Update main/app/app.py

main/app/app.py

@@ -25,6 +25,8 @@ import soundfile as sf
 from time import sleep
 from multiprocessing import cpu_count
 from main.app.tabs.inference.inference import inference_tabs
+from main.app.tabs.models.model import model_tabs
+
 sys.path.append(os.getcwd())
 
 from main.tools import huggingface
@@ -143,765 +145,10 @@ with gr.Blocks(title=" Ultimate RVC Maker ⚡", theme=theme) as app:
             inference_tabs()
         
         
-        with gr.TabItem(translations["downloads"], visible=configs.get("downloads_tab", True)):
-            gr.Markdown(translations["download_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["download_markdown_2"])
-            with gr.Row():
-                with gr.Accordion(translations["model_download"], open=True):
-                    with gr.Row():
-                        downloadmodel = gr.Radio(label=translations["model_download_select"], choices=[translations["download_url"], translations["download_from_csv"], translations["search_models"], translations["upload"]], interactive=True, value=translations["download_url"])
-                    with gr.Row():
-                        gr.Markdown("___")
-                    with gr.Column():
-                        with gr.Row():
-                            url_input = gr.Textbox(label=translations["model_url"], value="", placeholder="https://...", scale=6)
-                            download_model_name = gr.Textbox(label=translations["modelname"], value="", placeholder=translations["modelname"], scale=2)
-                        url_download = gr.Button(value=translations["downloads"], scale=2)
-                    with gr.Column():
-                        model_browser = gr.Dropdown(choices=models.keys(), label=translations["model_warehouse"], scale=8, allow_custom_value=True, visible=False)
-                        download_from_browser = gr.Button(value=translations["get_model"], scale=2, variant="primary", visible=False)
-                    with gr.Column():
-                        search_name = gr.Textbox(label=translations["name_to_search"], placeholder=translations["modelname"], interactive=True, scale=8, visible=False)
-                        search = gr.Button(translations["search_2"], scale=2, visible=False)
-                        search_dropdown = gr.Dropdown(label=translations["select_download_model"], value="", choices=[], allow_custom_value=True, interactive=False, visible=False)
-                        download = gr.Button(translations["downloads"], variant="primary", visible=False)
-                    with gr.Column():
-                        model_upload = gr.File(label=translations["drop_model"], file_types=[".pth", ".onnx", ".index", ".zip"], visible=False)
-            with gr.Row():
-                with gr.Accordion(translations["download_pretrained_2"], open=False):
-                    with gr.Row():
-                        pretrain_download_choices = gr.Radio(label=translations["model_download_select"], choices=[translations["download_url"], translations["list_model"], translations["upload"]], value=translations["download_url"], interactive=True)  
-                    with gr.Row():
-                        gr.Markdown("___")
-                    with gr.Column():
-                        with gr.Row():
-                            pretrainD = gr.Textbox(label=translations["pretrained_url"].format(dg="D"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4)
-                            pretrainG = gr.Textbox(label=translations["pretrained_url"].format(dg="G"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4)
-                        download_pretrain_button = gr.Button(translations["downloads"], scale=2)
-                    with gr.Column():
-                        with gr.Row():
-                            pretrain_choices = gr.Dropdown(label=translations["select_pretrain"], info=translations["select_pretrain_info"], choices=list(fetch_pretrained_data().keys()), value="Titan_Medium", allow_custom_value=True, interactive=True, scale=6, visible=False)
-                            sample_rate_pretrain = gr.Dropdown(label=translations["pretrain_sr"], info=translations["pretrain_sr"], choices=["48k", "40k", "32k"], value="48k", interactive=True, visible=False)
-                        download_pretrain_choices_button = gr.Button(translations["downloads"], scale=2, variant="primary", visible=False)
-                    with gr.Row():
-                        pretrain_upload_g = gr.File(label=translations["drop_pretrain"].format(dg="G"), file_types=[".pth"], visible=False)
-                        pretrain_upload_d = gr.File(label=translations["drop_pretrain"].format(dg="D"), file_types=[".pth"], visible=False)
-            with gr.Row():
-                url_download.click(
-                    fn=download_model, 
-                    inputs=[
-                        url_input, 
-                        download_model_name
-                    ], 
-                    outputs=[url_input],
-                    api_name="download_model"
-                )
-                download_from_browser.click(
-                    fn=lambda model: download_model(models[model], model), 
-                    inputs=[model_browser], 
-                    outputs=[model_browser],
-                    api_name="download_browser"
-                )
