Instructions to use stabilityai/stable-audio-open-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Stable Audio Tools
How to use stabilityai/stable-audio-open-1.0 with Stable Audio Tools:
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) - Notebooks
- Google Colab
- Kaggle
Difference between this checkpoint and the repo’s
#27
by nateraw - opened
Hi there! Thanks so much for this massive release :)
I noticed the config here is different than stable audio 1.0 config in the stable audio tools repo. So this is a new model that was previously unreported in the paper?
This is a new model based on the architecture from the Stable Audio 2 paper, but trained on CC data. There’ll be a technical report covering training and metrics for this model in the near future.
thanks!
nateraw changed discussion status to closed