Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex
  Capabilities
Authors: name: Zhifei Xie, name: Changqiao Wu

GPT-4o, an all-encompassing model, represents a milestone in the development
of large multi-modal language models. It can understand visual, auditory, and
textual modalities, directly output audio, and support flexible duplex
interaction. Models from the open-source community often achieve some
functionalities of GPT-4o, such as visual understanding and voice chat.
Nevertheless, training a unified model that incorporates all modalities is
challenging due to the complexities of multi-modal data, intricate model
architectures, and training processes. In this paper, we introduce Mini-Omni2,
a visual-audio assistant capable of providing real-time, end-to-end voice
responses to visoin and audio queries. By integrating pretrained visual and
auditory encoders, Mini-Omni2 maintains performance in individual modalities.
We propose a three-stage training process to align modalities, allowing the
language model to handle multi-modal inputs and outputs after training on a
limited dataset. For interaction, we introduce a command-based interruption
mechanism, enabling more flexible interaction with users. To the best of our
knowledge, Mini-Omni2 is one of the closest reproductions of GPT-4o, which have
similar form of functionality, and we hope it can offer valuable insights for
subsequent research.