Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex
  Capabilities
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.
6710b98b967512639d50a70e
Zhifei Xie
admin_assigned
2024-10-18T13:28:54.691Z
66c5d81a4061fd5907443787
/avatars/2e107195b1ff7d06bbc6c9bd4e5620cf.svg
zhifei
user
filicos
6710b98b967512639d50a70f
Changqiao Wu
claimed_verified
2024-10-18T08:14:20.838Z
66cdb20c6aee4c865fe9647e
/avatars/d3637e8278c4b14d0f03ebfbfd3d3711.svg
Changqiao Wu
user
gpt-omni
Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex
  Capabilities
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.
Mini-Omni2 is a real-time, end-to-end voice response model that integrates visual and auditory encoders and employs a three-stage training process to handle multi-modal data.
multi-modal language models visual-audio assistant pretrained visual encoders pretrained auditory encoders three-stage training process command-based interruption mechanism