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---
license: mit
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
- name: duration
dtype: float64
- name: language
dtype: string
splits:
- name: en
num_bytes: 87640124.0
num_examples: 438
- name: zh
num_bytes: 131476237.0
num_examples: 489
download_size: 218759346
dataset_size: 219116361.0
configs:
- config_name: default
data_files:
- split: en
path: data/en-*
- split: zh
path: data/zh-*
task_categories:
- automatic-speech-recognition
- audio-classification
language:
- en
- zh
tags:
- audio
- asr
size_categories:
- n<1K
---
# WESR-Bench
WESR-Bench is an expert-annotated natural speech dataset with word-level non-verbal vocal events, featuring both discrete events (standalone, denoted as \[tag\]) and continuous events (mixed with speech, denoted as \<tag\>...\</tag\>).
## Supported Tags
**Discrete events (15):**
- `inhale`, `cough`, `laughs`, `laughing`, `crowd_laughter`, `chuckle`, `shout`, `sobbing`, `cry`, `giggle`,`exhale`, `sigh`, `clear_throat`, `roar`, `scream`, `breathing`
**Continuous events (6):**
- `crying`, `laughing`, `panting`, `shouting`, `singing`, `whispering`
## Evaluation
See code and guidelines for evaluation in [Github](https://github.com/Cr-Fish/WESR).
## Citation
If you find WESR-Bench helpful in your research, please cite our paper:
```
@misc{yang2026wesrscalingevaluatingwordlevel,
title={WESR: Scaling and Evaluating Word-level Event-Speech Recognition},
author={Chenchen Yang and Kexin Huang and Liwei Fan and Qian Tu and Botian Jiang and Dong Zhang and Linqi Yin and Shimin Li and Zhaoye Fei and Qinyuan Cheng and Xipeng Qiu},
year={2026},
eprint={2601.04508},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2601.04508},
}
``` |