-            with gr.Row():
-                downloadmodel.change(fn=change_download_choices, inputs=[downloadmodel], outputs=[url_input, download_model_name, url_download, model_browser, download_from_browser, search_name, search, search_dropdown, download, model_upload])
-                search.click(fn=search_models, inputs=[search_name], outputs=[search_dropdown, download])
-                model_upload.upload(fn=save_drop_model, inputs=[model_upload], outputs=[model_upload])
-                download.click(
-                    fn=lambda model: download_model(model_options[model], model), 
-                    inputs=[search_dropdown], 
-                    outputs=[search_dropdown],
-                    api_name="search_models"
-                )
-            with gr.Row():
-                pretrain_download_choices.change(fn=change_download_pretrained_choices, inputs=[pretrain_download_choices], outputs=[pretrainD, pretrainG, download_pretrain_button, pretrain_choices, sample_rate_pretrain, download_pretrain_choices_button, pretrain_upload_d, pretrain_upload_g])
-                pretrain_choices.change(fn=update_sample_rate_dropdown, inputs=[pretrain_choices], outputs=[sample_rate_pretrain])
-            with gr.Row():
-                download_pretrain_button.click(
-                    fn=download_pretrained_model,
-                    inputs=[
-                        pretrain_download_choices, 
-                        pretrainD, 
-                        pretrainG
-                    ],
-                    outputs=[pretrainD],
-                    api_name="download_pretrain_link"
-                )
-                download_pretrain_choices_button.click(
-                    fn=download_pretrained_model,
-                    inputs=[
-                        pretrain_download_choices, 
-                        pretrain_choices, 
-                        sample_rate_pretrain
-                    ],
-                    outputs=[pretrain_choices],
-                    api_name="download_pretrain_choices"
-                )
-                pretrain_upload_g.upload(
-                    fn=lambda pretrain_upload_g: shutil.move(pretrain_upload_g.name, os.path.join("assets", "models", "pretrained_custom")), 
-                    inputs=[pretrain_upload_g], 
-                    outputs=[],
-                    api_name="upload_pretrain_g"
-                )
-                pretrain_upload_d.upload(
-                    fn=lambda pretrain_upload_d: shutil.move(pretrain_upload_d.name, os.path.join("assets", "models", "pretrained_custom")), 
-                    inputs=[pretrain_upload_d], 
-                    outputs=[],
-                    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("Model Options"):
+            model_tabs()
         
         
-        with gr.TabItem(translations["audio_editing"], visible=configs.get("audioldm2", True)):
-            gr.Markdown(translations["audio_editing_info"])
-            with gr.Row():
-                gr.Markdown(translations["audio_editing_markdown"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Group():
-                        with gr.Row():
-                            save_compute = gr.Checkbox(label=translations["save_compute"], value=True, interactive=True)
-                        tar_prompt = gr.Textbox(label=translations["target_prompt"], info=translations["target_prompt_info"], placeholder="Piano and violin cover", lines=5, interactive=True)
-                with gr.Column():
-                    cfg_scale_src = gr.Slider(value=3, minimum=0.5, maximum=25, label=translations["cfg_scale_src"], info=translations["cfg_scale_src_info"], interactive=True)
-                    cfg_scale_tar = gr.Slider(value=12, minimum=0.5, maximum=25, label=translations["cfg_scale_tar"], info=translations["cfg_scale_tar_info"], interactive=True)
-            with gr.Row():
-                edit_button = gr.Button(translations["editing"], variant="primary")
-            with gr.Row():
-                with gr.Column():
-                    drop_audio_file = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])  
-                    display_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
-                with gr.Column():
-                    with gr.Accordion(translations["input_output"], open=False):
-                        with gr.Column():
-                            export_audio_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True)
-                            input_audiopath = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, info=translations["provide_audio"], allow_custom_value=True, interactive=True)
-                            output_audiopath = gr.Textbox(label=translations["output_path"], value="audios/output.wav", placeholder="audios/output.wav", info=translations["output_path_info"], interactive=True)
-                        with gr.Column():
-                            refesh_audio = gr.Button(translations["refesh"])
-                    with gr.Accordion(translations["setting"], open=False):
-                        audioldm2_model = gr.Radio(label=translations["audioldm2_model"], info=translations["audioldm2_model_info"], choices=["audioldm2", "audioldm2-large", "audioldm2-music"], value="audioldm2-music", interactive=True)
-                        with gr.Row():
