demo.py

from original import *
import shutil, glob
from easyfuncs import download_from_url, CachedModels
os.makedirs("dataset",exist_ok=True)
model_library = CachedModels()
from typing import Iterable
import gradio as gr

os.system("python tools/download_models.py") # -> dummy extra






# gr.themes.builder()
from gradio.themes.base import Base
from gradio.themes.utils import colors, fonts, sizes
import time

# Applio Theme
class Applio(Base):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.neutral,
        secondary_hue: colors.Color | str = colors.neutral,
        neutral_hue: colors.Color | str = colors.neutral,
        spacing_size: sizes.Size | str = sizes.spacing_md,
        radius_size: sizes.Size | str = sizes.radius_md,
        text_size: sizes.Size | str = sizes.text_lg,
        font: fonts.Font | str | Iterable[fonts.Font | str] = (
            "Syne V",
            fonts.GoogleFont("Syne"),
            "ui-sans-serif",
            "system-ui",
        ),
        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
            "ui-monospace",
            fonts.GoogleFont("Nunito Sans"),
        ),
    ):
        super().__init__(
            primary_hue=primary_hue,
            secondary_hue=secondary_hue,
            neutral_hue=neutral_hue,
            spacing_size=spacing_size,
            radius_size=radius_size,
            text_size=text_size,
            font=font,
            font_mono=font_mono,
        )
        self.name = ("Applio",)
        self.secondary_100 = ("#dbeafe",)
        self.secondary_200 = ("#bfdbfe",)
        self.secondary_300 = ("#93c5fd",)
        self.secondary_400 = ("#60a5fa",)
        self.secondary_50 = ("#eff6ff",)
        self.secondary_500 = ("#3b82f6",)
        self.secondary_600 = ("#2563eb",)
        self.secondary_700 = ("#1d4ed8",)
        self.secondary_800 = ("#1e40af",)
        self.secondary_900 = ("#1e3a8a",)
        self.secondary_950 = ("#1d3660",)

