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Update app.py
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app.py
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@@ -79,18 +79,6 @@ def predict(music_prompt, melody, duration):
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# Split to MusicGen
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This is the demo by @fffiloni for Split to [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
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presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284), using Clip Interrogator to get an image description as init text.
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<br/>
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<a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
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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 sequences, more control and no queue.</p>
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"""
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)
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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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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], outputs=[output])
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gr.Markdown(
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"""
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### More details
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The model will generate a short music extract based on the audio you provided.
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You can generate up to 30 seconds of audio.
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This demo is set to use only the Melody model
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1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
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2. Small -- a 300M transformer decoder conditioned on text only.
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3. Medium -- a 1.5B transformer decoder conditioned on text only.
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4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
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When using `melody`, ou can optionaly provide a reference audio from
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which a broad melody will be extracted. The model will then try to follow both the description and melody provided.
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You can also use your own GPU or a Google Colab by following the instructions on our repo.
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See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
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for more details.
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"""
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demo.queue(max_size=32).launch()
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with gr.Blocks() as demo:
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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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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], outputs=[output])
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demo.queue(max_size=32).launch()
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