-                            src_prompt = gr.Textbox(label=translations["source_prompt"], lines=2, interactive=True, info=translations["source_prompt_info"], placeholder="A recording of a happy upbeat classical music piece")
-                        with gr.Row():
-                            with gr.Column(): 
-                                audioldm2_sample_rate = gr.Slider(minimum=8000, maximum=96000, label=translations["sr"], info=translations["sr_info"], value=44100, step=1, interactive=True)
-                                t_start = gr.Slider(minimum=15, maximum=85, value=45, step=1, label=translations["t_start"], interactive=True, info=translations["t_start_info"])
-                                steps = gr.Slider(value=50, step=1, minimum=10, maximum=300, label=translations["steps_label"], info=translations["steps_info"], interactive=True)
-            with gr.Row():
-                gr.Markdown(translations["output_audio"])
-            with gr.Row():
-                output_audioldm2 = gr.Audio(show_download_button=True, interactive=False, label=translations["output_audio"])
-            with gr.Row():
-                refesh_audio.click(fn=change_audios_choices, inputs=[input_audiopath], outputs=[input_audiopath])
-                drop_audio_file.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[drop_audio_file], outputs=[input_audiopath])
-                input_audiopath.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audiopath], outputs=[display_audio])
-            with gr.Row():
-                edit_button.click(
-                    fn=run_audioldm2,
-                    inputs=[
-                        input_audiopath, 
-                        output_audiopath, 
-                        export_audio_format, 
-                        audioldm2_sample_rate, 
-                        audioldm2_model, 
-                        src_prompt, 
-                        tar_prompt, 
-                        steps, 
-                        cfg_scale_src, 
-                        cfg_scale_tar, 
-                        t_start, 
-                        save_compute
-                    ],
-                    outputs=[output_audioldm2],
-                    api_name="audioldm2"
-                )
-
-        with gr.TabItem(translations["audio_effects"], visible=configs.get("effects_tab", True)):
-            gr.Markdown(translations["apply_audio_effects"])
-            with gr.Row():
-                gr.Markdown(translations["audio_effects_edit"])
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        reverb_check_box = gr.Checkbox(label=translations["reverb"], value=False, interactive=True)
-                        chorus_check_box = gr.Checkbox(label=translations["chorus"], value=False, interactive=True)
-                        delay_check_box = gr.Checkbox(label=translations["delay"], value=False, interactive=True)
-                        phaser_check_box = gr.Checkbox(label=translations["phaser"], value=False, interactive=True)
-                        compressor_check_box = gr.Checkbox(label=translations["compressor"], value=False, interactive=True)
-                        more_options = gr.Checkbox(label=translations["more_option"], value=False, interactive=True)    
-            with gr.Row():
-                with gr.Accordion(translations["input_output"], open=False):
-                    with gr.Row():
-                        upload_audio = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])
-                    with gr.Row():
-                        audio_in_path = gr.Dropdown(label=translations["input_audio"], value="", choices=paths_for_files, info=translations["provide_audio"], interactive=True, allow_custom_value=True)
-                        audio_out_path = gr.Textbox(label=translations["output_audio"], value="audios/audio_effects.wav", placeholder="audios/audio_effects.wav", info=translations["provide_output"], interactive=True)
-                    with gr.Row():
-                        with gr.Column():
-                            audio_combination = gr.Checkbox(label=translations["merge_instruments"], value=False, interactive=True)
-                            audio_combination_input = gr.Dropdown(label=translations["input_audio"], value="", choices=paths_for_files, info=translations["provide_audio"], interactive=True, allow_custom_value=True, visible=audio_combination.value)
-                    with gr.Row():
-                        audio_effects_refesh = gr.Button(translations["refesh"])
-                    with gr.Row():
-                        audio_output_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True)
-            with gr.Row():
-                apply_effects_button = gr.Button(translations["apply"], variant="primary", scale=2)
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Accordion(translations["reverb"], open=False, visible=reverb_check_box.value) as reverb_accordion:
-                            reverb_freeze_mode = gr.Checkbox(label=translations["reverb_freeze"], info=translations["reverb_freeze_info"], value=False, interactive=True)
-                            reverb_room_size = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.15, label=translations["room_size"], info=translations["room_size_info"], interactive=True)
-                            reverb_damping = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label=translations["damping"], info=translations["damping_info"], interactive=True)
