Update main/app/app.py

main/app/app.py

@@ -830,6 +830,217 @@ with gr.Blocks(title=" Ultimate RVC Maker ⚡", theme=theme) as app:
                     api_name="upload_pretrain_d"
                 )
 
+        with gr.TabItem(translations["training_model"], visible=configs.get("training_tab", True)):
+            gr.Markdown(f"## {translations['training_model']}")
+            with gr.Row():
+                gr.Markdown(translations["training_markdown"])
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        with gr.Column():
+                            training_name = gr.Textbox(label=translations["modelname"], info=translations["training_model_name"], value="", placeholder=translations["modelname"], interactive=True)
+                            training_sr = gr.Radio(label=translations["sample_rate"], info=translations["sample_rate_info"], choices=["32k", "40k", "48k"], value="48k", interactive=True) 
+                            training_ver = gr.Radio(label=translations["training_version"], info=translations["training_version_info"], choices=["v1", "v2"], value="v2", interactive=True) 
+                            with gr.Row():
+                                clean_dataset = gr.Checkbox(label=translations["clear_dataset"], value=False, interactive=True)
+                                preprocess_cut = gr.Checkbox(label=translations["split_audio"], value=True, interactive=True)
+                                process_effects = gr.Checkbox(label=translations["preprocess_effect"], value=False, interactive=True)
+                                checkpointing1 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)
+                                training_f0 = gr.Checkbox(label=translations["training_pitch"], value=True, interactive=True)
+                                upload = gr.Checkbox(label=translations["upload_dataset"], value=False, interactive=True)
+                            with gr.Row():
+                                clean_dataset_strength = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.7, step=0.1, interactive=True, visible=clean_dataset.value)
+                        with gr.Column():
+                            preprocess_button = gr.Button(translations["preprocess_button"], scale=2)
+                            upload_dataset = gr.Files(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"], visible=upload.value)
+                            preprocess_info = gr.Textbox(label=translations["preprocess_info"], value="", interactive=False)
+                with gr.Column():
+                    with gr.Row():
+                        with gr.Column():
+                            with gr.Accordion(label=translations["f0_method"], open=False):
+                                with gr.Group():
+                                    with gr.Row():
+                                        onnx_f0_mode2 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
+                                        unlock_full_method4 = gr.Checkbox(label=translations["f0_unlock"], info=translations["f0_unlock_info"], value=False, interactive=True)
+                                    extract_method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True)
+                                extract_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False)
+                            with gr.Accordion(label=translations["hubert_model"], open=False):
+                                with gr.Group():
+                                    embed_mode2 = gr.Radio(label=translations["embed_mode"], info=translations["embed_mode_info"], value="fairseq", choices=embedders_mode, interactive=True, visible=True)
+                                    extract_embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, value="hubert_base", interactive=True)
+                                with gr.Row():
+                                    extract_embedders_custom = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=extract_embedders.value == "custom")
+                        with gr.Column():
+                            extract_button = gr.Button(translations["extract_button"], scale=2)
+                            extract_info = gr.Textbox(label=translations["extract_info"], value="", interactive=False)
+                with gr.Column():
+                    with gr.Row():
+                        with gr.Column():
+                            total_epochs = gr.Slider(label=translations["total_epoch"], info=translations["total_epoch_info"], minimum=1, maximum=10000, value=300, step=1, interactive=True)
+                            save_epochs = gr.Slider(label=translations["save_epoch"], info=translations["save_epoch_info"], minimum=1, maximum=10000, value=50, step=1, interactive=True)
+                        with gr.Column():
+                            with gr.Row():