        super().set(
            # Blaise
            background_fill_primary="#110F0F",
            background_fill_primary_dark="#110F0F",
            background_fill_secondary="#110F0F",
            background_fill_secondary_dark="#110F0F",
            block_background_fill="*neutral_800",
            block_background_fill_dark="*neutral_800",
            block_border_color="*border_color_primary",
            block_border_color_dark="*border_color_primary",
            block_border_width="1px",
            block_border_width_dark="1px",
            block_info_text_color="*body_text_color_subdued",
            block_info_text_color_dark="*body_text_color_subdued",
            block_info_text_size="*text_sm",
            block_info_text_weight="400",
            block_label_background_fill="*background_fill_primary",
            block_label_background_fill_dark="*background_fill_secondary",
            block_label_border_color="*border_color_primary",
            block_label_border_color_dark="*border_color_primary",
            block_label_border_width="1px",
            block_label_border_width_dark="1px",
            block_label_margin="0",
            block_label_padding="*spacing_sm *spacing_lg",
            block_label_radius="calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px) 0",
            block_label_right_radius="0 calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px)",
            block_label_shadow="*block_shadow",
            block_label_text_color="*#110F0F",
            block_label_text_color_dark="*#110F0F",
            block_label_text_weight="400",
            block_padding="*spacing_xl",
            block_radius="*radius_md",
            block_shadow="none",
            block_shadow_dark="none",
            block_title_background_fill="rgb(255,255,255)",
            block_title_background_fill_dark="rgb(255,255,255)",
            block_title_border_color="none",
            block_title_border_color_dark="none",
            block_title_border_width="0px",
            block_title_padding="*block_label_padding",
            block_title_radius="*block_label_radius",
            block_title_text_color="#110F0F",
            block_title_text_color_dark="#110F0F",
            block_title_text_size="*text_md",
            block_title_text_weight="600",
            body_background_fill="#110F0F",
            body_background_fill_dark="#110F0F",
            body_text_color="white",
            body_text_color_dark="white",
            body_text_color_subdued="*neutral_400",
            body_text_color_subdued_dark="*neutral_400",
            body_text_size="*text_md",
            body_text_weight="400",
            border_color_accent="*neutral_600",
            border_color_accent_dark="*neutral_600",
            border_color_primary="*neutral_800",
            border_color_primary_dark="*neutral_800",
            button_border_width="*input_border_width",
            button_border_width_dark="*input_border_width",
            button_cancel_background_fill="*button_secondary_background_fill",
            button_cancel_background_fill_dark="*button_secondary_background_fill",
            button_cancel_background_fill_hover="*button_cancel_background_fill",
            button_cancel_background_fill_hover_dark="*button_cancel_background_fill",
            button_cancel_border_color="*button_secondary_border_color",
            button_cancel_border_color_dark="*button_secondary_border_color",
            button_cancel_border_color_hover="*button_cancel_border_color",
            button_cancel_border_color_hover_dark="*button_cancel_border_color",
            button_cancel_text_color="#110F0F",
            button_cancel_text_color_dark="#110F0F",
            button_cancel_text_color_hover="#110F0F",
            button_cancel_text_color_hover_dark="#110F0F",
            button_large_padding="*spacing_lg calc(2 * *spacing_lg)",
            button_large_radius="*radius_lg",
            button_large_text_size="*text_lg",
            button_large_text_weight="600",
            button_primary_background_fill="*primary_600",
            button_primary_background_fill_dark="*primary_600",
            button_primary_background_fill_hover="*primary_500",
            button_primary_background_fill_hover_dark="*primary_500",
            button_primary_border_color="*primary_500",
            button_primary_border_color_dark="*primary_500",
            button_primary_border_color_hover="*primary_400",
            button_primary_border_color_hover_dark="*primary_400",
            button_primary_text_color="white",
            button_primary_text_color_dark="white",
            button_primary_text_color_hover="#110F0F",
            button_primary_text_color_hover_dark="#110F0F",
            button_secondary_background_fill="transparent",
            button_secondary_background_fill_dark="transparent",
            button_secondary_background_fill_hover="*neutral_800",
            button_secondary_background_fill_hover_dark="*neutral_800",
            button_secondary_border_color="*neutral_700",
            button_secondary_border_color_dark="*neutral_700",
            button_secondary_border_color_hover="*neutral_600",
            button_secondary_border_color_hover_dark="*neutral_600",
            button_secondary_text_color="white",
            button_secondary_text_color_dark="white",
            button_secondary_text_color_hover="*button_secondary_text_color",
            button_secondary_text_color_hover_dark="*button_secondary_text_color",
            button_shadow="none",
            button_shadow_active="*shadow_inset",
            button_shadow_hover="none",
            button_small_padding="*spacing_sm calc(2 * *spacing_sm)",
            button_small_radius="*radius_lg",
            button_small_text_size="*text_md",
            button_small_text_weight="400",
            button_transition="0.3s ease all",
            checkbox_background_color="*neutral_700",
            checkbox_background_color_dark="*neutral_700",
            checkbox_background_color_focus="*checkbox_background_color",
            checkbox_background_color_focus_dark="*checkbox_background_color",
            checkbox_background_color_hover="*checkbox_background_color",
