Update app.py

app.py

@@ -63,5 +63,196 @@ with gr.Blocks(title="RVC UI") as app:
             format1 = gr.Radio(label="Export file format",choices=["wav", "flac", "mp3", "m4a"],value="wav",interactive=True)
             but1 = gr.Button("Convert", variant="primary")
             vc_output3 = gr.Textbox(label="Output information",interactive=False)
-    
+    with gr.Tabs():
+        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.")
+            with gr.Row():
+                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)
+            with gr.Row():
+                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.")
+            with gr.Row():
+                with gr.Column():
+                    trainset_dir4 = gr.Textbox(
+                        label=i18n("Enter the path of the training folder"),
+                    )
+                    spk_id5 = gr.Slider(
+                        minimum=0,
+                        maximum=4,
+                        step=1,
+                        label=i18n("Please specify the speaker/singer ID"),
+                        value=0,
+                        interactive=True,
+                    )
+                    but1 = gr.Button(i18n("Process data"), variant="primary")
+                with gr.Column():
+                    info1 = gr.Textbox(label=i18n("Output information"), value="")
+                    but1.click(
+                        preprocess_dataset,
+                        [trainset_dir4, exp_dir1, sr2, np7],
+                        [info1],
+                        api_name="train_preprocess",
+                    )
+                    gr.Markdown(
+                value=i18n(
+                    "#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index)."
+                )
+            )
+            with gr.Row():
+                with gr.Column():
+                    gpu_info9 = gr.Textbox(
+                        label=i18n("GPU Information"),
+                        value=gpu_info,
+                        visible=F0GPUVisible,
+                    )
+                    gpus6 = gr.Textbox(
+                        label=i18n(
+                            "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=i18n(
+                            "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=i18n(
+                            "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,
+                    )
+                with gr.Column():
+                    but2 = gr.Button(i18n("Feature extraction"), variant="primary")
+                    info2 = gr.Textbox(label=i18n("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(
+                value=i18n(
+                    "### Step 3. Start training.\nFill in the training settings and start training the model and index."
+                )
+            )
+            with gr.Row():
+                with gr.Column():
+                    save_epoch10 = gr.Slider(
+                        minimum=1,
+                        maximum=50,
+                        step=1,
+                        label=i18n("Save frequency (save_every_epoch)"),
+                        value=5,
+                        interactive=True,
+                    )
+                    total_epoch11 = gr.Slider(
+                        minimum=2,
+                        maximum=1000,
+                        step=1,
+                        label=i18n("Total training epochs (total_epoch)"),
+                        value=20,
+                        interactive=True,
+                    )
+                    batch_size12 = gr.Slider(
+                        minimum=1,
+                        maximum=40,
+                        step=1,
+                        label=i18n("Batch size per GPU"),
+                        value=default_batch_size,
+                        interactive=True,
+                    )
+                    if_save_latest13 = gr.Radio(
+                        label=i18n(
+                            "Save only the latest '.ckpt' file to save disk space"
+                        ),
+                        choices=[i18n("Yes"), i18n("No")],
+                        value=i18n("No"),
+                        interactive=True,
+                    )
+                    if_cache_gpu17 = gr.Radio(
+                        label=i18n(
+                            "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=[i18n("Yes"), i18n("No")],
+                        value=i18n("No"),
+                        interactive=True,
+                    )
+                    if_save_every_weights18 = gr.Radio(
+                        label=i18n(
+                            "Save a small final model to the 'weights' folder at each save point"
+                        ),
+                        choices=[i18n("Yes"), i18n("No")],
+                        value=i18n("No"),
+                        interactive=True,
+                    )
+                with gr.Column():
+                    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=i18n("Load pre-trained base model D path"),
+                        value="assets/pretrained_v2/f0D40k.pth",
+                        interactive=True,
+                    )
+                    gpus16 = gr.Textbox(
+                        label=i18n(
+                            "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2"
+                        ),
+                        value=gpus,
+                        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],
+                    )
+
+                    but3 = gr.Button("Train model"), variant="primary")
+                    but4 = gr.Button("Train feature index"), variant="primary")
+                    but5 = gr.Button("One-click training"), variant="primary")
+            with gr.Row():
+                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()