Update app.py

app.py

@@ -76,16 +76,12 @@ with gr.Blocks(title="RVC UI") as app:
         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.")
@@ -101,9 +97,13 @@ with gr.Blocks(title="RVC UI") as app:
         #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])
+        but1 = gr.Button("Process data", variant="primary")
+        but2 = gr.Button("Feature extraction", variant="primary")
         but3 = gr.Button("Train model", variant="primary")
         but4 = gr.Button("Train feature index", variant="primary")
         but5 = gr.Button("One-click training", variant="primary")
+        info1 = gr.Textbox(label="Output information", value="")
+        
         #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)