-                            reverb_wet_level = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.2, label=translations["wet_level"], info=translations["wet_level_info"], interactive=True)
-                            reverb_dry_level = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.8, label=translations["dry_level"], info=translations["dry_level_info"], interactive=True)
-                            reverb_width = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label=translations["width"], info=translations["width_info"], interactive=True)
-                    with gr.Row():
-                        with gr.Accordion(translations["chorus"], open=False, visible=chorus_check_box.value) as chorus_accordion:
-                            chorus_depth = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["chorus_depth"], info=translations["chorus_depth_info"], interactive=True)
-                            chorus_rate_hz = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1.5, label=translations["chorus_rate_hz"], info=translations["chorus_rate_hz_info"], interactive=True)
-                            chorus_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["chorus_mix"], info=translations["chorus_mix_info"], interactive=True)
-                            chorus_centre_delay_ms = gr.Slider(minimum=0, maximum=50, step=1, value=10, label=translations["chorus_centre_delay_ms"], info=translations["chorus_centre_delay_ms_info"], interactive=True)
-                            chorus_feedback = gr.Slider(minimum=-1, maximum=1, step=0.01, value=0, label=translations["chorus_feedback"], info=translations["chorus_feedback_info"], interactive=True)
-                    with gr.Row():
-                        with gr.Accordion(translations["delay"], open=False, visible=delay_check_box.value) as delay_accordion:
-                            delay_second = gr.Slider(minimum=0, maximum=5, step=0.01, value=0.5, label=translations["delay_seconds"], info=translations["delay_seconds_info"], interactive=True)
-                            delay_feedback = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["delay_feedback"], info=translations["delay_feedback_info"], interactive=True)
-                            delay_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["delay_mix"], info=translations["delay_mix_info"], interactive=True)
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Accordion(translations["more_option"], open=False, visible=more_options.value) as more_accordion:
-                            with gr.Row():
-                                fade = gr.Checkbox(label=translations["fade"], value=False, interactive=True)
-                                bass_or_treble = gr.Checkbox(label=translations["bass_or_treble"], value=False, interactive=True)
-                                limiter = gr.Checkbox(label=translations["limiter"], value=False, interactive=True)
-                                resample_checkbox = gr.Checkbox(label=translations["resample"], value=False, interactive=True)
-                            with gr.Row():
-                                distortion_checkbox = gr.Checkbox(label=translations["distortion"], value=False, interactive=True)
-                                gain_checkbox = gr.Checkbox(label=translations["gain"], value=False, interactive=True)
-                                bitcrush_checkbox = gr.Checkbox(label=translations["bitcrush"], value=False, interactive=True)
-                                clipping_checkbox = gr.Checkbox(label=translations["clipping"], value=False, interactive=True)
-                            with gr.Accordion(translations["fade"], open=True, visible=fade.value) as fade_accordion:
-                                with gr.Row():
-                                    fade_in = gr.Slider(minimum=0, maximum=10000, step=100, value=0, label=translations["fade_in"], info=translations["fade_in_info"], interactive=True)
-                                    fade_out = gr.Slider(minimum=0, maximum=10000, step=100, value=0, label=translations["fade_out"], info=translations["fade_out_info"], interactive=True)
-                            with gr.Accordion(translations["bass_or_treble"], open=True, visible=bass_or_treble.value) as bass_treble_accordion:
-                                with gr.Row():
-                                    bass_boost = gr.Slider(minimum=0, maximum=20, step=1, value=0, label=translations["bass_boost"], info=translations["bass_boost_info"], interactive=True)
-                                    bass_frequency = gr.Slider(minimum=20, maximum=200, step=10, value=100, label=translations["bass_frequency"], info=translations["bass_frequency_info"], interactive=True)
-                                with gr.Row():
-                                    treble_boost = gr.Slider(minimum=0, maximum=20, step=1, value=0, label=translations["treble_boost"], info=translations["treble_boost_info"], interactive=True)
-                                    treble_frequency = gr.Slider(minimum=1000, maximum=10000, step=500, value=3000, label=translations["treble_frequency"], info=translations["treble_frequency_info"], interactive=True)
-                            with gr.Accordion(translations["limiter"], open=True, visible=limiter.value) as limiter_accordion:
-                                with gr.Row():
