Stable Audio 3

Computer Science > Sound arXiv:2605.17991 (cs) [Submitted on 18 May 2026] Title:Stable Audio 3 Authors:Zach Evans, Julian D. Parker, Matthew Rice, CJ Carr, Zack Zukowski, Josiah Taylor, Jordi Pons View a PDF of the paper titled Stable Audio 3, by Zach Evans and 6 other authors View PDF HTML (experimental)

Abstract:Stable Audio 3 is a family of fast latent diffusion models (small, medium, large) for variable-length audio generation and editing. Since our models can generate several minutes of audio, variable-length generations are key to avoid the cost of producing full-length generations for short sounds. We also support inpainting, enabling targeted audio editing and the continuation of short recordings. Our latent diffusion models operate on top of a novel semantic-acoustic autoencoder that projects audio into a compact latent space, enabling efficient diffusion-based generation while preserving audio fidelity and encouraging semantic structure in the latent. Finally, we run adversarial post-training to both accelerate inference and improve generation quality, reducing the number of inference steps while improving fidelity and prompt adherence. Stable Audio 3 models are trained on licensed and Creative Commons data to generate music and sounds in less than a 2s on an H200 GPU and less than a few seconds on a MacBook Pro M4. We release the weights of small and medium, that can run on consumer-grade hardware, together with their training and inference pipeline.

Comments:	Training code: this https URL Inference and weights: this http URL
Subjects:	Sound (cs.SD); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2605.17991 [cs.SD]
	(or arXiv:2605.17991v1 [cs.SD] for this version)
	https://doi.org/10.48550/arXiv.2605.17991 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jordi Pons [view email]
[v1] Mon, 18 May 2026 07:47:03 UTC (67 KB)
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