+                                index_button = gr.Button(f"3. {translations['create_index']}", variant="primary", scale=2)
+                                training_button = gr.Button(f"4. {translations['training_model']}", variant="primary", scale=2)
+                    with gr.Row():
+                        with gr.Accordion(label=translations["setting"], open=False):
+                            with gr.Row():
+                                index_algorithm = gr.Radio(label=translations["index_algorithm"], info=translations["index_algorithm_info"], choices=["Auto", "Faiss", "KMeans"], value="Auto", interactive=True)
+                            with gr.Row():
+                                custom_dataset = gr.Checkbox(label=translations["custom_dataset"], info=translations["custom_dataset_info"], value=False, interactive=True)
+                                overtraining_detector = gr.Checkbox(label=translations["overtraining_detector"], info=translations["overtraining_detector_info"], value=False, interactive=True)
+                                clean_up = gr.Checkbox(label=translations["cleanup_training"], info=translations["cleanup_training_info"], value=False, interactive=True)
+                                cache_in_gpu = gr.Checkbox(label=translations["cache_in_gpu"], info=translations["cache_in_gpu_info"], value=False, interactive=True)
+                            with gr.Column():
+                                dataset_path = gr.Textbox(label=translations["dataset_folder"], value="dataset", interactive=True, visible=custom_dataset.value)
+                            with gr.Column():
+                                threshold = gr.Slider(minimum=1, maximum=100, value=50, step=1, label=translations["threshold"], interactive=True, visible=overtraining_detector.value)
+                                with gr.Accordion(translations["setting_cpu_gpu"], open=False):
+                                    with gr.Column():
+                                        gpu_number = gr.Textbox(label=translations["gpu_number"], value=str("-".join(map(str, range(torch.cuda.device_count()))) if torch.cuda.is_available() else "-"), info=translations["gpu_number_info"], interactive=True)
+                                        gpu_info = gr.Textbox(label=translations["gpu_info"], value=get_gpu_info(), info=translations["gpu_info_2"], interactive=False)
+                                        cpu_core = gr.Slider(label=translations["cpu_core"], info=translations["cpu_core_info"], minimum=0, maximum=cpu_count(), value=cpu_count(), step=1, interactive=True)          
+                                        train_batch_size = gr.Slider(label=translations["batch_size"], info=translations["batch_size_info"], minimum=1, maximum=64, value=8, step=1, interactive=True)
+                            with gr.Row():
+                                save_only_latest = gr.Checkbox(label=translations["save_only_latest"], info=translations["save_only_latest_info"], value=True, interactive=True)
+                                save_every_weights = gr.Checkbox(label=translations["save_every_weights"], info=translations["save_every_weights_info"], value=True, interactive=True)
+                                not_use_pretrain = gr.Checkbox(label=translations["not_use_pretrain_2"], info=translations["not_use_pretrain_info"], value=False, interactive=True)
+                                custom_pretrain = gr.Checkbox(label=translations["custom_pretrain"], info=translations["custom_pretrain_info"], value=False, interactive=True)
+                            with gr.Row():
+                                vocoders = gr.Radio(label=translations["vocoder"], info=translations["vocoder_info"], choices=["Default", "MRF-HiFi-GAN", "RefineGAN"], value="Default", interactive=True) 
+                            with gr.Row():
+                                deterministic = gr.Checkbox(label=translations["deterministic"], info=translations["deterministic_info"], value=False, interactive=True)
+                                benchmark = gr.Checkbox(label=translations["benchmark"], info=translations["benchmark_info"], value=False, interactive=True)
+                            with gr.Row():
+                                model_author = gr.Textbox(label=translations["training_author"], info=translations["training_author_info"], value="", placeholder=translations["training_author"], interactive=True)
+                            with gr.Row():
+                                with gr.Column():
+                                    with gr.Accordion(translations["custom_pretrain_info"], open=False, visible=custom_pretrain.value and not not_use_pretrain.value) as pretrain_setting:
+                                        pretrained_D = gr.Dropdown(label=translations["pretrain_file"].format(dg="D"), choices=pretrainedD, value=pretrainedD[0] if len(pretrainedD) > 0 else '', interactive=True, allow_custom_value=True)
