Audio-Omni: Extending Multi-modal Understanding to Versatile Audio Generation and Editing
Recent progress in multimodal models has spurred rapid advances in audio understanding, generation, and editing. However, these capabilities are typically addressed by specialized models, leaving the development of a truly unified framework that can seamlessly integrate all three tasks underexplored. While some pioneering works have explored unifying audio understanding and generation, they often remain confined to specific domains. To address this, we introduce Audio-Omni, the first end-to-end framework to unify generation and editing across general sound, music, and speech domains, with integrated multi-modal understanding capabilities. Our architecture synergizes a frozen Multimodal La...
| Authors | Zeyue Tian, Binxin Yang, Zhaoyang Liu et al. |
|---|---|
| Primary category | cs.SD |
| Published | 12 April 2026 |
| Last updated at source | 26 April 2026 |
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