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---
license: gpl-3.0
pipeline_tag: any-to-any
tags:
- omni
---
# Stream-Omni: Simultaneous Multimodal Interactions with Large Language-Vision-Speech Model
[](https://arxiv.org/abs/2506.13642)
[](https://github.com/ictnlp/Stream-Omni)
[](https://huggingface.co/ICTNLP/stream-omni-8b)
[](https://huggingface.co/datasets/ICTNLP/InstructOmni)
[](https://github.com/ictnlp/Stream-Omni)
> [**Shaolei Zhang**](https://zhangshaolei1998.github.io/), [**Shoutao Guo**](https://scholar.google.com.hk/citations?user=XwHtPyAAAAAJ), [**Qingkai Fang**](https://fangqingkai.github.io/), [**Yan Zhou**](https://zhouyan19.github.io/zhouyan/), [**Yang Feng**](https://people.ucas.edu.cn/~yangfeng?language=en)\*
The introduction and usage of Stream-Omni refer to [https://github.com/ictnlp/Stream-Omni](https://github.com/ictnlp/Stream-Omni).
Stream-Omni is an end-to-end language-vision-speech chatbot that simultaneously supports interaction across various modality combinations, with the following features💡:
- **Omni Interaction**: Support any multimodal inputs including text, vision, and speech, and generate both text and speech responses.
- **Seamless "see-while-hear" Experience**: Simultaneously output *intermediate textual results* (e.g., ASR transcriptions and model responses) during speech interactions, like the advanced voice service of GPT-4o.
- **Efficient Training**: Require only a small amount of omni-modal data for training.
<p align="center" width="100%">
<img src="./stream-omni.png" alt="stream-omni" style="width: 90%; min-width: 300px; display: block; margin: auto;">
</p>
## 🖥 Demo
| Microphone Input | File Input |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| <video src='https://github.com/user-attachments/assets/99325a91-b81c-4fd1-a68e-c0628d5784fe' width="100%"/> | <video src='https://github.com/user-attachments/assets/bcb37b15-6c8c-4eae-845e-5da10aaac11e' width="100%"/> |
> [!NOTE]
>
> **Stream-Omni can produce intermediate textual results (ASR transcription and text response) during speech interaction, offering users a seamless "see-while-hear" experience.**
|