+                                        pretrained_G = gr.Dropdown(label=translations["pretrain_file"].format(dg="G"), choices=pretrainedG, value=pretrainedG[0] if len(pretrainedG) > 0 else '', interactive=True, allow_custom_value=True)
+                                        refesh_pretrain = gr.Button(translations["refesh"], scale=2)
+                    with gr.Row():
+                        training_info = gr.Textbox(label=translations["train_info"], value="", interactive=False)
+                    with gr.Row():
+                        with gr.Column():
+                            with gr.Accordion(translations["export_model"], open=False):
+                                with gr.Row():
+                                    model_file= gr.Dropdown(label=translations["model_name"], choices=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
+                                    index_file = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", interactive=True, allow_custom_value=True)
+                                with gr.Row():
+                                    refesh_file = gr.Button(f"1. {translations['refesh']}", scale=2)
+                                    zip_model = gr.Button(translations["zip_model"], variant="primary", scale=2)
+                                with gr.Row():
+                                    zip_output = gr.File(label=translations["output_zip"], file_types=[".zip"], interactive=False, visible=False)
+            with gr.Row():
+                vocoders.change(fn=pitch_guidance_lock, inputs=[vocoders], outputs=[training_f0])
+                training_f0.change(fn=vocoders_lock, inputs=[training_f0, vocoders], outputs=[vocoders])
+                unlock_full_method4.change(fn=unlock_f0, inputs=[unlock_full_method4], outputs=[extract_method])
+            with gr.Row():
+                refesh_file.click(fn=change_models_choices, inputs=[], outputs=[model_file, index_file]) 
+                zip_model.click(fn=zip_file, inputs=[training_name, model_file, index_file], outputs=[zip_output])                
+                dataset_path.change(fn=lambda folder: os.makedirs(folder, exist_ok=True), inputs=[dataset_path], outputs=[])
+            with gr.Row():
+                upload.change(fn=visible, inputs=[upload], outputs=[upload_dataset]) 
+                overtraining_detector.change(fn=visible, inputs=[overtraining_detector], outputs=[threshold]) 
+                clean_dataset.change(fn=visible, inputs=[clean_dataset], outputs=[clean_dataset_strength])
+            with gr.Row():
+                custom_dataset.change(fn=lambda custom_dataset: [visible(custom_dataset), "dataset"],inputs=[custom_dataset], outputs=[dataset_path, dataset_path])
+                training_ver.change(fn=unlock_vocoder, inputs=[training_ver, vocoders], outputs=[vocoders])
+                vocoders.change(fn=unlock_ver, inputs=[training_ver, vocoders], outputs=[training_ver])
+                upload_dataset.upload(
+                    fn=lambda files, folder: [shutil.move(f.name, os.path.join(folder, os.path.split(f.name)[1])) for f in files] if folder != "" else gr_warning(translations["dataset_folder1"]),
+                    inputs=[upload_dataset, dataset_path], 
+                    outputs=[], 
+                    api_name="upload_dataset"
+                )           
+            with gr.Row():
+                not_use_pretrain.change(fn=lambda a, b: visible(a and not b), inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrain_setting])
+                custom_pretrain.change(fn=lambda a, b: visible(a and not b), inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrain_setting])
+                refesh_pretrain.click(fn=change_pretrained_choices, inputs=[], outputs=[pretrained_D, pretrained_G])
+            with gr.Row():
+                preprocess_button.click(
+                    fn=preprocess,
+                    inputs=[
+                        training_name, 
+                        training_sr, 
+                        cpu_core,
+                        preprocess_cut, 
+                        process_effects,
+                        dataset_path,
+                        clean_dataset,
+                        clean_dataset_strength
+                    ],
+                    outputs=[preprocess_info],
+                    api_name="preprocess"
+                )
+            with gr.Row():
+                embed_mode2.change(fn=visible_embedders, inputs=[embed_mode2], outputs=[extract_embedders])
+                extract_method.change(fn=hoplength_show, inputs=[extract_method], outputs=[extract_hop_length])
+                extract_embedders.change(fn=lambda extract_embedders: visible(extract_embedders == "custom"), inputs=[extract_embedders], outputs=[extract_embedders_custom])
