app.py

import os
import torch
import torchaudio
import gradio as gr
import spaces
from einops import rearrange
from huggingface_hub import login
from stable_audio_3 import StableAudioModel

# Authenticate with gated model when HF_TOKEN secret is present
hf_token = os.environ.get("HF_TOKEN")
if hf_token:
    login(token=hf_token)


# Required for ZeroGPU Spaces - must have at least one @spaces.GPU function
@spaces.GPU(duration=1)
def _gpu_startup_check():
    return "GPU check passed"


# Run the check at startup
_gpu_startup_check()


# Model cache
MODEL_CACHE = {}


@spaces.GPU(duration=1)
def load_model(model_name):
    """Load model on demand and cache it."""
    if model_name not in MODEL_CACHE:
        print(f"Loading {model_name} model...")
        model = StableAudioModel.from_pretrained(
            model_name,
            device="cpu"
        )
        MODEL_CACHE[model_name] = model
        print(f"{model_name} loaded successfully!")
    return MODEL_CACHE[model_name]


def generate_audio(prompt, duration, steps, cfg_scale, seed, model_name):
    print(f"Generating with {model_name}: prompt='{prompt}', duration={duration}s, steps={steps}, cfg={cfg_scale}, seed={seed}")

    model = load_model(model_name)

    audio = model.generate(
        prompt=prompt,
        duration=duration,
        steps=steps,
        cfg_scale=cfg_scale,
        seed=seed,
        batch_size=1
    )

    # Post-process: (batch, channels, samples) -> stereo waveform
    audio = rearrange(audio, "b d n -> d (b n)")
    audio = audio.to(torch.float32).clamp(-1, 1).mul(32767).to(torch.int16).cpu()

    output_path = "output.wav"
    torchaudio.save(output_path, audio, 44100)
    print("Generation complete!")
    return output_path


with gr.Blocks(title="Stable Audio 3 Small") as demo:
    gr.Markdown("# 🎵 Stable Audio 3 Small - Music & SFX Generation")
    gr.Markdown("Generate music and sound effects using Stability AI's Stable Audio 3 Small models. Runs on CPU.")

    with gr.Row():
        with gr.Column():
            model_name = gr.Dropdown(
                choices=["small-music", "small-sfx"],
                value="small-music",
                label="Model"
            )
            prompt = gr.Textbox(
                label="Prompt",
                placeholder="Describe the music or sound effect you want to generate...",
                lines=2
            )
            duration = gr.Slider(
                minimum=1, maximum=120, value=30, step=1,
                label="Duration (seconds)"
            )
            steps = gr.Slider(
                minimum=1, maximum=50, value=8, step=1,
                label="Steps"
            )
            cfg_scale = gr.Slider(
                minimum=0.0, maximum=10.0, value=1.0, step=0.1,
                label="CFG Scale"
            )
            seed = gr.Number(
                value=-1, label="Seed (-1 for random)"
            )
            btn = gr.Button("Generate", variant="primary")

        with gr.Column():
            audio_output = gr.Audio(
                label="Generated Audio",
                type="filepath"
            )

    btn.click(
        fn=generate_audio,
        inputs=[prompt, duration, steps, cfg_scale, seed, model_name],
        outputs=audio_output
    )


demo.queue(max_size=4, default_concurrency_limit=1).launch()