Update app.py

app.py

@@ -21,7 +21,8 @@ with gr.Blocks(title="RVC UI") as app:
     
     with gr.Tabs():
         models = gr.Dropdown(label="voice model", choices=sorted(names))
-        clean_button = gr.Button("Refresh model", variant="primary")
+        with gr.Row():
+            clean_button = gr.Button("Refresh model", variant="primary")
         with gr.Row():
             with gr.Row():
                 pith_voice = gr.Number(label="Transpose 12 for femal, -12 fo male)", value=0)
@@ -63,53 +64,48 @@ with gr.Blocks(title="RVC UI") as app:
             but1 = gr.Button("Convert", variant="primary")
             vc_output3 = gr.Textbox(label="Output information", interactive=False)
     
-    with gr.TabItem("Train"):
-        gr.Markdown("### Step 1. Fill in the experimental configuration.\nExperimental data is stored in the 'logs' folder, with each experiment having a separate folder. Manually enter the experiment name path, which contains the experimental configuration, logs, and trained model files.")
-        exp_dir1 = gr.Textbox(label="Enter the experiment name", value="my voice")
-        author = gr.Textbox(label="Model Author (Nullable)")
-        #np7 = gr.Slider(minimum=0, maximum=config.n_cpu, step=1, label="Number of CPU processes used for pitch extraction and data processing", value=int(np.ceil(config.n_cpu / 1.5)), interactive=True)
-        sr2 = gr.Radio(label="Target sample rate", choices=["40k", "48k"], value="40k", interactive=True)
-        if_f0_3 = gr.Radio(label="Whether the model has pitch guidance (required for singing, optional for speech)", choices=["Yes", "No"], value="Yes", interactive=True)
-        version19 = gr.Radio(label="Version", choices=["v1", "v2"], value="v2", interactive=True, visible=True)
-        gr.Markdown("### Step 2. Audio processing. \n#### 1. Slicing.\nAutomatically traverse all files in the training folder that can be decoded into audio and perform slice normalization. Generates 2 wav folders in the experiment directory. Currently, only single-singer/speaker training is supported.")
-            
-        trainset_dir4 = gr.Textbox(label="Enter the path of the training folder")
-        spk_id5 = gr.Slider(minimum=0, maximum=4, step=1, label="Please specify the speaker/singer ID", value=0, interactive=True)
-        but1 = gr.Button("Process data", variant="primary")
-                
-        info1 = gr.Textbox(label="Output information", value="")
-        #but1.click(preprocess_dataset,[trainset_dir4, exp_dir1, sr2, np7],[info1],api_name="train_preprocess")
-        gr.Markdown("#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index).")
-            
-        #gpu_info9 = gr.Textbox(label="GPU Information",value=gpu_info,visible=F0GPUVisible)
-        #gpus6 = gr.Textbox(label="Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2",value=gpus,interactive=True,visible=F0GPUVisible)
-        #gpus_rmvpe = gr.Textbox(label="Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1",value="%s-%s" % (gpus, gpus),interactive=True,visible=F0GPUVisible)
-        f0method8 = gr.Radio(label="Select the pitch extraction algorithm: when extracting singing, you can use 'pm' to speed up. For high-quality speech with fast performance, but worse CPU usage, you can use 'dio'. 'harvest' results in better quality but is slower. 'rmvpe' has the best results and consumes less CPU/GPU", choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], value="rmvpe_gpu", interactive=True)
-                
-        but2 = gr.Button("Feature extraction", variant="primary")
-        info2 = gr.Textbox(label="Output information", value="")
-        #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")
-        gr.Markdown("### Step 3. Start training.\nFill in the training settings and start training the model and index.")
-        save_epoch10 = gr.Slider(minimum=1, maximum=50, step=1, label="Save frequency (save_every_epoch)", value=5, interactive=True)
-        total_epoch11 = gr.Slider(minimum=2, maximum=1000, step=1, label="Total training epochs (total_epoch)", value=20, interactive=True)
-        batch_size12 = gr.Slider(minimum=1, maximum=40, step=1, label="Batch size per GPU", value=20, interactive=True)
-        if_save_latest13 = gr.Radio(label="Save only the latest '.ckpt' file to save disk space", choices=["Yes", "No"], value="No", interactive=True)
-        if_cache_gpu17 = gr.Radio(label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement", choices=["Yes", "No"], value="No", interactive=True)
-        if_save_every_weights18 = gr.Radio(label="Save a small final model to the 'weights' folder at each save point", choices=["Yes","No"], value="No", interactive=True)
-                
-        pretrained_G14 = gr.Textbox(label="Load pre-trained base model G path", value="assets/pretrained_v2/f0G40k.pth", interactive=True)
-        pretrained_D15 = gr.Textbox(label="Load pre-trained base model D path", value="assets/pretrained_v2/f0D40k.pth", interactive=True)
-        gpus16 = gr.Textbox(label="Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2", value="0", 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(fn=lambda: None, inputs=[if_f0_3, sr2, version19], outputs=[f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15])
-        but3 = gr.Button("Train model", variant="primary")
-        but4 = gr.Button("Train feature index", variant="primary")
