Zeyue Tian, Binxin Yang, Zhaoyang Liu, Jiexuan Zhang, Ruibin Yuan, Hubery Yin, Qifeng Chen, Chen Li, Jing Lyu, Wei Xue, Yike Guo

Researchers have introduced Audio-Omni, a unified framework that combines audio understanding, generation, and editing across sound, music, and speech in a single model. The system pairs a frozen multimodal large language model for reasoning with a trainable Diffusion Transformer for synthesis. To address the scarcity of audio editing training data, the team created AudioEdit, a dataset of over one million curated editing pairs. The framework achieves state-of-the-art results on multiple benchmarks and demonstrates emergent capabilities including knowledge-augmented reasoning, in-context generation, and zero-shot cross-lingual control.