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Running
on
T4
Running
on
T4
Commit
•
0f1b90d
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Parent(s):
c879373
QOL UI improvements
Browse files- Swapping the output from audio to waveform video
- Shrink the text on the top session, move some of that under the application and leave just the essential on top
- Bug-fix: the duplicate badge was referencing the old Space
- app_batched.py +22 -22
app_batched.py
CHANGED
@@ -58,7 +58,9 @@ def predict(texts, melodies):
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for output in outputs:
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with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
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audio_write(file.name, output, MODEL.sample_rate, strategy="loudness", add_suffix=False)
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-
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return [out_files]
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@@ -67,35 +69,23 @@ with gr.Blocks() as demo:
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"""
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# MusicGen
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This is the demo for MusicGen, a simple and controllable model for music generation
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presented at: "Simple and Controllable Music Generation".
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Enter the description of the music you want and an optional audio used for melody conditioning.
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The model will extract the broad melody from the uploaded wav if provided.
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This will generate a 12s extract with the `melody` model.
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For generating longer sequences (up to 30 seconds) and skipping queue, you can duplicate
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to full demo space, which contains more control and upgrade to GPU in the settings.
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<br/>
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<a href="https://huggingface.co/spaces/
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<img style="margin-
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</p>
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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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)
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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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text = gr.Text(label="
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melody = gr.Audio(source="upload", type="numpy", label="
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with gr.Row():
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submit = gr.Button("
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with gr.Column():
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output = gr.
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submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=12)
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gr.Examples(
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fn=predict,
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@@ -124,5 +114,15 @@ with gr.Blocks() as demo:
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inputs=[text, melody],
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outputs=[output]
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)
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demo.queue(max_size=15).launch()
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for output in outputs:
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with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
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audio_write(file.name, output, MODEL.sample_rate, strategy="loudness", add_suffix=False)
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waveform_video = gr.make_waveform(file.name)
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out_files.append(waveform_video)
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print(out_files)
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return [out_files]
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"""
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# MusicGen
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This is the demo for [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).
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<br/>
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<a href="https://huggingface.co/spaces/facebook/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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text = gr.Text(label="Describe your music", lines=2, interactive=True)
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melody = gr.Audio(source="upload", type="numpy", label="Condition on a melody (optional)", interactive=True)
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with gr.Row():
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submit = gr.Button("Generate")
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with gr.Column():
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output = gr.Video(label="Generated Music")
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submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=12)
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gr.Examples(
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fn=predict,
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inputs=[text, melody],
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outputs=[output]
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)
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gr.Markdown("""
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### More details
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By typing a description of the music you want and an optional audio used for melody conditioning,
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the model will extract the broad melody from the uploaded wav if provided and generate a 12s extract with the `melody` model.
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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=15).launch()
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