-                                    limiter_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-1, label=translations["limiter_threashold_db"], info=translations["limiter_threashold_db_info"], interactive=True)
-                                    limiter_release_ms = gr.Slider(minimum=10, maximum=1000, step=1, value=100, label=translations["limiter_release_ms"], info=translations["limiter_release_ms_info"], interactive=True)
-                            with gr.Column():
-                                pitch_shift_semitones = gr.Slider(minimum=-20, maximum=20, step=1, value=0, label=translations["pitch"], info=translations["pitch_info"], interactive=True)
-                                audio_effect_resample_sr = gr.Slider(minimum=0, maximum=96000, step=1, value=0, label=translations["resample"], info=translations["resample_info"], interactive=True, visible=resample_checkbox.value)
-                                distortion_drive_db = gr.Slider(minimum=0, maximum=50, step=1, value=20, label=translations["distortion"], info=translations["distortion_info"], interactive=True, visible=distortion_checkbox.value)
-                                gain_db = gr.Slider(minimum=-60, maximum=60, step=1, value=0, label=translations["gain"], info=translations["gain_info"], interactive=True, visible=gain_checkbox.value)
-                                clipping_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-1, label=translations["clipping_threashold_db"], info=translations["clipping_threashold_db_info"], interactive=True, visible=clipping_checkbox.value)
-                                bitcrush_bit_depth = gr.Slider(minimum=1, maximum=24, step=1, value=16, label=translations["bitcrush_bit_depth"], info=translations["bitcrush_bit_depth_info"], interactive=True, visible=bitcrush_checkbox.value)
-                    with gr.Row():
-                        with gr.Accordion(translations["phaser"], open=False, visible=phaser_check_box.value) as phaser_accordion:
-                            phaser_depth = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["phaser_depth"], info=translations["phaser_depth_info"], interactive=True)
-                            phaser_rate_hz = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1, label=translations["phaser_rate_hz"], info=translations["phaser_rate_hz_info"], interactive=True)
-                            phaser_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["phaser_mix"], info=translations["phaser_mix_info"], interactive=True)
-                            phaser_centre_frequency_hz = gr.Slider(minimum=50, maximum=5000, step=10, value=1000, label=translations["phaser_centre_frequency_hz"], info=translations["phaser_centre_frequency_hz_info"], interactive=True)
-                            phaser_feedback = gr.Slider(minimum=-1, maximum=1, step=0.01, value=0, label=translations["phaser_feedback"], info=translations["phaser_feedback_info"], interactive=True)
-                    with gr.Row():
-                        with gr.Accordion(translations["compressor"], open=False, visible=compressor_check_box.value) as compressor_accordion:
-                            compressor_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-20, label=translations["compressor_threashold_db"], info=translations["compressor_threashold_db_info"], interactive=True)
-                            compressor_ratio = gr.Slider(minimum=1, maximum=20, step=0.1, value=1, label=translations["compressor_ratio"], info=translations["compressor_ratio_info"], interactive=True)
-                            compressor_attack_ms = gr.Slider(minimum=0.1, maximum=100, step=0.1, value=10, label=translations["compressor_attack_ms"], info=translations["compressor_attack_ms_info"], interactive=True)
-                            compressor_release_ms = gr.Slider(minimum=10, maximum=1000, step=1, value=100, label=translations["compressor_release_ms"], info=translations["compressor_release_ms_info"], interactive=True)   
-            with gr.Row():
-                gr.Markdown(translations["output_audio"])
-            with gr.Row():
-                audio_play_input = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
-                audio_play_output = gr.Audio(show_download_button=True, interactive=False, label=translations["output_audio"])
-            with gr.Row():
-                reverb_check_box.change(fn=visible, inputs=[reverb_check_box], outputs=[reverb_accordion])
-                chorus_check_box.change(fn=visible, inputs=[chorus_check_box], outputs=[chorus_accordion])
-                delay_check_box.change(fn=visible, inputs=[delay_check_box], outputs=[delay_accordion])
-            with gr.Row():
-                compressor_check_box.change(fn=visible, inputs=[compressor_check_box], outputs=[compressor_accordion])
-                phaser_check_box.change(fn=visible, inputs=[phaser_check_box], outputs=[phaser_accordion])
-                more_options.change(fn=visible, inputs=[more_options], outputs=[more_accordion])
-            with gr.Row():
-                fade.change(fn=visible, inputs=[fade], outputs=[fade_accordion])
-                bass_or_treble.change(fn=visible, inputs=[bass_or_treble], outputs=[bass_treble_accordion])
-                limiter.change(fn=visible, inputs=[limiter], outputs=[limiter_accordion])
