bouloulou

import torch import torchaudio from einops import rearrange 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"

Download model

model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0") sample_rate = model_config["sample_rate"] sample_size = model_config["sample_size"]

model = model.to(device)

Set up text and timing conditioning

conditioning = [{ "prompt": "128 BPM tech house drum loop", }]

Generate stereo audio

output = generate_diffusion_cond( model, conditioning=conditioning, sample_size=sample_size, device=device )

Rearrange audio batch to a single sequence

output = rearrange(output, "b d n -> d (b n)")

Peak normalize, clip, convert to int16, and save to file

output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu() torchaudio.save("output.wav", output, sample_rate)