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from original import * |
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import shutil, glob |
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from easyfuncs import download_from_url, CachedModels |
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os.makedirs("dataset",exist_ok=True) |
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model_library = CachedModels() |
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with gr.Blocks(title="💻 RVC V2",theme=gr.themes.Soft(primary_hue="blue",neutral_hue="cyan")) as app: |
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with gr.Row(): |
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gr.markdown("# RVC V2") |
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gr.markdown("## Code By Rejekts") |
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with gr.Tabs(): |
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with gr.TabItem("Inference"): |
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with gr.Row(): |
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voice_model = gr.Dropdown(label="Model Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True) |
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refresh_button = gr.Button("Refresh", variant="primary") |
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spk_item = gr.Slider( |
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minimum=0, |
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maximum=2333, |
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step=1, |
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label="Speaker ID", |
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value=0, |
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visible=False, |
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interactive=True, |
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) |
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vc_transform0 = gr.Number( |
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label="Pitch", |
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value=0 |
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) |
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but0 = gr.Button(value="Convert", variant="primary") |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Row(): |
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dropbox = gr.File(label="Drop your audio here & hit the Reload button.") |
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with gr.Row(): |
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record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath") |
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with gr.Row(): |
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paths_for_files = lambda path:[os.path.abspath(os.path.join(path, f)) for f in os.listdir(path) if os.path.splitext(f)[1].lower() in ('.mp3', '.wav', '.flac', '.ogg')] |
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input_audio0 = gr.Dropdown( |
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label="Input Path", |
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value=paths_for_files('audios')[0] if len(paths_for_files('audios')) > 0 else '', |
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choices=paths_for_files('audios'), |
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allow_custom_value=True |
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) |
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with gr.Row(): |
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audio_player = gr.Audio() |
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input_audio0.change( |
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inputs=[input_audio0], |
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outputs=[audio_player], |
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fn=lambda path: {"value":path,"__type__":"update"} if os.path.exists(path) else None |
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) |
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record_button.stop_recording( |
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fn=lambda audio:audio, |
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inputs=[record_button], |
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outputs=[input_audio0]) |
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dropbox.upload( |
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fn=lambda audio:audio.name, |
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inputs=[dropbox], |
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outputs=[input_audio0]) |
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with gr.Column(): |
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with gr.Accordion("Change Index", open=False): |
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file_index2 = gr.Dropdown( |
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label="Change Index", |
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choices=sorted(index_paths), |
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interactive=True, |
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value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else '' |
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) |
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index_rate1 = gr.Slider( |
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minimum=0, |
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maximum=1, |
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label="Index Strength", |
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value=0.5, |
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interactive=True, |
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) |
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vc_output2 = gr.Audio(label="Output") |
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with gr.Accordion("General Settings", open=False): |
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f0method0 = gr.Radio( |
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label="Method", |
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choices=["pm", "harvest", "crepe", "rmvpe"] |
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if config.dml == False |
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else ["pm", "harvest", "rmvpe"], |
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value="rmvpe", |
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interactive=True, |
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) |
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filter_radius0 = gr.Slider( |
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minimum=0, |
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maximum=7, |
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label="Breathiness Reduction (Harvest only)", |
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value=3, |
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step=1, |
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interactive=True, |
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) |
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resample_sr0 = gr.Slider( |
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minimum=0, |
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maximum=48000, |
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label="Resample", |
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value=0, |
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step=1, |
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interactive=True, |
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visible=False |
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) |
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rms_mix_rate0 = gr.Slider( |
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minimum=0, |
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maximum=1, |
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label="Volume Normalization", |
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value=0, |
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interactive=True, |
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) |
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protect0 = gr.Slider( |
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minimum=0, |
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maximum=0.5, |
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label="Breathiness Protection (0 is enabled, 0.5 is disabled)", |
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value=0.33, |
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step=0.01, |
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interactive=True, |
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) |
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if voice_model != None: vc.get_vc(voice_model.value,protect0,protect0) |
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file_index1 = gr.Textbox( |
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label="Index Path", |
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interactive=True, |
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visible=False |
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) |
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refresh_button.click( |
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fn=change_choices, |
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inputs=[], |
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outputs=[voice_model, file_index2], |
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api_name="infer_refresh", |
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) |
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refresh_button.click( |
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fn=lambda:{"choices":paths_for_files('audios'),"__type__":"update"}, |
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inputs=[], |
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outputs = [input_audio0], |
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) |
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refresh_button.click( |
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fn=lambda:{"value":paths_for_files('audios')[0],"__type__":"update"} if len(paths_for_files('audios')) > 0 else {"value":"","__type__":"update"}, |
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inputs=[], |
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outputs = [input_audio0], |
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) |
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with gr.Row(): |
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f0_file = gr.File(label="F0 Path", visible=False) |
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with gr.Row(): |
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vc_output1 = gr.Textbox(label="Information", placeholder="Welcome!",visible=False) |
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but0.click( |
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vc.vc_single, |
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[ |
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spk_item, |
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input_audio0, |
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vc_transform0, |