            checkbox_background_color_hover_dark="*checkbox_background_color",
            checkbox_background_color_selected="*secondary_600",
            checkbox_background_color_selected_dark="*secondary_600",
            checkbox_border_color="*neutral_700",
            checkbox_border_color_dark="*neutral_700",
            checkbox_border_color_focus="*secondary_500",
            checkbox_border_color_focus_dark="*secondary_500",
            checkbox_border_color_hover="*neutral_600",
            checkbox_border_color_hover_dark="*neutral_600",
            checkbox_border_color_selected="*secondary_600",
            checkbox_border_color_selected_dark="*secondary_600",
            checkbox_border_radius="*radius_sm",
            checkbox_border_width="*input_border_width",
            checkbox_border_width_dark="*input_border_width",
            checkbox_check="url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e\")",
            checkbox_label_background_fill="transparent",
            checkbox_label_background_fill_dark="transparent",
            checkbox_label_background_fill_hover="transparent",
            checkbox_label_background_fill_hover_dark="transparent",
            checkbox_label_background_fill_selected="transparent",
            checkbox_label_background_fill_selected_dark="transparent",
            checkbox_label_border_color="transparent",
            checkbox_label_border_color_dark="transparent",
            checkbox_label_border_color_hover="transparent",
            checkbox_label_border_color_hover_dark="transparent",
            checkbox_label_border_width="transparent",
            checkbox_label_border_width_dark="transparent",
            checkbox_label_gap="*spacing_lg",
            checkbox_label_padding="*spacing_md calc(2 * *spacing_md)",
            checkbox_label_shadow="none",
            checkbox_label_text_color="*body_text_color",
            checkbox_label_text_color_dark="*body_text_color",
            checkbox_label_text_color_selected="*checkbox_label_text_color",
            checkbox_label_text_color_selected_dark="*checkbox_label_text_color",
            checkbox_label_text_size="*text_md",
            checkbox_label_text_weight="400",
            checkbox_shadow="*input_shadow",
            color_accent="*primary_500",
            color_accent_soft="*primary_50",
            color_accent_soft_dark="*neutral_700",
            container_radius="*radius_xl",
            embed_radius="*radius_lg",
            error_background_fill="*background_fill_primary",
            error_background_fill_dark="*background_fill_primary",
            error_border_color="*border_color_primary",
            error_border_color_dark="*border_color_primary",
            error_border_width="1px",
            error_border_width_dark="1px",
            error_text_color="#ef4444",
            error_text_color_dark="#ef4444",
            form_gap_width="0px",
            input_background_fill="*neutral_900",
            input_background_fill_dark="*neutral_900",
            input_background_fill_focus="*secondary_600",
            input_background_fill_focus_dark="*secondary_600",
            input_background_fill_hover="*input_background_fill",
            input_background_fill_hover_dark="*input_background_fill",
            input_border_color="*neutral_700",
            input_border_color_dark="*neutral_700",
            input_border_color_focus="*secondary_600",
            input_border_color_focus_dark="*primary_600",
            input_border_color_hover="*input_border_color",
            input_border_color_hover_dark="*input_border_color",
            input_border_width="1px",
            input_border_width_dark="1px",
            input_padding="*spacing_xl",
            input_placeholder_color="*neutral_500",
            input_placeholder_color_dark="*neutral_500",
            input_radius="*radius_lg",
            input_shadow="none",
            input_shadow_dark="none",
            input_shadow_focus="*input_shadow",
            input_shadow_focus_dark="*input_shadow",
            input_text_size="*text_md",
            input_text_weight="400",
            layout_gap="*spacing_xxl",
            link_text_color="*secondary_500",
            link_text_color_active="*secondary_500",
            link_text_color_active_dark="*secondary_500",
            link_text_color_dark="*secondary_500",
            link_text_color_hover="*secondary_400",
            link_text_color_hover_dark="*secondary_400",
            link_text_color_visited="*secondary_600",
            link_text_color_visited_dark="*secondary_600",
            loader_color="*color_accent",
            loader_color_dark="*color_accent",
            panel_background_fill="*background_fill_secondary",
            panel_background_fill_dark="*background_fill_secondary",
            panel_border_color="*border_color_primary",
            panel_border_color_dark="*border_color_primary",
            panel_border_width="1px",
            panel_border_width_dark="1px",
            prose_header_text_weight="600",
            prose_text_size="*text_md",
            prose_text_weight="400",
            radio_circle="url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e\")",
            section_header_text_size="*text_md",
            section_header_text_weight="400",
            shadow_drop="rgba(0,0,0,0.05) 0px 1px 2px 0px",
            shadow_drop_lg="0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1)",
            shadow_inset="rgba(0,0,0,0.05) 0px 2px 4px 0px inset",
            shadow_spread="3px",
            shadow_spread_dark="1px",
            slider_color="#9E9E9E",
            slider_color_dark="#9E9E9E",
            stat_background_fill="*primary_500",
            stat_background_fill_dark="*primary_500",
            table_border_color="*neutral_700",
            table_border_color_dark="*neutral_700",
            table_even_background_fill="*neutral_950",
            table_even_background_fill_dark="*neutral_950",
            table_odd_background_fill="*neutral_900",
            table_odd_background_fill_dark="*neutral_900",
            table_radius="*radius_lg",
            table_row_focus="*color_accent_soft",
            table_row_focus_dark="*color_accent_soft",
        )


