Update app.py
Browse files
app.py
CHANGED
@@ -92,8 +92,7 @@ with gr.Blocks(css=css) as demo:
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Upload an audio file, split audio tracks with Demucs, choose a track as conditional sound for MusicGen, get a remix !
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*** Careful, MusicGen model loaded here can only handle up to 30 second audio, please use the audio component gradio feature to edit your audio before conditioning ***
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<br/>
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<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
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for longer audio, more control and no queue.</p>
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"""
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)
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@@ -102,14 +101,14 @@ with gr.Blocks(css=css) as demo:
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uploaded_sound = gr.Audio(type="numpy", label="Input", source="upload")
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with gr.Row():
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chosen_track = gr.Radio(["vocals", "bass", "drums", "other", "all-in"], label="Track", info="Which track from your audio do you want to mashup ?", value="vocals")
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load_sound_btn = gr.Button('Load your
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#split_vocals = gr.Audio(type="filepath", label="Vocals")
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#split_bass = gr.Audio(type="filepath", label="Bass")
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#split_drums = gr.Audio(type="filepath", label="Drums")
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#split_others = gr.Audio(type="filepath", label="Other")
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with gr.Row():
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music_prompt = gr.Textbox(label="Musical Prompt", info="Describe what kind of music you wish for", interactive=True)
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melody = gr.Audio(source="upload", type="numpy", label="Track Condition (from previous step)", interactive=False)
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with gr.Row():
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model = gr.Radio(["melody", "medium", "small", "large"], label="MusicGen Model", value="melody", interactive=True)
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@@ -123,6 +122,40 @@ with gr.Blocks(css=css) as demo:
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# cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
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output = gr.Audio(label="Generated Music")
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load_sound_btn.click(split_process, inputs=[uploaded_sound, chosen_track], outputs=[melody])
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submit.click(predict, inputs=[music_prompt, melody, duration, model], outputs=[output])
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Upload an audio file, split audio tracks with Demucs, choose a track as conditional sound for MusicGen, get a remix !
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*** Careful, MusicGen model loaded here can only handle up to 30 second audio, please use the audio component gradio feature to edit your audio before conditioning ***
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<br/>
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+
[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg)](https://huggingface.co/spaces/fffiloni/SplitTrack2MusicGen?duplicate=true)
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for longer audio, more control and no queue.</p>
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"""
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)
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uploaded_sound = gr.Audio(type="numpy", label="Input", source="upload")
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with gr.Row():
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chosen_track = gr.Radio(["vocals", "bass", "drums", "other", "all-in"], label="Track", info="Which track from your audio do you want to mashup ?", value="vocals")
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load_sound_btn = gr.Button('Load your chosen track')
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#split_vocals = gr.Audio(type="filepath", label="Vocals")
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#split_bass = gr.Audio(type="filepath", label="Bass")
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#split_drums = gr.Audio(type="filepath", label="Drums")
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#split_others = gr.Audio(type="filepath", label="Other")
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with gr.Row():
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music_prompt = gr.Textbox(label="Musical Prompt", info="Describe what kind of music you wish for", interactive=True, placeholder="lofi slow bpm electro chill with organic samples")
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melody = gr.Audio(source="upload", type="numpy", label="Track Condition (from previous step)", interactive=False)
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with gr.Row():
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model = gr.Radio(["melody", "medium", "small", "large"], label="MusicGen Model", value="melody", interactive=True)
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# cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
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output = gr.Audio(label="Generated Music")
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gr.Examples(
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fn=predict,
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examples=[
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[
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"An 80s driving pop song with heavy drums and synth pads in the background",
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None,
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10,
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"melody"
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],
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[
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"A cheerful country song with acoustic guitars",
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None,
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10,
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"melody"
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],
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[
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"90s rock song with electric guitar and heavy drums",
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None,
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],
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[
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"a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
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None,
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10,
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"melody"
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],
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[
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"lofi slow bpm electro chill with organic samples",
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None,
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],
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],
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inputs=[music_prompt, melody, duration, model],
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outputs=[output]
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)
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load_sound_btn.click(split_process, inputs=[uploaded_sound, chosen_track], outputs=[melody])
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submit.click(predict, inputs=[music_prompt, melody, duration, model], outputs=[output])
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