+            with gr.Row():
+                extract_button.click(
+                    fn=extract,
+                    inputs=[
+                        training_name, 
+                        training_ver, 
+                        extract_method, 
+                        training_f0, 
+                        extract_hop_length, 
+                        cpu_core,
+                        gpu_number,
+                        training_sr, 
+                        extract_embedders, 
+                        extract_embedders_custom,
+                        onnx_f0_mode2,
+                        embed_mode2
+                    ],
+                    outputs=[extract_info],
+                    api_name="extract"
+                )
+            with gr.Row():
+                index_button.click(
+                    fn=create_index,
+                    inputs=[
+                        training_name, 
+                        training_ver, 
+                        index_algorithm
+                    ],
+                    outputs=[training_info],
+                    api_name="create_index"
+                )
+            with gr.Row():
+                training_button.click(
+                    fn=training,
+                    inputs=[
+                        training_name, 
+                        training_ver, 
+                        save_epochs, 
+                        save_only_latest, 
+                        save_every_weights, 
+                        total_epochs, 
+                        training_sr,
+                        train_batch_size, 
+                        gpu_number,
+                        training_f0,
+                        not_use_pretrain,
+                        custom_pretrain,
+                        pretrained_G,
+                        pretrained_D,
+                        overtraining_detector,
+                        threshold,
+                        clean_up,
+                        cache_in_gpu,
+                        model_author,
+                        vocoders,
+                        checkpointing1,
+                        deterministic, 
+                        benchmark
+                    ],
+                    outputs=[training_info],
+                    api_name="training_model"
+                )
+
         with gr.TabItem(translations["audio_editing"], visible=configs.get("audioldm2", True)):
             gr.Markdown(translations["audio_editing_info"])
             with gr.Row():
@@ -1151,217 +1362,6 @@ with gr.Blocks(title=" Ultimate RVC Maker ⚡", theme=theme) as app:
                     api_name="create_dataset"
                 )
 
-        with gr.TabItem(translations["training_model"], visible=configs.get("training_tab", True)):
-            gr.Markdown(f"## {translations['training_model']}")
-            with gr.Row():
-                gr.Markdown(translations["training_markdown"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            training_name = gr.Textbox(label=translations["modelname"], info=translations["training_model_name"], value="", placeholder=translations["modelname"], interactive=True)
-                            training_sr = gr.Radio(label=translations["sample_rate"], info=translations["sample_rate_info"], choices=["32k", "40k", "48k"], value="48k", interactive=True) 
-                            training_ver = gr.Radio(label=translations["training_version"], info=translations["training_version_info"], choices=["v1", "v2"], value="v2", interactive=True) 
-                            with gr.Row():
-                                clean_dataset = gr.Checkbox(label=translations["clear_dataset"], value=False, interactive=True)
-                                preprocess_cut = gr.Checkbox(label=translations["split_audio"], value=True, interactive=True)
-                                process_effects = gr.Checkbox(label=translations["preprocess_effect"], value=False, interactive=True)
-                                checkpointing1 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)
-                                training_f0 = gr.Checkbox(label=translations["training_pitch"], value=True, interactive=True)
-                                upload = gr.Checkbox(label=translations["upload_dataset"], value=False, interactive=True)
-                            with gr.Row():
-                                clean_dataset_strength = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.7, step=0.1, interactive=True, visible=clean_dataset.value)
-                        with gr.Column():
-                            preprocess_button = gr.Button(translations["preprocess_button"], scale=2)
-                            upload_dataset = gr.Files(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"], visible=upload.value)
