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import gradio as gr | |
import torch | |
from tangoflux import TangoFluxInference | |
# Initialize model (auto-downloads on first run) | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model = TangoFluxInference(name="declare-lab/TangoFlux", device=device) | |
def generate_audio(prompt, steps, duration): | |
audio = model.generate(prompt=prompt, steps=steps, duration=duration) | |
return 44100, audio | |
# Gradio interface | |
demo = gr.Interface( | |
fn=generate_audio, | |
inputs=[ | |
gr.Textbox(lines=2, label="Prompt"), | |
gr.Slider(25, 100, value=50, step=1, label="Steps"), | |
gr.Slider(1, 30, value=10, step=1, label="Duration (s)") | |
], | |
outputs=gr.Audio(type="numpy", label="Generated Audio"), | |
title="TangoFlux: Text-to-Audio Generation", | |
description="Generate sound effects (e.g., lightning, thunder) using TangoFlux." | |
) | |
demo.queue().launch() | |