InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn
  Dialogue
Authors: name: Wenwen Tong, name: Hewei Guo, name: Dongchuan Ran, name: Jiangnan Chen, name: Jiefan Lu, name: Kaibin Wang, name: Keqiang Li, name: Xiaoxu Zhu, name: Jiakui Li, name: Kehan Li, name: Xueheng Li, name: Lumin Li, name: Chenxu Guo, name: Jiasheng Zhou, name: Jiandong Chen, name: Xianye Wu, name: Jiahao Wang, name: Silei Wu, name: Lei Chen, name: Hanming Deng, name: Yuxuan Song, name: Dinghao Zhou, name: Guiping Zhong, name: Ken Zheng, name: Shiyin Kang, name: Lewei Lu

We introduce InteractiveOmni, a unified and open-source omni-modal large
language model for audio-visual multi-turn interaction, ranging from 4B to 8B
parameters, designed to lead the field of lightweight models by offering
comprehensive omni-modal understanding and speech generation capabilities. To
achieve this, we integrate the vision encoder, audio encoder, large language
model, and speech decoder into a unified model for understanding and generation
tasks. We design a multi-stage training strategy to ensure robust cross-modal
capabilities, including pre-training for omni-modal understanding, followed by
post-training with speech conversation and audio-visual interaction. To enable
human-like long-term conversational ability, we meticulously curate a
multi-turn training dataset that enhances the model's ability to handle complex
and multi-turn interactions. To effectively evaluate the multi-turn memory and
speech interaction capabilities, we construct the multi-modal multi-turn memory
benchmark and the multi-turn speech interaction benchmark. Experiments
demonstrate that InteractiveOmni significantly outperforms leading open-source
models and provides a more intelligent multi-turn audio-visual experience,
particularly in its long-term memory capabilities. Notably, InteractiveOmni-4B
is comparable to the much larger model like Qwen2.5-Omni-7B on general
benchmarks, and it can retain 97% of the performance of the InteractiveOmni-8B
while utilizing only 50% of the model size. Achieving state-of-the-art results
against similarly sized models across image, audio, video understanding, and
speech generation tasks, InteractiveOmni is an accessible, open-source
foundation for next-generation intelligent interactive systems.