ) |
| | with gr.Row(): |
| | but6 = gr.Button(i18n("融合"), variant="primary") |
| | info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) |
| | but6.click( |
| | merge, |
| | [ |
| | ckpt_a, |
| | ckpt_b, |
| | alpha_a, |
| | sr_, |
| | if_f0_, |
| | info__, |
| | name_to_save0, |
| | version_2, |
| | ], |
| | info4, |
| | api_name="ckpt_merge", |
| | ) |
| | with gr.Group(): |
| | gr.Markdown( |
| | value=i18n("修改模型信息(仅支持weights文件夹下提取的小模型文件)") |
| | ) |
| | with gr.Row(): |
| | ckpt_path0 = gr.Textbox( |
| | label=i18n("模型路径"), value="", interactive=True |
| | ) |
| | info_ = gr.Textbox( |
| | label=i18n("要改的模型信息"), |
| | value="", |
| | max_lines=8, |
| | interactive=True, |
| | ) |
| | name_to_save1 = gr.Textbox( |
| | label=i18n("保存的文件名, 默认空为和源文件同名"), |
| | value="", |
| | max_lines=8, |
| | interactive=True, |
| | ) |
| | with gr.Row(): |
| | but7 = gr.Button(i18n("修改"), variant="primary") |
| | info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) |
| | but7.click( |
| | change_info, |
| | [ckpt_path0, info_, name_to_save1], |
| | info5, |
| | api_name="ckpt_modify", |
| | ) |
| | with gr.Group(): |
| | gr.Markdown( |
| | value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)") |
| | ) |
| | with gr.Row(): |
| | ckpt_path1 = gr.Textbox( |
| | label=i18n("模型路径"), value="", interactive=True |
| | ) |
| | but8 = gr.Button(i18n("查看"), variant="primary") |
| | info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) |
| | but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show") |
| | with gr.Group(): |
| | gr.Markdown( |
| | value=i18n( |
| | "模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况" |
| | ) |
| | ) |
| | with gr.Row(): |
| | ckpt_path2 = gr.Textbox( |
| | label=i18n("模型路径"), |
| | value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth", |
| | interactive=True, |
| | ) |
| | save_name = gr.Textbox( |
| | label=i18n("保存名"), value="", interactive=True |
| | ) |
| | sr__ = gr.Radio( |
| | label=i18n("目标采样率"), |
| | choices=["32k", "40k", "48k"], |
| | value="40k", |
| | interactive=True, |
| | ) |
| | if_f0__ = gr.Radio( |
| | label=i18n("模型是否带音高指导,1是0否"), |
| | choices=["1", "0"], |
| | value="1", |
| | interactive=True, |
| | ) |
| | version_1 = gr.Radio( |
| | label=i18n("模型版本型号"), |
| | choices=["v1", "v2"], |
| | value="v2", |
| | interactive=True, |
| | ) |
| | info___ = gr.Textbox( |
| | label=i18n("要置入的模型信息"), |
| | value="", |
| | max_lines=8, |
| | interactive=True, |
| | ) |
| | but9 = gr.Button(i18n("提取"), variant="primary") |
| | info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) |
| | ckpt_path2.change( |
| | change_info_, [ckpt_path2], [sr__, if_f0__, version_1] |
| | ) |
| | but9.click( |
| | extract_small_model, |
| | [ckpt_path2, save_name, sr__, if_f0__, info___, version_1], |
| | info7, |
| | api_name="ckpt_extract", |
| | ) |
| |
|
| | with gr.TabItem(i18n("Onnx导出")): |
| | with gr.Row(): |
| | ckpt_dir = gr.Textbox( |
| | label=i18n("RVC模型路径"), value="", interactive=True |
| | ) |
| | with gr.Row(): |
| | onnx_dir = gr.Textbox( |
| | label=i18n("Onnx输出路径"), value="", interactive=True |
| | ) |
| | with gr.Row(): |
| | infoOnnx = gr.Label(label="info") |
| | with gr.Row(): |
| | butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") |
| | butOnnx.click( |
| | export_onnx, [ckpt_dir, onnx_dir], infoOnnx, api_name="export_onnx" |
| | ) |
| |
|
| | tab_faq = i18n("常见问题解答") |
| | with gr.TabItem(tab_faq): |
| | try: |
| | if tab_faq == "常见问题解答": |
| | with open("docs/cn/faq.md", "r", encoding="utf8") as f: |
| | info = f.read() |
| | else: |
| | with open("docs/en/faq_en.md", "r", encoding="utf8") as f: |
| | info = f.read() |
| | gr.Markdown(value=info) |
| | except: |
| | gr.Markdown(traceback.format_exc()) |
| |
|
| | if config.iscolab: |
| | app.queue().launch(share=True, max_threads=511) |
| | else: |
| | app.queue().launch( |
| | max_threads=511, |
| | server_name="0.0.0.0", |
| | inbrowser=not config.noautoopen, |
| | server_port=config.listen_port, |
| | quiet=True, |
| | ) |
| |
|
