| 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, |
| ) |
|
|
