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Create app.py
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app.py
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import torch
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import torchaudio
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from einops import rearrange
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from stable_audio_tools import get_pretrained_model
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from stable_audio_tools.inference.generation import generate_diffusion_cond
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import gradio as gr
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import spaces
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# Define the function to generate audio
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@spaces.GPU()
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def generate_audio(prompt, bpm, seconds_total):
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Download model
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model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
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sample_rate = model_config["sample_rate"]
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sample_size = model_config["sample_size"]
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model = model.to(device)
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# Set up text and timing conditioning
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conditioning = [{
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"prompt": f"{bpm} BPM {prompt}",
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"seconds_start": 0,
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"seconds_total": seconds_total
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}]
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# Generate stereo audio
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output = generate_diffusion_cond(
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model,
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steps=100,
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cfg_scale=7,
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conditioning=conditioning,
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sample_size=sample_size,
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sigma_min=0.3,
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sigma_max=500,
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sampler_type="dpmpp-3m-sde",
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device=device
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)
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# Rearrange audio batch to a single sequence
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output = rearrange(output, "b d n -> d (b n)")
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# Peak normalize, clip, convert to int16, and save to file
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output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
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output_path = "output.wav"
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torchaudio.save(output_path, output, sample_rate)
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return output_path
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# Define the Gradio interface
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iface = gr.Interface(
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fn=generate_audio,
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inputs=[
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gr.Textbox(label="Prompt", placeholder="Enter the description of the audio (e.g., tech house drum loop)"),
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gr.Number(label="BPM", value=128),
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gr.Number(label="Duration (seconds)", value=30)
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],
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outputs=gr.Audio(label="Generated Audio"),
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title="Stable Audio Generation",
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description="Generate audio based on a text prompt using stable audio tools.",
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)
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# Launch the interface
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iface.launch()
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