-                            preprocess_info = gr.Textbox(label=translations["preprocess_info"], value="", interactive=False)
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            with gr.Accordion(label=translations["f0_method"], open=False):
-                                with gr.Group():
-                                    with gr.Row():
-                                        onnx_f0_mode2 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
-                                        unlock_full_method4 = gr.Checkbox(label=translations["f0_unlock"], info=translations["f0_unlock_info"], value=False, interactive=True)
-                                    extract_method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True)
-                                extract_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False)
-                            with gr.Accordion(label=translations["hubert_model"], open=False):
-                                with gr.Group():
-                                    embed_mode2 = gr.Radio(label=translations["embed_mode"], info=translations["embed_mode_info"], value="fairseq", choices=embedders_mode, interactive=True, visible=True)
-                                    extract_embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, value="hubert_base", interactive=True)
-                                with gr.Row():
-                                    extract_embedders_custom = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=extract_embedders.value == "custom")
-                        with gr.Column():
-                            extract_button = gr.Button(translations["extract_button"], scale=2)
-                            extract_info = gr.Textbox(label=translations["extract_info"], value="", interactive=False)
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            total_epochs = gr.Slider(label=translations["total_epoch"], info=translations["total_epoch_info"], minimum=1, maximum=10000, value=300, step=1, interactive=True)
-                            save_epochs = gr.Slider(label=translations["save_epoch"], info=translations["save_epoch_info"], minimum=1, maximum=10000, value=50, step=1, interactive=True)
-                        with gr.Column():
-                            with gr.Row():
-                                index_button = gr.Button(f"3. {translations['create_index']}", variant="primary", scale=2)
-                                training_button = gr.Button(f"4. {translations['training_model']}", variant="primary", scale=2)
-                    with gr.Row():
-                        with gr.Accordion(label=translations["setting"], open=False):
-                            with gr.Row():
-                                index_algorithm = gr.Radio(label=translations["index_algorithm"], info=translations["index_algorithm_info"], choices=["Auto", "Faiss", "KMeans"], value="Auto", interactive=True)
-                            with gr.Row():
-                                custom_dataset = gr.Checkbox(label=translations["custom_dataset"], info=translations["custom_dataset_info"], value=False, interactive=True)
-                                overtraining_detector = gr.Checkbox(label=translations["overtraining_detector"], info=translations["overtraining_detector_info"], value=False, interactive=True)
-                                clean_up = gr.Checkbox(label=translations["cleanup_training"], info=translations["cleanup_training_info"], value=False, interactive=True)
-                                cache_in_gpu = gr.Checkbox(label=translations["cache_in_gpu"], info=translations["cache_in_gpu_info"], value=False, interactive=True)
-                            with gr.Column():
-                                dataset_path = gr.Textbox(label=translations["dataset_folder"], value="dataset", interactive=True, visible=custom_dataset.value)
-                            with gr.Column():
-                                threshold = gr.Slider(minimum=1, maximum=100, value=50, step=1, label=translations["threshold"], interactive=True, visible=overtraining_detector.value)
-                                with gr.Accordion(translations["setting_cpu_gpu"], open=False):
-                                    with gr.Column():
-                                        gpu_number = gr.Textbox(label=translations["gpu_number"], value=str("-".join(map(str, range(torch.cuda.device_count()))) if torch.cuda.is_available() else "-"), info=translations["gpu_number_info"], interactive=True)
-                                        gpu_info = gr.Textbox(label=translations["gpu_info"], value=get_gpu_info(), info=translations["gpu_info_2"], interactive=False)
-                                        cpu_core = gr.Slider(label=translations["cpu_core"], info=translations["cpu_core_info"], minimum=0, maximum=cpu_count(), value=cpu_count(), step=1, interactive=True)          
