Stable Audio Open
<|reference_start|>Stable Audio Open: Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.<|reference_end|>
@article{evans2024stable,
    title={Stable Audio Open},
    author={Zach Evans, Julian D. Parker, CJ Carr, Zack Zukowski, Josiah Taylor,
  Jordi Pons},
    journal={arXiv preprint arXiv:2407.14358},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14358},
    primaryClass={cs.SD cs.AI eess.AS}
}