-                resample_checkbox.change(fn=visible, inputs=[resample_checkbox], outputs=[audio_effect_resample_sr])
-            with gr.Row():
-                distortion_checkbox.change(fn=visible, inputs=[distortion_checkbox], outputs=[distortion_drive_db])
-                gain_checkbox.change(fn=visible, inputs=[gain_checkbox], outputs=[gain_db])
-                clipping_checkbox.change(fn=visible, inputs=[clipping_checkbox], outputs=[clipping_threashold_db])
-                bitcrush_checkbox.change(fn=visible, inputs=[bitcrush_checkbox], outputs=[bitcrush_bit_depth])
-            with gr.Row():
-                upload_audio.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[upload_audio], outputs=[audio_in_path])
-                audio_in_path.change(fn=lambda audio: audio if audio else None, inputs=[audio_in_path], outputs=[audio_play_input])
-                audio_effects_refesh.click(fn=lambda a, b: [change_audios_choices(a), change_audios_choices(b)], inputs=[audio_in_path, audio_combination_input], outputs=[audio_in_path, audio_combination_input])
-            with gr.Row():
-                more_options.change(fn=lambda: [False]*8, inputs=[], outputs=[fade, bass_or_treble, limiter, resample_checkbox, distortion_checkbox, gain_checkbox, clipping_checkbox, bitcrush_checkbox])
-                audio_combination.change(fn=visible, inputs=[audio_combination], outputs=[audio_combination_input])
-            with gr.Row():
-                apply_effects_button.click(
-                    fn=audio_effects,
-                    inputs=[
-                        audio_in_path, 
-                        audio_out_path, 
-                        resample_checkbox, 
-                        audio_effect_resample_sr, 
-                        chorus_depth, 
-                        chorus_rate_hz, 
-                        chorus_mix, 
-                        chorus_centre_delay_ms, 
-                        chorus_feedback, 
-                        distortion_drive_db, 
-                        reverb_room_size, 
-                        reverb_damping, 
-                        reverb_wet_level, 
-                        reverb_dry_level, 
-                        reverb_width, 
-                        reverb_freeze_mode, 
-                        pitch_shift_semitones, 
-                        delay_second, 
-                        delay_feedback, 
-                        delay_mix, 
-                        compressor_threashold_db, 
-                        compressor_ratio, 
-                        compressor_attack_ms, 
-                        compressor_release_ms, 
-                        limiter_threashold_db, 
-                        limiter_release_ms, 
-                        gain_db, 
-                        bitcrush_bit_depth, 
-                        clipping_threashold_db, 
-                        phaser_rate_hz, 
-                        phaser_depth, 
-                        phaser_centre_frequency_hz, 
-                        phaser_feedback, 
-                        phaser_mix, 
-                        bass_boost, 
-                        bass_frequency, 
-                        treble_boost, 
-                        treble_frequency, 
-                        fade_in, 
-                        fade_out, 
-                        audio_output_format, 
-                        chorus_check_box, 
-                        distortion_checkbox, 
-                        reverb_check_box, 
-                        delay_check_box, 
-                        compressor_check_box, 
-                        limiter, 
-                        gain_checkbox, 
-                        bitcrush_checkbox, 
-                        clipping_checkbox, 
-                        phaser_check_box, 
-                        bass_or_treble, 
-                        fade,
-                        audio_combination,
-                        audio_combination_input
-                    ],
-                    outputs=[audio_play_output],
-                    api_name="audio_effects"
-                )
-
-        with gr.TabItem(translations["createdataset"], visible=configs.get("create_dataset_tab", True)):
-            gr.Markdown(translations["create_dataset_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["create_dataset_markdown_2"])
-            with gr.Row():
-                dataset_url = gr.Textbox(label=translations["url_audio"], info=translations["create_dataset_url"], value="", placeholder="https://www.youtube.com/...", interactive=True)
-                output_dataset = gr.Textbox(label=translations["output_data"], info=translations["output_data_info"], value="dataset", placeholder="dataset", interactive=True)
-            with gr.Row():
-                with gr.Column():
-                    with gr.Group():
-                        with gr.Row():
-                            separator_reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=True)
-                            denoise_mdx = gr.Checkbox(label=translations["denoise"], value=False, interactive=True)
-                        with gr.Row():
-                            kim_vocal_version = gr.Radio(label=translations["model_ver"], info=translations["model_ver_info"], choices=["Version-1", "Version-2"], value="Version-2", interactive=True)
-                            kim_vocal_overlap = gr.Radio(label=translations["overlap"], info=translations["overlap_info"], choices=["0.25", "0.5", "0.75", "0.99"], value="0.25", interactive=True)