theme = Applio()



with gr.Blocks(title="RVC V2",theme=theme) as app:
    with gr.Row():
        
        gr.HTML("<img  src='https://huggingface.co/spaces/Blane187/RVC_HF_V2/resolve/main/a.png' alt='image'>")
        #toggle_dark = gr.Button(value="Toggle Dark")
    with gr.Tabs():
        with gr.TabItem("Inference"):
            with gr.Row():
                voice_model = gr.Dropdown(label="Model Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True)
                refresh_button = gr.Button("Refresh", variant="primary")
                spk_item = gr.Slider(
                    minimum=0,
                    maximum=2333,
                    step=1,
                    label="Speaker ID",
                    value=0,
                    visible=False,
                    interactive=True,
                )
                vc_transform0 = gr.Number(label="Pitch",value=0)
                but0 = gr.Button(value="Convert", variant="primary")
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        dropbox = gr.Audio(label="your audio here.")
                    
                with gr.Column():
                    with gr.Accordion("Change Index", open=False):
                        file_index2 = gr.Dropdown(
                            label="Change Index",
                            choices=sorted(index_paths),
                            interactive=True,
                            value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else ''
                        )
                        index_rate1 = gr.Slider(
                            minimum=0,
                            maximum=1,
                            label="Index Strength",
                            value=0.5,
                            interactive=True,
                        )
                    vc_output2 = gr.Audio(label="Output")
                    with gr.Accordion("General Settings", open=False):
                        f0method0 = gr.Radio(
                            label="Method",
                            choices=["pm", "harvest", "crepe", "rmvpe", "torchfcpe"]
                            if config.dml == False
                            else ["pm", "harvest", "rmvpe"],
                            value="rmvpe",
                            interactive=True,
                        )
                        filter_radius0 = gr.Slider(
                            minimum=0,
                            maximum=7,
                            label="Breathiness Reduction (Harvest only)",
                            value=3,
                            step=1,
                            interactive=True,
                        )
                        resample_sr0 = gr.Slider(
                            minimum=0,
                            maximum=48000,
                            label="Resample",
                            value=0,
                            step=1,
                            interactive=True,
                            visible=False
                        )
                        rms_mix_rate0 = gr.Slider(
                            minimum=0,
                            maximum=1,
                            label="Volume Normalization",
                            value=0,
                            interactive=True,
                        )
                        protect0 = gr.Slider(
                            minimum=0,
                            maximum=0.5,
                            label="Breathiness Protection (0 is enabled, 0.5 is disabled)",
                            value=0.33,
                            step=0.01,
                            interactive=True,
                        )
                        if voice_model != None: vc.get_vc(voice_model.value,protect0,protect0)
                    file_index1 = gr.Textbox(
                        label="Index Path",
                        interactive=True,
                        visible=False#Not used here
                    )
                    refresh_button.click(
                        fn=change_choices,
                        inputs=[],
                        outputs=[voice_model, file_index2],
                        api_name="infer_refresh",
                    )
                    
            with gr.Row():
                f0_file = gr.File(label="F0 Path", visible=False)
            with gr.Row():
                vc_output1 = gr.Textbox(label="Information", placeholder="Welcome!",visible=False)
                but0.click(
                    vc.vc_single,  
                    [
                        spk_item,
                        dropbox,
                        vc_transform0,
                        f0_file,
                        f0method0,
                        file_index1,
                        file_index2,
                        index_rate1,
                        filter_radius0,
                        resample_sr0,
                        rms_mix_rate0,
                        protect0,
                    ],
                    [vc_output1, vc_output2],
                    api_name="infer_convert",
                )  
                voice_model.change(
                    fn=vc.get_vc,
                    inputs=[voice_model, protect0, protect0],
                    outputs=[spk_item, protect0, protect0, file_index2, file_index2],
                    api_name="infer_change_voice",
                )
        with gr.TabItem("Download Models"):
            with gr.Row():
                url_input = gr.Textbox(label="URL to model", value="",placeholder="https://...", scale=6)
                name_output = gr.Textbox(label="Save as", value="",placeholder="MyModel",scale=2)
                url_download = gr.Button(value="Download Model",scale=2)
                url_download.click(
                    inputs=[url_input,name_output],
                    outputs=[url_input],
                    fn=download_from_url,
                )
            with gr.Row():
                model_browser = gr.Dropdown(choices=list(model_library.models.keys()),label="OR Search Models (Quality UNKNOWN)",scale=5)
                download_from_browser = gr.Button(value="Get",scale=2)
                download_from_browser.click(
                    inputs=[model_browser],
                    outputs=[model_browser],
                    fn=lambda model: download_from_url(model_library.models[model],model),
                )
        #if warning:
        with gr.TabItem("read this"):
            gr.Markdown(f"This Spaces Using CPU dude\n may inference take long time\n and Train tab is disable :)")
        
