Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex Capabilities

• URL: http://arxiv.org/abs/2410.11190v3
• Date: Tue, 05 Nov 2024 02:27:57 GMT
• Title: Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex Capabilities
• Authors: Zhifei Xie, Changqiao Wu,
• Abstract summary: Mini- Omni2 is a visual-audio assistant capable of providing real-time, end-to-end voice responses to visoin and audio queries. 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.
• Score: 0.0
• License:
• Abstract: 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.

Related papers

• Ocean-omni: To Understand the World with Omni-modality [28.306965534325904]
  Ocean-omni is the first open-source 7B Multimodal Large Language Model (MLLM) We introduce Ocean-omni, the first open-source 7B Multimodal Large Language Model (MLLM)
  arXiv  Detail & Related papers  (2024-10-11T06:44:31Z)
• MIO: A Foundation Model on Multimodal Tokens [74.85153216521945]
  We introduce MIO, a novel foundation model built on multimodal tokens. MIO is capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
  arXiv  Detail & Related papers  (2024-09-26T09:57:16Z)
• Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming [0.0]
  Mini- Omni is an audio-based end-to-end conversational model capable of real-time speech interaction. We propose a text-instructed speech generation method, along with batch-parallel strategies during inference to boost the performance. We also introduce the VoiceAssistant-400K dataset to fine-tune models for optimized speech output.
  arXiv  Detail & Related papers  (2024-08-29T17:18:53Z)
• Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action [46.76487873983082]
  Unified-IO 2 is the first autoregressive multimodal model capable of understanding and generating image, text, audio, and action. We train our model from scratch on a large multimodal pre-training corpus from diverse sources. With a single unified model, Unified-IO 2 achieves state-of-the-art performance on the GRIT benchmark.
  arXiv  Detail & Related papers  (2023-12-28T17:57:06Z)
• ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities [71.15303690248021]
  We release ONE-PEACE, a highly model with 4B parameters that can seamlessly align and integrate representations across vision, audio, and language modalities. The architecture of ONE-PEACE comprises modality adapters, shared self-attention layers, and modality FFNs. With the scaling-friendly architecture and pretraining tasks, ONE-PEACE has the potential to expand to unlimited modalities.
  arXiv  Detail & Related papers  (2023-05-18T17:59:06Z)
• mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality [95.76661165594884]
  mPLUG-Owl is a training paradigm that equips large language models (LLMs) with multi-modal abilities. The training paradigm involves a two-stage method for aligning image and text, which learns visual knowledge with the assistance of LLM. Experimental results show that our model outperforms existing multi-modal models.
  arXiv  Detail & Related papers  (2023-04-27T13:27:01Z)
• mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video [89.19867891570945]
  mPLUG-2 is a new unified paradigm with modularized design for multi-modal pretraining. It shares common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement. It is flexible to select different modules for different understanding and generation tasks across all modalities including text, image, and video.
  arXiv  Detail & Related papers  (2023-02-01T12:40:03Z)
• i-Code: An Integrative and Composable Multimodal Learning Framework [99.56065789066027]
  i-Code is a self-supervised pretraining framework where users may flexibly combine the modalities of vision, speech, and language into unified and general-purpose vector representations. The entire system is pretrained end-to-end with new objectives including masked modality unit modeling and cross-modality contrastive learning. Experimental results demonstrate how i-Code can outperform state-of-the-art techniques on five video understanding tasks and the GLUE NLP benchmark, improving by as much as 11%.
  arXiv  Detail & Related papers  (2022-05-03T23:38:50Z)
• Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model [51.42415340921237]
  We propose a Dynamic Inter- and Intra-modality Attention (DIIA) model to fuse the two modalities (audio and textual) We further develop a Multimodal Knowledge Distillation (MKD) module to enable our multimodal MC model to accurately predict the answers based only on either the text or the audio.
  arXiv  Detail & Related papers  (2021-07-04T08:35:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.