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f0_file, |
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f0method0, |
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file_index1, |
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file_index2, |
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index_rate1, |
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filter_radius0, |
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resample_sr0, |
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rms_mix_rate0, |
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protect0, |
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], |
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[vc_output1, vc_output2], |
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api_name="infer_convert", |
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) |
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voice_model.change( |
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fn=vc.get_vc, |
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inputs=[voice_model, protect0, protect0], |
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outputs=[spk_item, protect0, protect0, file_index2, file_index2], |
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api_name="infer_change_voice", |
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) |
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with gr.TabItem("Download Models"): |
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with gr.Row(): |
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url_input = gr.Textbox(label="URL to model", value="",placeholder="https://...", scale=6) |
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name_output = gr.Textbox(label="Save as", value="",placeholder="MyModel",scale=2) |
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url_download = gr.Button(value="Download Model",scale=2) |
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url_download.click( |
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inputs=[url_input,name_output], |
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outputs=[url_input], |
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fn=download_from_url, |
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) |
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with gr.Row(): |
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model_browser = gr.Dropdown(choices=list(model_library.models.keys()),label="OR Search Models (Quality UNKNOWN)",scale=5) |
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download_from_browser = gr.Button(value="Get",scale=2) |
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download_from_browser.click( |
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inputs=[model_browser], |
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outputs=[model_browser], |
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fn=lambda model: download_from_url(model_library.models[model],model), |
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) |
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with gr.TabItem("Train"): |
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with gr.Row(): |
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with gr.Column(): |
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training_name = gr.Textbox(label="Name your model", value="My-Voice",placeholder="My-Voice") |
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np7 = gr.Slider( |
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minimum=0, |
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maximum=config.n_cpu, |
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step=1, |
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label="Number of CPU processes used to extract pitch features", |
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value=int(np.ceil(config.n_cpu / 1.5)), |
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interactive=True, |
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) |
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sr2 = gr.Radio( |
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label="Sampling Rate", |
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choices=["40k", "32k"], |
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value="32k", |
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interactive=True, |
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visible=False |
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) |
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if_f0_3 = gr.Radio( |
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label="Will your model be used for singing? If not, you can ignore this.", |
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choices=[True, False], |
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value=True, |
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interactive=True, |
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visible=False |
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) |
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version19 = gr.Radio( |
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label="Version", |
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choices=["v1", "v2"], |
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value="v2", |
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interactive=True, |
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visible=False, |
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) |
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dataset_folder = gr.Textbox( |
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label="dataset folder", value='dataset' |
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) |
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easy_uploader = gr.Files(label="Drop your audio files here",file_types=['audio']) |
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info1 = gr.Textbox(label="Process Information", value="",visible=True) |
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easy_uploader.upload(inputs=[dataset_folder],outputs=[],fn=lambda folder:os.makedirs(folder,exist_ok=True)) |
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easy_uploader.upload( |
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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'), |
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inputs=[easy_uploader, dataset_folder], |
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outputs=[]) |
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gpus6 = gr.Textbox( |
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label="Enter the GPU numbers to use separated by -, (e.g. 0-1-2)", |
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value=gpus, |
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interactive=True, |
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visible=F0GPUVisible, |
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) |
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gpu_info9 = gr.Textbox( |
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label="GPU Info", value=gpu_info, visible=F0GPUVisible |
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) |
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spk_id5 = gr.Slider( |
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minimum=0, |
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maximum=4, |
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step=1, |
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label="Speaker ID", |
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value=0, |
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interactive=True, |
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visible=False |
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) |
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but1.click( |
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preprocess_dataset, |
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[dataset_folder, training_name, sr2, np7], |
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[info1], |
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api_name="train_preprocess", |
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) |
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with gr.Column(): |
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f0method8 = gr.Radio( |
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label="F0 extraction method", |
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choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], |
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value="rmvpe_gpu", |
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interactive=True, |
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) |
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gpus_rmvpe = gr.Textbox( |
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label="GPU numbers to use separated by -, (e.g. 0-1-2)", |
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value="%s-%s" % (gpus, gpus), |
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interactive=True, |
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visible=F0GPUVisible, |
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) |
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info2 = gr.Textbox(label="Extract Information", value="", max_lines=8) |
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f0method8.change( |
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fn=change_f0_method, |
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inputs=[f0method8], |
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outputs=[gpus_rmvpe], |
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) |
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but2.click( |
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extract_f0_feature, |
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[ |
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gpus6, |
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np7, |
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f0method8, |
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if_f0_3, |
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training_name, |
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version19, |
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gpus_rmvpe, |
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], |
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[info2], |
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api_name="train_extract_f0_feature", |
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) |
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with gr.Column(): |
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total_epoch11 = gr.Slider( |
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minimum=2, |
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maximum=1000, |
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step=1, |
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label="Epochs (more epochs may improve quality but takes longer)", |
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value=150, |
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interactive=True, |
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) |
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with gr.Column(): |
