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)
