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 Large Language Model for high-level reasoning with a trainable Diffusion Transformer for high-fidelity synthesis. To overcome the critical data scarcity in audio editing, we construct AudioEdit, a new large-scale dataset comprising over one million meticulously curated editing pairs. Extensive experiments demonstrate that Audio-Omni achieves state-of-the-art performance across a suite of benchmarks, outperforming prior unified approaches while achieving performance on par with or superior to specialized expert models. Beyond its core capabilities, Audio-Omni exhibits remarkable inherited capabilities, including knowledge-augmented reasoning generation, in-context generation, and zero-shot cross-lingual control for audio generation, highlighting a promising direction toward universal generative audio intelligence. The code, model, and dataset will be publicly released on https://zeyuet.github.io/Audio-Omni.
69dec0d5d3c84fcbf29214e7
Zeyue Tian
69dec0d5d3c84fcbf29214e8
Binxin Yang
69dec0d5d3c84fcbf29214e9
Zhaoyang Liu
69dec0d5d3c84fcbf29214ea
Jiexuan Zhang
69dec0d5d3c84fcbf29214eb
Ruibin Yuan
69dec0d5d3c84fcbf29214ec
Hubery Yin
69dec0d5d3c84fcbf29214ed
Qifeng Chen
69dec0d5d3c84fcbf29214ee
Chen Li
69dec0d5d3c84fcbf29214ef
Jing Lv
69dec0d5d3c84fcbf29214f0
Wei Xue
69dec0d5d3c84fcbf29214f1
Yike Guo
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 Large Language Model for high-level reasoning with a trainable Diffusion Transformer for high-fidelity synthesis. To overcome the critical data scarcity in audio editing, we construct AudioEdit, a new large-scale dataset comprising over one million meticulously curated editing pairs. Extensive experiments demonstrate that Audio-Omni achieves state-of-the-art performance across a suite of benchmarks, outperforming prior unified approaches while achieving performance on par with or superior to specialized expert models. Beyond its core capabilities, Audio-Omni exhibits remarkable inherited capabilities, including knowledge-augmented reasoning generation, in-context generation, and zero-shot cross-lingual control for audio generation, highlighting a promising direction toward universal generative audio intelligence. The code, model, and dataset will be publicly released on https://zeyuet.github.io/Audio-Omni.
Audio-Omni presents the first end-to-end framework unifying audio generation and editing across sound, music, and speech domains using a frozen multimodal language model and trainable diffusion transformer architecture.
Multimodal Large Language Model Diffusion Transformer audio editing audio generation audio understanding frozen model trainable model large-scale dataset AudioEdit universal generative audio intelligence