-                        with gr.Row():    
-                            kim_vocal_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=8192, value=1024, step=1, interactive=True)
-                            kim_vocal_batch_size = gr.Slider(label=translations["batch_size"], info=translations["mdx_batch_size_info"], minimum=1, maximum=64, value=1, step=1, interactive=True) 
-                        with gr.Row():
-                            kim_vocal_segments_size = gr.Slider(label=translations["segments_size"], info=translations["segments_size_info"], minimum=32, maximum=3072, value=256, step=32, interactive=True)
-                        with gr.Row():
-                            sample_rate0 = gr.Slider(minimum=8000, maximum=96000, step=1, value=44100, label=translations["sr"], info=translations["sr_info"], interactive=True)
-                with gr.Column():
-                    create_button = gr.Button(translations["createdataset"], variant="primary", scale=2, min_width=4000)
-                    with gr.Group():
-                        with gr.Row():
-                            clean_audio = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True)
-                            skip = gr.Checkbox(label=translations["skip"], value=False, interactive=True)
-                        with gr.Row():   
-                            dataset_clean_strength = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.5, label=translations["clean_strength"], info=translations["clean_strength_info"], interactive=True, visible=clean_audio.value)
-                        with gr.Row():
-                            skip_start = gr.Textbox(label=translations["skip_start"], info=translations["skip_start_info"], value="", placeholder="0,...", interactive=True, visible=skip.value)
-                            skip_end = gr.Textbox(label=translations["skip_end"], info=translations["skip_end_info"], value="", placeholder="0,...", interactive=True, visible=skip.value)
-                    create_dataset_info = gr.Textbox(label=translations["create_dataset_info"], value="", interactive=False)
-            with gr.Row():
-                clean_audio.change(fn=visible, inputs=[clean_audio], outputs=[dataset_clean_strength])
-                skip.change(fn=lambda a: [valueEmpty_visible1(a)]*2, inputs=[skip], outputs=[skip_start, skip_end])
-            with gr.Row():
-                create_button.click(
-                    fn=create_dataset,
-                    inputs=[
-                        dataset_url, 
-                        output_dataset, 
-                        clean_audio, 
-                        dataset_clean_strength, 
-                        separator_reverb, 
-                        kim_vocal_version, 
-                        kim_vocal_overlap, 
-                        kim_vocal_segments_size, 
-                        denoise_mdx, 
-                        skip, 
-                        skip_start, 
-                        skip_end,
-                        kim_vocal_hop_length,
-                        kim_vocal_batch_size,
-                        sample_rate0
-                    ],
-                    outputs=[create_dataset_info],
-                    api_name="create_dataset"
-                )
-
-        with gr.TabItem(translations["fushion"], visible=configs.get("fushion_tab", True)):
-            gr.Markdown(translations["fushion_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["fushion_markdown_2"])
-            with gr.Row():
-                name_to_save = gr.Textbox(label=translations["modelname"], placeholder="Model.pth", value="", max_lines=1, interactive=True)
-            with gr.Row():
-                fushion_button = gr.Button(translations["fushion"], variant="primary", scale=4)
-            with gr.Column():
-                with gr.Row():
-                    model_a = gr.File(label=f"{translations['model_name']} 1", file_types=[".pth", ".onnx"]) 
-                    model_b = gr.File(label=f"{translations['model_name']} 2", file_types=[".pth", ".onnx"])
-                with gr.Row():
-                    model_path_a = gr.Textbox(label=f"{translations['model_path']} 1", value="", placeholder="assets/weights/Model_1.pth")
-                    model_path_b = gr.Textbox(label=f"{translations['model_path']} 2", value="", placeholder="assets/weights/Model_2.pth")
-            with gr.Row():
-                ratio = gr.Slider(minimum=0, maximum=1, label=translations["model_ratio"], info=translations["model_ratio_info"], value=0.5, interactive=True)
-            with gr.Row():
-                output_model = gr.File(label=translations["output_model_path"], file_types=[".pth", ".onnx"], interactive=False, visible=False)
-            with gr.Row():
-                model_a.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_a], outputs=[model_path_a])
-                model_b.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_b], outputs=[model_path_b])
-            with gr.Row():
-                fushion_button.click(
-                    fn=fushion_model,
-                    inputs=[
-                        name_to_save, 
-                        model_path_a, 
-                        model_path_b, 
-                        ratio
-                    ],
-                    outputs=[name_to_save, output_model],
