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---
license: mit
language:
- en
base_model: Qwen/Qwen2-0.5B
tags:
- text-to-speech
- speech-to-speech
---
<p align="center"><strong style="font-size: 18px;">
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming
</strong>
</p>
<p align="center">
π€ <a href="">Hugging Face</a> | π <a href="https://github.com/gpt-omni/mini-omni">Github</a>
| π <a href="https://arxiv.org/abs/2408.16725">Technical report</a>
</p>
Mini-Omni is an open-source multimodel large language model that can **hear, talk while thinking**. Featuring real-time end-to-end speech input and **streaming audio output** conversational capabilities.
<p align="center">
<img src="frameworkv3.jpg" width="100%"/>
</p>
## Features
β
**Real-time speech-to-speech** conversational capabilities. No extra ASR or TTS models required.
β
**Talking while thinking**, with the ability to generate text and audio at the same time.
β
**Streaming audio outupt** capabilities.
β
With "Audio-to-Text" and "Audio-to-Audio" **batch inference** to further boost the performance.
**NOTE**: please refer to the [code repository](https://github.com/gpt-omni/mini-omni) for more details.
## Install
Create a new conda environment and install the required packages:
```sh
conda create -n omni python=3.10
conda activate omni
git clone https://github.com/gpt-omni/mini-omni.git
cd mini-omni
pip install -r requirements.txt
```
## Quick start
**Interactive demo**
- start server
```sh
conda activate omni
cd mini-omni
python3 server.py --ip '0.0.0.0' --port 60808
```
- run streamlit demo
NOTE: you need to run streamlit locally with PyAudio installed.
```sh
pip install PyAudio==0.2.14
API_URL=http://0.0.0.0:60808/chat streamlit run webui/omni_streamlit.py
```
- run gradio demo
```sh
API_URL=http://0.0.0.0:60808/chat python3 webui/omni_gradio.py
```
example:
NOTE: need to unmute first. Gradio seems can not play audio stream instantly, so the latency feels a bit longer.
https://github.com/user-attachments/assets/29187680-4c42-47ff-b352-f0ea333496d9
**Local test**
```sh
conda activate omni
cd mini-omni
# test run the preset audio samples and questions
python inference.py
```
## Acknowledgements
- [Qwen2](https://github.com/QwenLM/Qwen2/) as the LLM backbone.
- [litGPT](https://github.com/Lightning-AI/litgpt/) for training and inference.
- [whisper](https://github.com/openai/whisper/) for audio encoding.
- [snac](https://github.com/hubertsiuzdak/snac/) for audio decoding.
- [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) for generating synthetic speech.
- [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) and [MOSS](https://github.com/OpenMOSS/MOSS/tree/main) for alignment. |