  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.
