Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -46,47 +46,77 @@ def gradio_generate(prompt, steps, guidance, duration):
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torchaudio.save(filename, output, 44100)
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return filename
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# Launch the interface
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gr_interface.queue(15).launch()
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torchaudio.save(filename, output, 44100)
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return filename
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# Create custom interface with HTML badges
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with gr.Blocks(theme="soft") as gr_interface:
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# Add HTML badges at the top
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gr.HTML(
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"""
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<div class='container' style='display:flex; justify-content:center; gap:12px;'>
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<a href="https://huggingface.co/spaces/openfree/Best-AI" target="_blank">
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<img src="https://img.shields.io/static/v1?label=OpenFree&message=BEST%20AI%20Services&color=%230000ff&labelColor=%23000080&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="OpenFree badge">
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</a>
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<a href="https://discord.gg/openfreeai" target="_blank">
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<img src="https://img.shields.io/static/v1?label=Discord&message=Openfree%20AI&color=%230000ff&labelColor=%23800080&logo=discord&logoColor=white&style=for-the-badge" alt="Discord badge">
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</a>
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</div>
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"""
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)
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# Title and description
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gr.Markdown("# TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization")
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gr.Markdown(description_text)
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# Input components
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with gr.Row():
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with gr.Column():
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input_text = gr.Textbox(lines=2, label="Prompt")
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with gr.Row():
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denoising_steps = gr.Slider(minimum=10, maximum=100, value=25, step=5, label="Steps", interactive=True)
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guidance_scale = gr.Slider(minimum=1, maximum=10, value=4.5, step=0.5, label="Guidance Scale", interactive=True)
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duration_scale = gr.Slider(minimum=1, maximum=30, value=10, step=1, label="Duration", interactive=True)
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submit_btn = gr.Button("Generate Audio", variant="primary")
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with gr.Column():
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output_audio = gr.Audio(label="Generated Audio", type="filepath")
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# Examples
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gr.Examples(
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examples=[
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# [prompt, steps, guidance, duration]
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["Quiet whispered conversation gradually fading into distant jet engine roar diminishing into silence", 25, 4.5, 10],
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["Clear sound of bicycle tires crunching on loose gravel and dirt, followed by deep male laughter echoing", 25, 4.5, 10],
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["Multiple ducks quacking loudly with splashing water and piercing wild animal shriek in background", 25, 4.5, 10],
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["Powerful ocean waves crashing and receding on sandy beach with distant seagulls", 25, 4.5, 10],
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["Gentle female voice cooing and baby responding with happy gurgles and giggles", 25, 4.5, 10],
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["Clear male voice speaking, sharp popping sound, followed by genuine group laughter", 25, 4.5, 10],
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["Stream of water hitting empty ceramic cup, pitch rising as cup fills up", 25, 4.5, 10],
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["Massive crowd erupting in thunderous applause and excited cheering", 25, 4.5, 10],
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["Deep rolling thunder with bright lightning strikes crackling through sky", 25, 4.5, 10],
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["Aggressive dog barking and distressed cat meowing as racing car roars past at high speed", 25, 4.5, 10],
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["Peaceful stream bubbling and birds singing, interrupted by sudden explosive gunshot", 25, 4.5, 10],
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["Man speaking outdoors, goat bleating loudly, metal gate scraping closed, ducks quacking frantically, wind howling into microphone", 25, 4.5, 10],
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["Series of loud aggressive dog barks echoing", 25, 4.5, 10],
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["Multiple distinct cat meows at different pitches", 25, 4.5, 10],
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["Rhythmic wooden table tapping overlaid with steady water pouring sound", 25, 4.5, 10],
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["Sustained crowd applause with camera clicks and amplified male announcer voice", 25, 4.5, 10],
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["Two sharp gunshots followed by panicked birds taking flight with rapid wing flaps", 25, 4.5, 10],
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["Melodic human whistling harmonizing with natural birdsong", 25, 4.5, 10],
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["Deep rhythmic snoring with clear breathing patterns", 25, 4.5, 10],
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["Multiple racing engines revving and accelerating with sharp whistle piercing through", 25, 4.5, 10],
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],
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inputs=[input_text, denoising_steps, guidance_scale, duration_scale],
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outputs=output_audio,
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fn=gradio_generate,
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cache_examples="lazy",
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)
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# Connect the button click to the generation function
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submit_btn.click(
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fn=gradio_generate,
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inputs=[input_text, denoising_steps, guidance_scale, duration_scale],
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outputs=output_audio
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)
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# Launch the interface
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gr_interface.queue(15).launch()
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