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Audio-Omni framework unifies audio generation, editing, and understanding

Researchers have introduced Audio-Omni, a novel framework designed to unify audio understanding, generation, and editing across diverse domains like speech, music, and general sounds. This system integrates a frozen Multimodal Large Language Model with a trainable Diffusion Transformer, addressing the challenge of data scarcity in audio editing with a new dataset called AudioEdit. Experiments indicate that Audio-Omni achieves state-of-the-art results, rivaling specialized models and demonstrating advanced capabilities such as knowledge-augmented reasoning and zero-shot cross-lingual control. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a unified framework for audio tasks, potentially advancing generative audio intelligence and cross-modal applications.

RANK_REASON This is a research paper introducing a new framework and dataset for audio processing.

Read on arXiv cs.CV →

COVERAGE [1]

  1. arXiv cs.CV TIER_1 · Zeyue Tian, Binxin Yang, Zhaoyang Liu, Jiexuan Zhang, Ruibin Yuan, Hubery Yin, Qifeng Chen, Chen Li, Jing Lyu, Wei Xue, Yike Guo ·

    Audio-Omni: Extending Multi-modal Understanding to Versatile Audio Generation and Editing

    arXiv:2604.10708v2 Announce Type: replace-cross Abstract: 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…