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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="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue="zinc")) as app:
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with gr.Row():
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gr.HTML("<img src='file/a.png' alt='image'>")
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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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but1 = gr.Button("1. Process", variant="primary")
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info1 = gr.Textbox(label="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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but2 = gr.Button("2. Extract Features", variant="primary")
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info2 = gr.Textbox(label="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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but4 = gr.Button("3. Train Index", variant="primary")
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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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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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|
