Taming Audio VAEs via Target-KL Regularization

• URL: http://arxiv.org/abs/2605.17085v1
• Date: Sat, 16 May 2026 17:01:47 GMT
• Title: Taming Audio VAEs via Target-KL Regularization
• Abstract summary: We study the impact of target-KL regularization on text-to-sound generation.<n>We find that sweeping compression rates is helpful in identifying the optimal generation setting.
• Score: 6.889976738085788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Latent diffusion models have emerged as the dominant paradigm for many generation tasks including audio generation such as text-to-audio, text-to-music and text-to-speech. A key component of latent diffusion is an autoencoder (VAE) that compresses high-dimensional signals into a low frame rate continuous representation that is conducive for downstream prediction. Regularizing these VAEs is challenging, as there is a trade-off between over-regularized (poor output quality) and under-regularized (difficult to predict) latent representations. We propose a framework for studying this trade-off through compression and train Audio VAEs at specific bitrates via target-KL regularization. This allows direct comparison to well-studied discrete neural audio codec models, and the construction of rate-distortion curves for audio VAEs. We evaluate the impact of target-KL regularization on text-to-sound generation and find that sweeping compression rates is helpful in identifying the optimal generation setting.

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