| 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() | |