InstructS2S-200K

# InstructS2S-200K

## Dataset Description

**InstructS2S-200K** is a multi-turn speech-to-speech conversation dataset containing approximately 200,000 dialogues, developed for the LLaMA-Omni and LLaMA-Omni 2 research projects on real-time spoken chatbots.

## Usage

The dataset is split into multiple parts and needs to be reconstructed:

```bash
# Combine the parts and extract
cat en_part_* > instructs2s_200k.tar.gz
tar -xzf instructs2s_200k.tar.gz
```

## License

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. It is available for academic research purposes only, but cannot be used for commercial purposes.

## Citation

```bibtex
@inproceedings{fang2025llamaomni2,
title={LLaMA-Omni 2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis},
author={Fang, Qingkai and Zhou, Yan and Guo, Shoutao and Zhang, Shaolei and Feng, Yang},
booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics},
year={2025}
}

@inproceedings{fang2025llamaomni,
title={LLaMA-Omni: Seamless Speech Interaction with Large Language Models},
author={Fang, Qingkai and Guo, Shoutao and Zhou, Yan and Ma, Zhengrui and Zhang, Shaolei and Feng, Yang},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025}
}
```

# InstructS2S-200K

## 数据集说明
**InstructS2S-200K** 是一款多轮语音-语音对话数据集，包含约20万条对话，专为LLaMA-Omni与LLaMA-Omni 2两项实时口语对话机器人相关研究项目打造。

## 使用方法
本数据集被拆分为多个分片，需进行合并重构后方可使用：
bash
# 合并所有分片并解压
cat en_part_* > instructs2s_200k.tar.gz
tar -xzf instructs2s_200k.tar.gz

## 许可协议
本数据集采用知识共享署名-非商业性使用4.0国际许可协议（Creative Commons Attribution-NonCommercial 4.0 International，CC BY-NC 4.0）发布，仅可用于学术研究用途，不得用于商业用途。

## 引用格式
bibtex
@inproceedings{fang2025llamaomni2,
title={LLaMA-Omni 2: 基于大语言模型（Large Language Model，LLM）的自回归流式语音合成实时口语对话机器人},
author={Fang, Qingkai and Zhou, Yan and Guo, Shoutao and Zhang, Shaolei and Feng, Yang},
booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics},
year={2025}
}

@inproceedings{fang2025llamaomni,
title={LLaMA-Omni：与大语言模型（Large Language Model，LLM）的无缝语音交互},
author={Fang, Qingkai and Guo, Shoutao and Zhou, Yan and Ma, Zhengrui and Zhang, Shaolei and Feng, Yang},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025}
}