-                                        train_batch_size = gr.Slider(label=translations["batch_size"], info=translations["batch_size_info"], minimum=1, maximum=64, value=8, step=1, interactive=True)
-                            with gr.Row():
-                                save_only_latest = gr.Checkbox(label=translations["save_only_latest"], info=translations["save_only_latest_info"], value=True, interactive=True)
-                                save_every_weights = gr.Checkbox(label=translations["save_every_weights"], info=translations["save_every_weights_info"], value=True, interactive=True)
-                                not_use_pretrain = gr.Checkbox(label=translations["not_use_pretrain_2"], info=translations["not_use_pretrain_info"], value=False, interactive=True)
-                                custom_pretrain = gr.Checkbox(label=translations["custom_pretrain"], info=translations["custom_pretrain_info"], value=False, interactive=True)
-                            with gr.Row():
-                                vocoders = gr.Radio(label=translations["vocoder"], info=translations["vocoder_info"], choices=["Default", "MRF-HiFi-GAN", "RefineGAN"], value="Default", interactive=True) 
-                            with gr.Row():
-                                deterministic = gr.Checkbox(label=translations["deterministic"], info=translations["deterministic_info"], value=False, interactive=True)
-                                benchmark = gr.Checkbox(label=translations["benchmark"], info=translations["benchmark_info"], value=False, interactive=True)
-                            with gr.Row():
-                                model_author = gr.Textbox(label=translations["training_author"], info=translations["training_author_info"], value="", placeholder=translations["training_author"], interactive=True)
-                            with gr.Row():
-                                with gr.Column():
-                                    with gr.Accordion(translations["custom_pretrain_info"], open=False, visible=custom_pretrain.value and not not_use_pretrain.value) as pretrain_setting:
-                                        pretrained_D = gr.Dropdown(label=translations["pretrain_file"].format(dg="D"), choices=pretrainedD, value=pretrainedD[0] if len(pretrainedD) > 0 else '', interactive=True, allow_custom_value=True)
-                                        pretrained_G = gr.Dropdown(label=translations["pretrain_file"].format(dg="G"), choices=pretrainedG, value=pretrainedG[0] if len(pretrainedG) > 0 else '', interactive=True, allow_custom_value=True)
-                                        refesh_pretrain = gr.Button(translations["refesh"], scale=2)
-                    with gr.Row():
-                        training_info = gr.Textbox(label=translations["train_info"], value="", interactive=False)
-                    with gr.Row():
-                        with gr.Column():
-                            with gr.Accordion(translations["export_model"], open=False):
-                                with gr.Row():
-                                    model_file= gr.Dropdown(label=translations["model_name"], choices=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
-                                    index_file = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", interactive=True, allow_custom_value=True)
-                                with gr.Row():
-                                    refesh_file = gr.Button(f"1. {translations['refesh']}", scale=2)
-                                    zip_model = gr.Button(translations["zip_model"], variant="primary", scale=2)
-                                with gr.Row():
-                                    zip_output = gr.File(label=translations["output_zip"], file_types=[".zip"], interactive=False, visible=False)
-            with gr.Row():
-                vocoders.change(fn=pitch_guidance_lock, inputs=[vocoders], outputs=[training_f0])
-                training_f0.change(fn=vocoders_lock, inputs=[training_f0, vocoders], outputs=[vocoders])
-                unlock_full_method4.change(fn=unlock_f0, inputs=[unlock_full_method4], outputs=[extract_method])
-            with gr.Row():
-                refesh_file.click(fn=change_models_choices, inputs=[], outputs=[model_file, index_file]) 
-                zip_model.click(fn=zip_file, inputs=[training_name, model_file, index_file], outputs=[zip_output])                
-                dataset_path.change(fn=lambda folder: os.makedirs(folder, exist_ok=True), inputs=[dataset_path], outputs=[])
-            with gr.Row():
-                upload.change(fn=visible, inputs=[upload], outputs=[upload_dataset]) 
-                overtraining_detector.change(fn=visible, inputs=[overtraining_detector], outputs=[threshold]) 
