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arXiv · 2606.04939

UAT: Unified Audio-Text Diffusion for Audio Generation, Editing, and Captioning

Hui Wang·Yifan Yang·Zeyue Tian·Yuhang Jia·Jinghua Zhao·Long Zhou·Bing Han·Cheng Liu·Jiaming Zhou·Geng Tu·Yong Qin
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Abstract

Audio generation and audio-to-text understanding remain largely separate, with diffusion models dominating high-fidelity synthesis and autoregressive (AR) language models driving captioning and semantic prediction. Existing unified approaches typically rely on either heterogeneous modules or AR-centric modeling, which can hinder joint optimization and limit acoustic fidelity. We present UAT, to our knowledge, the first diffusion-centric framework that supports unified audio generation, editing, and captioning. UAT couples continuous latent diffusion for audio with masked discrete diffusion for text, enabling bidirectional audio-text modeling within a shared dual-stream backbone. Experiments show that UAT preserves strong audio generation and editing capabilities while achieving competitive captioning performance, demonstrating a favorable balance between acoustic synthesis and semantic prediction. Demo samples are available at https://UAT-demo.github.io.

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Hui Wang, Yifan Yang, Zeyue Tian, Yuhang Jia, Jinghua Zhao, Long Zhou, Bing Han, Cheng Liu, Jiaming Zhou, Geng Tu, Yong Qin. 2026-06-03. UAT: Unified Audio-Text Diffusion for Audio Generation, Editing, and Captioning. https://arxiv.org/abs/2606.04939

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