Easy-Turn-Trainset

# Easy Turn: Integrating Acoustic and Linguistic Modalities for Robust Turn-Taking in Full-Duplex Spoken Dialogue Systems

<p align="center">
Guojian Li¹, Chengyou Wang¹, Hongfei Xue¹,
Shuiyuan Wang¹, Dehui Gao¹, Zihan Zhang²,
Yuke Lin², Wenjie Li², Longshuai Xiao²,
Zhonghua Fu¹^,╀, Lei Xie¹^,╀
</p>

<p align="center">
¹ Audio, Speech and Language Processing Group (ASLP@NPU), Northwestern Polytechnical University <br>
² Huawei Technologies, China <br>
</p>

<div align="center">

| 🎤 [Demo Page](https://aslp-lab.github.io/Easy-Turn/) | 🤖 [Easy Turn Model](https://huggingface.co/ASLP-lab/Easy-Turn) | 📑 [Paper](https://arxiv.org) | 🌐 [Huggingface](https://huggingface.co/collections/ASLP-lab/easy-turn-68d3ed0b294df61214428ea7) |
|:---:|:---:|:---:|:---:|

</div>

<p align="center">
<img src="src/logo.png" alt="Institution 5" style="width: 600px; border-radius: 30px;">
</p>

## Download
The Easy Turn resources are available at [Model](https://huggingface.co/ASLP-lab/Easy-Turn), [Trainset](https://huggingface.co/datasets/ASLP-lab/Easy-Turn-Trainset), and [Testset](https://huggingface.co/datasets/ASLP-lab/Easy-Turn-Testset).

## Easy Turn Trainset
The Easy Turn Trainset is a large-scale audio dataset for turn-taking detection, comprising both real and synthetic data. It contains four subsets corresponding to different conversational turn-taking states: 580 hours of complete state, 532 hours of incomplete state, 10 hours of backchannel state, and 23 hours of wait state, totaling approximately 1,100 hours. Each recording is accompanied by a text transcription and labeled with one of the four turn-taking states.
<div align="center"><img width="550px" src="src/data_pipeline.jpg" /></div>

## Citation
Please cite our paper if you find this work useful:

# Easy Turn：融合声学与语言模态实现鲁棒全双工口语对话系统轮次检测

<p align="center">
李国建¹, 王承友¹, 薛鸿飞¹,
王水元¹, 高德辉¹, 张子涵²,
林宇科², 李文杰², 肖龙帅²,
付中华¹^,╀, 谢磊¹^,╀
</p>

<p align="center">
¹ 西北工业大学音频、语音与语言处理课题组（ASLP@NPU）<br>
² 华为技术有限公司，中国 <br>
</p>

<div align="center">

| 🎤 [演示页面](https://aslp-lab.github.io/Easy-Turn/) | 🤖 [Easy Turn模型](https://huggingface.co/ASLP-lab/Easy-Turn) | 📑 [论文](https://arxiv.org) | 🌐 [Huggingface数据集集合](https://huggingface.co/collections/ASLP-lab/easy-turn-68d3ed0b294df61214428ea7) |
|:---:|:---:|:---:|:---:|

</div>

<p align="center">
<img src="src/logo.png" alt="机构标识" style="width: 600px; border-radius: 30px;">
</p>

## 下载
Easy Turn相关资源可通过[模型](https://huggingface.co/ASLP-lab/Easy-Turn)、[训练集](https://huggingface.co/datasets/ASLP-lab/Easy-Turn-Trainset)与[测试集](https://huggingface.co/datasets/ASLP-lab/Easy-Turn-Testset)获取。

## Easy Turn训练集
Easy Turn训练集是一款面向轮次检测的大规模音频数据集，涵盖真实与合成两类数据。其包含对应四种不同对话轮次状态的子集：完整状态数据580小时、不完整状态数据532小时、反馈语状态数据10小时，以及等待状态数据23小时，总时长约1100小时。每条录音均附带文本转录结果，并标注有四种轮次状态之一。
<div align="center"><img width="550px" src="src/data_pipeline.jpg" /></div>

## 引用
若您认为本研究对您的工作有所助益，请引用我们的论文：