        with gr.TabItem("Train", visible=False):
            with gr.Row():
                with gr.Column():
                    training_name = gr.Textbox(label="Name your model", value="My-Voice",placeholder="My-Voice")
                    np7 = gr.Slider(
                        minimum=0,
                        maximum=config.n_cpu,
                        step=1,
                        label="Number of CPU processes used to extract pitch features",
                        value=int(np.ceil(config.n_cpu / 1.5)),
                        interactive=True,
                    )
                    sr2 = gr.Radio(
                        label="Sampling Rate",
                        choices=["40k", "32k"],
                        value="32k",
                        interactive=True,
                        visible=False
                    )
                    if_f0_3 = gr.Radio(
                        label="Will your model be used for singing? If not, you can ignore this.",
                        choices=[True, False],
                        value=True,
                        interactive=True,
                        visible=False
                    )
                    version19 = gr.Radio(
                        label="Version",
                        choices=["v1", "v2"],
                        value="v2",
                        interactive=True,
                        visible=False,
                    )
                    dataset_folder = gr.Textbox(
                        label="dataset folder", value='dataset'
                    )
                    easy_uploader = gr.Files(label="Drop your audio files here",file_types=['audio'])
                    but1 = gr.Button("1. Process", variant="primary")
                    info1 = gr.Textbox(label="Information", value="",visible=True)
                    easy_uploader.upload(inputs=[dataset_folder],outputs=[],fn=lambda folder:os.makedirs(folder,exist_ok=True))
                    easy_uploader.upload(
                        fn=lambda files,folder: [shutil.copy2(f.name,os.path.join(folder,os.path.split(f.name)[1])) for f in files] if folder != "" else gr.Warning('Please enter a folder name for your dataset'),
                        inputs=[easy_uploader, dataset_folder], 
                        outputs=[])
                    gpus6 = gr.Textbox(
                        label="Enter the GPU numbers to use separated by -, (e.g. 0-1-2)",
                        value=gpus,
                        interactive=True,
                        visible=F0GPUVisible,
                    )
                    gpu_info9 = gr.Textbox(
                        label="GPU Info", value=gpu_info, visible=F0GPUVisible
                    )
                    spk_id5 = gr.Slider(
                        minimum=0,
                        maximum=4,
                        step=1,
                        label="Speaker ID",
                        value=0,
                        interactive=True,
                        visible=False
                    )
                    but1.click(
                        preprocess_dataset,
                        [dataset_folder, training_name, sr2, np7],
                        [info1],
                        api_name="train_preprocess",
                    ) 
                with gr.Column():
                    f0method8 = gr.Radio(
                        label="F0 extraction method",
                        choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"],
                        value="rmvpe_gpu",
                        interactive=True,
                    )
                    gpus_rmvpe = gr.Textbox(
                        label="GPU numbers to use separated by -, (e.g. 0-1-2)",
                        value="%s-%s" % (gpus, gpus),
                        interactive=True,
                        visible=F0GPUVisible,
                    )
                    but2 = gr.Button("2. Extract Features", variant="primary")
                    info2 = gr.Textbox(label="Information", value="", max_lines=8)
                    f0method8.change(
                        fn=change_f0_method,
                        inputs=[f0method8],
                        outputs=[gpus_rmvpe],
                    )
                    but2.click(
                        extract_f0_feature,
                        [
                            gpus6,
                            np7,
                            f0method8,
                            if_f0_3,
                            training_name,
                            version19,
                            gpus_rmvpe,
                        ],
                        [info2],
                        api_name="train_extract_f0_feature",
                    )
                with gr.Column():
                    total_epoch11 = gr.Slider(
                        minimum=2,
                        maximum=1000,
                        step=1,
                        label="Epochs (more epochs may improve quality but takes longer)",
                        value=150,
                        interactive=True,
                    )
                    but4 = gr.Button("3. Train Index", variant="primary")
                    but3 = gr.Button("4. Train Model", variant="primary")
                    info3 = gr.Textbox(label="Information", value="", max_lines=10)
                    with gr.Accordion(label="General Settings", open=False):
                        gpus16 = gr.Textbox(
                            label="GPUs separated by -, (e.g. 0-1-2)",
                            value="0",
                            interactive=True,
                            visible=True
                        )
                        save_epoch10 = gr.Slider(
                            minimum=1,
                            maximum=50,
                            step=1,
                            label="Weight Saving Frequency",
                            value=25,
                            interactive=True,