-                clean_dataset.change(fn=visible, inputs=[clean_dataset], outputs=[clean_dataset_strength])
-            with gr.Row():
-                custom_dataset.change(fn=lambda custom_dataset: [visible(custom_dataset), "dataset"],inputs=[custom_dataset], outputs=[dataset_path, dataset_path])
-                training_ver.change(fn=unlock_vocoder, inputs=[training_ver, vocoders], outputs=[vocoders])
-                vocoders.change(fn=unlock_ver, inputs=[training_ver, vocoders], outputs=[training_ver])
-                upload_dataset.upload(
-                    fn=lambda files, folder: [shutil.move(f.name, os.path.join(folder, os.path.split(f.name)[1])) for f in files] if folder != "" else gr_warning(translations["dataset_folder1"]),
-                    inputs=[upload_dataset, dataset_path], 
-                    outputs=[], 
-                    api_name="upload_dataset"
-                )           
-            with gr.Row():
-                not_use_pretrain.change(fn=lambda a, b: visible(a and not b), inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrain_setting])
-                custom_pretrain.change(fn=lambda a, b: visible(a and not b), inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrain_setting])
-                refesh_pretrain.click(fn=change_pretrained_choices, inputs=[], outputs=[pretrained_D, pretrained_G])
-            with gr.Row():
-                preprocess_button.click(
-                    fn=preprocess,
-                    inputs=[
-                        training_name, 
-                        training_sr, 
-                        cpu_core,
-                        preprocess_cut, 
-                        process_effects,
-                        dataset_path,
-                        clean_dataset,
-                        clean_dataset_strength
-                    ],
-                    outputs=[preprocess_info],
-                    api_name="preprocess"
-                )
-            with gr.Row():
-                embed_mode2.change(fn=visible_embedders, inputs=[embed_mode2], outputs=[extract_embedders])
-                extract_method.change(fn=hoplength_show, inputs=[extract_method], outputs=[extract_hop_length])
-                extract_embedders.change(fn=lambda extract_embedders: visible(extract_embedders == "custom"), inputs=[extract_embedders], outputs=[extract_embedders_custom])
-            with gr.Row():
-                extract_button.click(
-                    fn=extract,
-                    inputs=[
-                        training_name, 
-                        training_ver, 
-                        extract_method, 
-                        training_f0, 
-                        extract_hop_length, 
-                        cpu_core,
-                        gpu_number,
-                        training_sr, 
-                        extract_embedders, 
-                        extract_embedders_custom,
-                        onnx_f0_mode2,
-                        embed_mode2
-                    ],
-                    outputs=[extract_info],
-                    api_name="extract"
-                )
-            with gr.Row():
-                index_button.click(
-                    fn=create_index,
-                    inputs=[
-                        training_name, 
-                        training_ver, 
-                        index_algorithm
-                    ],
-                    outputs=[training_info],
-                    api_name="create_index"
-                )
-            with gr.Row():
-                training_button.click(
-                    fn=training,
-                    inputs=[
-                        training_name, 
-                        training_ver, 
-                        save_epochs, 
-                        save_only_latest, 
-                        save_every_weights, 
-                        total_epochs, 
-                        training_sr,
-                        train_batch_size, 
-                        gpu_number,
-                        training_f0,
-                        not_use_pretrain,
-                        custom_pretrain,
-                        pretrained_G,
-                        pretrained_D,
-                        overtraining_detector,
-                        threshold,
-                        clean_up,
-                        cache_in_gpu,
-                        model_author,
-                        vocoders,
-                        checkpointing1,
-                        deterministic, 
-                        benchmark
-                    ],
-                    outputs=[training_info],
-                    api_name="training_model"
-                )
-
         with gr.TabItem(translations["fushion"], visible=configs.get("fushion_tab", True)):
             gr.Markdown(translations["fushion_markdown"])
             with gr.Row():