MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911183893692416 |
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| author | Wang, Chengyao Zhong, Zhisheng Peng, Bohao Yang, Senqiao Liu, Yuqi Gui, Haokun Xia, Bin Li, Jingyao Yu, Bei Jia, Jiaya |
| author_facet | Wang, Chengyao Zhong, Zhisheng Peng, Bohao Yang, Senqiao Liu, Yuqi Gui, Haokun Xia, Bin Li, Jingyao Yu, Bei Jia, Jiaya |
| contents | We present MGM-Omni, a unified Omni LLM for omni-modal understanding and expressive, long-horizon speech generation. Unlike cascaded pipelines that isolate speech synthesis, MGM-Omni adopts a "brain-mouth" design with a dual-track, token-based architecture that cleanly decouples multimodal reasoning from real-time speech generation. This design enables efficient cross-modal interaction and low-latency, streaming speech generation. For understanding, a unified training strategy coupled with a dual audio encoder design enables long-form audio perception across diverse acoustic conditions. For generation, a chunk-based parallel decoding scheme narrows the text speech token-rate gap, accelerating inference and supporting streaming zero-shot voice cloning with stable timbre over extended durations. Compared to concurrent work, MGM-Omni achieves these capabilities with markedly data-efficient training. Extensive experiments demonstrate that MGM-Omni outperforms existing open source models in preserving timbre identity across extended sequences, producing natural and context-aware speech, and achieving superior long-form audio and omnimodal understanding. MGM-Omni establishes an efficient, end-to-end paradigm for omnimodal understanding and controllable, personalised long-horizon speech generation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_25131 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech Wang, Chengyao Zhong, Zhisheng Peng, Bohao Yang, Senqiao Liu, Yuqi Gui, Haokun Xia, Bin Li, Jingyao Yu, Bei Jia, Jiaya Sound Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Multimedia We present MGM-Omni, a unified Omni LLM for omni-modal understanding and expressive, long-horizon speech generation. Unlike cascaded pipelines that isolate speech synthesis, MGM-Omni adopts a "brain-mouth" design with a dual-track, token-based architecture that cleanly decouples multimodal reasoning from real-time speech generation. This design enables efficient cross-modal interaction and low-latency, streaming speech generation. For understanding, a unified training strategy coupled with a dual audio encoder design enables long-form audio perception across diverse acoustic conditions. For generation, a chunk-based parallel decoding scheme narrows the text speech token-rate gap, accelerating inference and supporting streaming zero-shot voice cloning with stable timbre over extended durations. Compared to concurrent work, MGM-Omni achieves these capabilities with markedly data-efficient training. Extensive experiments demonstrate that MGM-Omni outperforms existing open source models in preserving timbre identity across extended sequences, producing natural and context-aware speech, and achieving superior long-form audio and omnimodal understanding. MGM-Omni establishes an efficient, end-to-end paradigm for omnimodal understanding and controllable, personalised long-horizon speech generation. |
| title | MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech |
| topic | Sound Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2509.25131 |