                        )
                        batch_size12 = gr.Slider(
                            minimum=1,
                            maximum=40,
                            step=1,
                            label="Batch Size",
                            value=default_batch_size,
                            interactive=True,
                        )
                        if_save_latest13 = gr.Radio(
                            label="Only save the latest model",
                            choices=["yes", "no"],
                            value="yes",
                            interactive=True,
                            visible=False
                        )
                        if_cache_gpu17 = gr.Radio(
                            label="If your dataset is UNDER 10 minutes, cache it to train faster",
                            choices=["yes", "no"],
                            value="no",
                            interactive=True,
                        )
                        if_save_every_weights18 = gr.Radio(
                            label="Save small model at every save point",
                            choices=["yes", "no"],
                            value="yes",
                            interactive=True,
                        )
                        with gr.Accordion(label="Change pretrains", open=False):
                            pretrained = lambda sr, letter: [os.path.abspath(os.path.join('assets/pretrained_v2', file)) for file in os.listdir('assets/pretrained_v2') if file.endswith('.pth') and sr in file and letter in file]
                            pretrained_G14 = gr.Dropdown(
                                label="pretrained G",
                                # Get a list of all pretrained G model files in assets/pretrained_v2 that end with .pth
                                choices = pretrained(sr2.value, 'G'),
                                value=pretrained(sr2.value, 'G')[0] if len(pretrained(sr2.value, 'G')) > 0 else '',
                                interactive=True,
                                visible=True
                            )
                            pretrained_D15 = gr.Dropdown(
                                label="pretrained D",
                                choices = pretrained(sr2.value, 'D'),
                                value= pretrained(sr2.value, 'D')[0] if len(pretrained(sr2.value, 'G')) > 0 else '',
                                visible=True,
                                interactive=True
                            )
                    with gr.Row():
                        download_model = gr.Button('5.Download Model')
                    with gr.Row():
                        model_files = gr.Files(label='Your Model and Index file can be downloaded here:')
                        download_model.click(
                            fn=lambda name: os.listdir(f'assets/weights/{name}') + glob.glob(f'logs/{name.split(".")[0]}/added_*.index'),
                            inputs=[training_name], 
                            outputs=[model_files, info3])
                    with gr.Row():
                        sr2.change(
                            change_sr2,
                            [sr2, if_f0_3, version19],
                            [pretrained_G14, pretrained_D15],
                        )
                        version19.change(
                            change_version19,
                            [sr2, if_f0_3, version19],
                            [pretrained_G14, pretrained_D15, sr2],
                        )
                        if_f0_3.change(
                            change_f0,
                            [if_f0_3, sr2, version19],
                            [f0method8, pretrained_G14, pretrained_D15],
                        )
                    with gr.Row():
                        but5 = gr.Button("1 Click Training", variant="primary", visible=False)
                        but3.click(
                            click_train,
                            [
                                training_name,
                                sr2,
                                if_f0_3,
                                spk_id5,
                                save_epoch10,
                                total_epoch11,
                                batch_size12,
                                if_save_latest13,
                                pretrained_G14,
                                pretrained_D15,
                                gpus16,
                                if_cache_gpu17,
                                if_save_every_weights18,
                                version19,
                            ],
                            info3,
                            api_name="train_start",
                        )
                        but4.click(train_index, [training_name, version19], info3)
                        but5.click(
                            train1key,
                            [
                                training_name,
                                sr2,
                                if_f0_3,
                                dataset_folder,
                                spk_id5,
                                np7,
                                f0method8,
                                save_epoch10,
                                total_epoch11,
                                batch_size12,
                                if_save_latest13,
                                pretrained_G14,
                                pretrained_D15,
                                gpus16,
                                if_cache_gpu17,
                                if_save_every_weights18,
                                version19,
                                gpus_rmvpe,
                            ],
                            info3,
                            api_name="train_start_all",
                        )

    app.launch(share=True)