-                    api_name="fushion_model"
-                )
-                fushion_button.click(fn=lambda: visible(True), inputs=[], outputs=[output_model])  
-
-        with gr.TabItem(translations["read_model"], visible=configs.get("read_tab", True)):
-            gr.Markdown(translations["read_model_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["read_model_markdown_2"])
-            with gr.Row():
-                model = gr.File(label=translations["drop_model"], file_types=[".pth", ".onnx"]) 
-            with gr.Row():
-                read_button = gr.Button(translations["readmodel"], variant="primary", scale=2)
-            with gr.Column():
-                model_path = gr.Textbox(label=translations["model_path"], value="", placeholder="assets/weights/Model.pth", info=translations["model_path_info"], interactive=True)
-                output_info = gr.Textbox(label=translations["modelinfo"], value="", interactive=False, scale=6)
-            with gr.Row():
-                model.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model], outputs=[model_path])
-                read_button.click(
-                    fn=model_info,
-                    inputs=[model_path],
-                    outputs=[output_info],
-                    api_name="read_model"
-                )
-
-        with gr.TabItem(translations["convert_model"], visible=configs.get("onnx_tab", True)):
-            gr.Markdown(translations["pytorch2onnx"])
-            with gr.Row():
-                gr.Markdown(translations["pytorch2onnx_markdown"])
-            with gr.Row():
-                model_pth_upload = gr.File(label=translations["drop_model"], file_types=[".pth"]) 
-            with gr.Row():
-                convert_onnx = gr.Button(translations["convert_model"], variant="primary", scale=2)
-            with gr.Row():
-                model_pth_path = gr.Textbox(label=translations["model_path"], value="", placeholder="assets/weights/Model.pth", info=translations["model_path_info"], interactive=True)
-            with gr.Row():
-                output_model2 = gr.File(label=translations["output_model_path"], file_types=[".pth", ".onnx"], interactive=False, visible=False)
-            with gr.Row():
-                model_pth_upload.upload(fn=lambda model_pth_upload: shutil.move(model_pth_upload.name, os.path.join("assets", "weights")), inputs=[model_pth_upload], outputs=[model_pth_path])
-                convert_onnx.click(
-                    fn=onnx_export,
-                    inputs=[model_pth_path],
-                    outputs=[output_model2, output_info],
-                    api_name="model_onnx_export"
-                )
-                convert_onnx.click(fn=lambda: visible(True), inputs=[], outputs=[output_model2])  
-
-        with gr.TabItem(translations["f0_extractor_tab"], visible=configs.get("f0_extractor_tab", True)):
-            gr.Markdown(translations["f0_extractor_markdown"])
-            with gr.Row():
-                gr.Markdown(translations["f0_extractor_markdown_2"])
-            with gr.Row():
-                extractor_button = gr.Button(translations["extract_button"].replace("2. ", ""), variant="primary")
-            with gr.Row():
-                with gr.Column():
-                    upload_audio_file = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])
-                    audioplay = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
-                with gr.Column():
-                    with gr.Accordion(translations["f0_method"], open=False):
-                        with gr.Group():
-                            onnx_f0_mode3 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
-                            f0_method_extract = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True)
-                    with gr.Accordion(translations["audio_path"], open=True):
-                        input_audio_path = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, allow_custom_value=True, interactive=True)
-                        refesh_audio_button = gr.Button(translations["refesh"])
-            with gr.Row():
-                gr.Markdown("___")
-            with gr.Row():
-                file_output = gr.File(label="", file_types=[".txt"], interactive=False)
-                image_output = gr.Image(label="", interactive=False, show_download_button=True)
-            with gr.Row():
-                upload_audio_file.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[upload_audio_file], outputs=[input_audio_path])
-                input_audio_path.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio_path], outputs=[audioplay])
-                refesh_audio_button.click(fn=change_audios_choices, inputs=[input_audio_path], outputs=[input_audio_path])
-            with gr.Row():
-                extractor_button.click(
-                    fn=f0_extract,
-                    inputs=[
-                        input_audio_path,
-                        f0_method_extract,
-                        onnx_f0_mode3
-                    ],
-                    outputs=[file_output, image_output],
-                    api_name="f0_extract"
-                )
-
         with gr.TabItem(translations["settings"], visible=configs.get("settings_tab", True)):
             gr.Markdown(translations["settings_markdown"])
             with gr.Row():