-        but5 = gr.Button("One-click training", variant="primary")
-        #info3 = gr.Textbox(label=i18n("Output information"), value="")
-        #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,author,],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,author],info3,api_name="train_start_all")
+        with gr.TabItem("Train"):
+            gr.Markdown("### Step 1. Fill in the experimental configuration.\nExperimental data is stored in the 'logs' folder, with each experiment having a separate folder. Manually enter the experiment name path, which contains the experimental configuration, logs, and trained model files.")
+            exp_dir1 = gr.Textbox(label="Enter the experiment name", value="my voice")
+            author = gr.Textbox(label="Model Author (Nullable)")
+            #np7 = gr.Slider(minimum=0, maximum=config.n_cpu, step=1, label="Number of CPU processes used for pitch extraction and data processing", value=int(np.ceil(config.n_cpu / 1.5)), interactive=True)
+            sr2 = gr.Radio(label="Target sample rate", choices=["40k", "48k"], value="40k", interactive=True)
+            if_f0_3 = gr.Radio(label="Whether the model has pitch guidance (required for singing, optional for speech)", choices=["Yes", "No"], value="Yes", interactive=True)
+            version19 = gr.Radio(label="Version", choices=["v1", "v2"], value="v2", interactive=True, visible=True)
+            gr.Markdown("### Step 2. Audio processing. \n#### 1. Slicing.\nAutomatically traverse all files in the training folder that can be decoded into audio and perform slice normalization. Generates 2 wav folders in the experiment directory. Currently, only single-singer/speaker training is supported.")
+            trainset_dir4 = gr.Textbox(label="Enter the path of the training folder")
+            spk_id5 = gr.Slider(minimum=0, maximum=4, step=1, label="Please specify the speaker/singer ID", value=0, interactive=True)
+            but1 = gr.Button("Process data", variant="primary")
+            info1 = gr.Textbox(label="Output information", value="")
+            #but1.click(preprocess_dataset,[trainset_dir4, exp_dir1, sr2, np7],[info1],api_name="train_preprocess")
+            gr.Markdown("#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index).")
+            #gpu_info9 = gr.Textbox(label="GPU Information",value=gpu_info,visible=F0GPUVisible)
+            #gpus6 = gr.Textbox(label="Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2",value=gpus,interactive=True,visible=F0GPUVisible)
+            #gpus_rmvpe = gr.Textbox(label="Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1",value="%s-%s" % (gpus, gpus),interactive=True,visible=F0GPUVisible)
+            f0method8 = gr.Radio(label="Select the pitch extraction algorithm: when extracting singing, you can use 'pm' to speed up. For high-quality speech with fast performance, but worse CPU usage, you can use 'dio'. 'harvest' results in better quality but is slower. 'rmvpe' has the best results and consumes less CPU/GPU", choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], value="rmvpe_gpu", interactive=True)
+            but2 = gr.Button("Feature extraction", variant="primary")
+            info2 = gr.Textbox(label="Output information", value="")
+            #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")
+            gr.Markdown("### Step 3. Start training.\nFill in the training settings and start training the model and index.")
+            save_epoch10 = gr.Slider(minimum=1, maximum=50, step=1, label="Save frequency (save_every_epoch)", value=5, interactive=True)
+            total_epoch11 = gr.Slider(minimum=2, maximum=1000, step=1, label="Total training epochs (total_epoch)", value=20, interactive=True)
+            batch_size12 = gr.Slider(minimum=1, maximum=40, step=1, label="Batch size per GPU", value=20, interactive=True)
+            if_save_latest13 = gr.Radio(label="Save only the latest '.ckpt' file to save disk space", choices=["Yes", "No"], value="No", interactive=True)
+            if_cache_gpu17 = gr.Radio(label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement", choices=["Yes", "No"], value="No", interactive=True)
+            if_save_every_weights18 = gr.Radio(label="Save a small final model to the 'weights' folder at each save point", choices=["Yes","No"], value="No", interactive=True)               
+            pretrained_G14 = gr.Textbox(label="Load pre-trained base model G path", value="assets/pretrained_v2/f0G40k.pth", interactive=True)
+            pretrained_D15 = gr.Textbox(label="Load pre-trained base model D path", value="assets/pretrained_v2/f0D40k.pth", interactive=True)
+            gpus16 = gr.Textbox(label="Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2", value="0", 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(fn=lambda: None, inputs=[if_f0_3, sr2, version19], outputs=[f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15])
+            but3 = gr.Button("Train model", variant="primary")
+            but4 = gr.Button("Train feature index", variant="primary")
+            but5 = gr.Button("One-click training", variant="primary")
+            #info3 = gr.Textbox(label=i18n("Output information"), value="")
+            #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,author,],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,author],info3,api_name="train_start_all")
 
-app.launch()
+app.launch(view_api=False)