| import torch |
| import torchaudio |
| from einops import rearrange |
| import gradio as gr |
| from stable_audio_tools import get_pretrained_model |
| from stable_audio_tools.inference.generation import generate_diffusion_cond |
|
|
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| model, config = get_pretrained_model("stabilityai/stable-audio-open-small") |
| model = model.to(device) |
| sample_rate = config["sample_rate"] |
| sample_size = config["sample_size"] |
|
|
| def generate_audio(prompt): |
| conditioning = [{"prompt": prompt, "seconds_total": 11}] |
| with torch.no_grad(): |
| output = generate_diffusion_cond( |
| model, |
| steps=8, |
| conditioning=conditioning, |
| sample_size=sample_size, |
| device=device |
| ) |
| output = rearrange(output, "b d n -> d (b n)") |
| output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu() |
| path = "output.wav" |
| torchaudio.save(path, output, sample_rate) |
| return path |
|
|
| ui = gr.Interface(fn=generate_audio, |
| inputs=gr.Textbox(label="Prompt (e.g. 128 BPM tech house drum loop)"), |
| outputs=gr.Audio(type="filepath"), |
| title="Stable Audio Generator") |
|
|
| ui.launch() |
|
|
