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---
license: cc-by-nc-sa-4.0
---
# S2S-Arena Dataset
This repository hosts the **S2S-Arena** dataset. It covers four practical domains with 21 tasks, includes 154 instructions of varying difficulty levels, and features a mix of samples from TTS synthesis, human recordings, and existing audio datasets.
## Introduction
### GitHub Repository
For more information and access to the dataset, please visit the GitHub repository:
[S2S-Arena on GitHub](https://github.com/FreedomIntelligence/S2S-Arena)
### Related Publication
For detailed insights into the dataset’s construction, methodology, and applications, please refer to the accompanying academic publication: `[coming soon]`
## Data Description
The dataset includes labeled audio files, textual emotion annotations, language translations, and task-specific metadata, supporting fine-grained analysis and application in machine learning. Each entry follows this format:
```json
{
"id": "emotion_audio_0",
"input_path": "./emotion/audio_0.wav",
"text": "[emotion: happy]Kids are talking by the door",
"task": "Emotion recognition and expression",
"task_description": "Can the model recognize emotions and provide appropriate responses based on different emotions?",
"text_cn": "孩子们在门旁说话",
"language": "English",
"category": "Social Companionship",
"level": "L3"
}
```
1. id: Unique identifier for each sample
2. input_path: Path to the audio file
3. text: English text with emotion annotation
4. task: Primary task associated with the data
5. task_description: Task description for model interpretability
6. text_cn: Chinese translation of the English text
7. language: Language of the input
8. category: Interaction context category
9. level: Difficulty or complexity level of the sample
"Some data also includes a `noise` attribute, indicating that noise has been added to the current sample and specifying the type of noise."
## BIb
```
@misc{jiang2025s2sarenaevaluatingspeech2speechprotocols,
title={S2S-Arena, Evaluating Speech2Speech Protocols on Instruction Following with Paralinguistic Information},
author={Feng Jiang and Zhiyu Lin and Fan Bu and Yuhao Du and Benyou Wang and Haizhou Li},
year={2025},
eprint={2503.05085},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.05085},
}
```
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