MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech

We present MGM-Omni, an Omni MLLM for omni-modalunderstanding and expressive, long-horizon speech generation. MGM-Omni adopts a “brain–mouth” design with a dual-track, token-based ar-chitecture that cleanly decouples multimodal reasoning from real-timespeech generation. This design enables efficient cross-modal interactionand low-latency, streaming speech generation. For multimodal under-standing, a unified training strategy coupled with a dual audio encoderdesign enables long-form omni modal perception across diverse acous-tic conditions. For speech generation, a chunk-based parallel decodingscheme narrows the text–speech token-rate gap, accelerating inferenceand supporting streaming zero-shot voice cloning with stable timbre overextended durations. Compared to concurrent work, MGM-Omni achievesthese capabilities with markedly data-efficient training. Extensive exper-iments demonstrate that MGM-Omni outperforms existing open sourcemodels in preserving timbre identity across extended sequences, produc-ing natural and context-aware speech, and achieving superior long-formaudio and omnimodal understanding. MGM-Omni establishes an effi-cient, end-to-end paradigm for omnimodal understanding and control-lable, personalized long-horizon speech generation. Code and models areavailable at https://github.com/dvlab-research/MGM-Omni.