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but1 = gr.Button("1. Process", variant="primary") |
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with gr.Column(): |
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but2 = gr.Button("2. Extract Features", variant="primary") |
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with gr.Column(): |
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but4 = gr.Button("3. Train Index", variant="primary") |
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with gr.Column(): |
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but3 = gr.Button("4. Train Model", variant="primary") |
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info3 = gr.Textbox(label="Information", value="", max_lines=10) |
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with gr.Accordion(label="General Settings", open=False): |
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gpus16 = gr.Textbox( |
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label="GPUs separated by -, (e.g. 0-1-2)", |
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value="0", |
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interactive=True, |
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visible=True |
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) |
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save_epoch10 = gr.Slider( |
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minimum=1, |
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maximum=50, |
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step=1, |
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label="Weight Saving Frequency", |
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value=25, |
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interactive=True, |
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) |
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batch_size12 = gr.Slider( |
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minimum=1, |
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maximum=40, |
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step=1, |
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label="Batch Size", |
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value=default_batch_size, |
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interactive=True, |
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) |
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if_save_latest13 = gr.Radio( |
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label="Only save the latest model", |
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choices=["yes", "no"], |
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value="yes", |
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interactive=True, |
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visible=False |
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) |
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if_cache_gpu17 = gr.Radio( |
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label="If your dataset is UNDER 10 minutes, cache it to train faster", |
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choices=["yes", "no"], |
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value="no", |
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interactive=True, |
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) |
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if_save_every_weights18 = gr.Radio( |
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label="Save small model at every save point", |
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choices=["yes", "no"], |
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value="yes", |
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interactive=True, |
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) |
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with gr.Accordion(label="Change pretrains", open=False): |
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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] |
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pretrained_G14 = gr.Dropdown( |
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label="pretrained G", |
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choices = pretrained(sr2.value, 'G'), |
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value=pretrained(sr2.value, 'G')[0] if len(pretrained(sr2.value, 'G')) > 0 else '', |
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interactive=True, |
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visible=True |
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) |
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pretrained_D15 = gr.Dropdown( |
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label="pretrained D", |
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choices = pretrained(sr2.value, 'D'), |
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value= pretrained(sr2.value, 'D')[0] if len(pretrained(sr2.value, 'G')) > 0 else '', |
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visible=True, |
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interactive=True |
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) |
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with gr.Row(): |
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download_model = gr.Button('5.Download Model') |
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with gr.Row(): |
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model_files = gr.Files(label='Your Model and Index file can be downloaded here:') |
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download_model.click( |
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fn=lambda name: os.listdir(f'assets/weights/{name}') + glob.glob(f'logs/{name.split(".")[0]}/added_*.index'), |
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inputs=[training_name], |
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outputs=[model_files, info3]) |
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with gr.Row(): |
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sr2.change( |
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change_sr2, |
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[sr2, if_f0_3, version19], |
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[pretrained_G14, pretrained_D15], |
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) |
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version19.change( |
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change_version19, |
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[sr2, if_f0_3, version19], |
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[pretrained_G14, pretrained_D15, sr2], |
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) |
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if_f0_3.change( |
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change_f0, |
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[if_f0_3, sr2, version19], |
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[f0method8, pretrained_G14, pretrained_D15], |
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) |
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with gr.Row(): |
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but5 = gr.Button("1 Click Training", variant="primary", visible=False) |
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but3.click( |
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click_train, |
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[ |
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training_name, |
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sr2, |
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if_f0_3, |
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spk_id5, |
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save_epoch10, |
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total_epoch11, |
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batch_size12, |
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if_save_latest13, |
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pretrained_G14, |
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pretrained_D15, |
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gpus16, |
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if_cache_gpu17, |
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if_save_every_weights18, |
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version19, |
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], |
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info3, |
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api_name="train_start", |
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) |
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but4.click(train_index, [training_name, version19], info3) |
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but5.click( |
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train1key, |
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[ |
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training_name, |
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sr2, |
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if_f0_3, |
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dataset_folder, |
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spk_id5, |
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np7, |
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f0method8, |
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save_epoch10, |
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total_epoch11, |
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batch_size12, |
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if_save_latest13, |
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pretrained_G14, |
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pretrained_D15, |
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gpus16, |
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if_cache_gpu17, |
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if_save_every_weights18, |
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version19, |
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gpus_rmvpe, |
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], |
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info3, |
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api_name="train_start_all", |
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) |
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|
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if config.iscolab: |
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app.queue(concurrency_count=511, max_size=1022).launch(share=True) |
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else: |
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app.queue(concurrency_count=511, max_size=1022).launch( |
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server_name="0.0.0.0", |
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inbrowser=not config.noautoopen, |
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server_port=config.listen_port, |
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quiet=True, |
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) |
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