README.md

---
pretty_name: AudioEval
license: cc-by-nc-4.0
size_categories:
- 1K<n<10K
source_datasets:
- original
annotations_creators:
- expert-generated
- crowdsourced
tags:
- audio
- text-to-audio
- benchmark
- evaluation
configs:
- config_name: default
  data_files:
  - split: full
    path: data/**
  drop_labels: true
---

# AudioEval

AudioEval is a text-to-audio evaluation benchmark with 4200 generated clips, 451 prompts, 24 systems, and 25200 per-rater annotations.
This release uses one main clip table in `data/metadata.jsonl`.

## Files

- `data/metadata.jsonl`: one clip-level table for all 4200 clips.
- `data/*.wav`: audio files referenced by `file_name`.
- `annotations/ratings.csv`: anonymized per-rater annotations.
- `annotations/prompts.tsv`: prompt metadata.
- `annotations/system_info.csv`: system name mapping.
- `stats/*.csv`: reliability and model summary tables.

## Summary

- 11.712 total hours of audio, about 10.039 seconds per clip on average.
- There are 9 non-expert raters and 3 expert raters.
- Rating rows by rater type: non_expert=12600, expert=12600.
- Each rating row contains 5 integer scores from 1 to 10.

## Main Columns

- `file_name`, `wav_name`, `prompt_id`, `prompt_text`
- `scene_category`, `sound_event_count`, `audioset_ontology`
- `system_id`, `system_name`
- `non_expert_*_mean`, `expert_*_mean`
- `non_expert_*_raw_scores`, `expert_*_raw_scores`

The five evaluation dimensions are `production_complexity`, `content_enjoyment`, `production_quality`, `textual_alignment`, and `content_usefulness`.

## Loading

Once you have access to the repository on the Hub, you can load the main table like this:

```python
from datasets import load_dataset

data = load_dataset("Hui519/AudioEval", split="full")
print(data[0]["audio"])
print(data[0]["prompt_text"])
```

- Rater demographic tables are intentionally excluded from this release.

## License

This dataset is released under Creative Commons Attribution-NonCommercial 4.0 International (`CC BY-NC 4.0`).

## Citation

```bibtex
@article{wang2025audioeval,
  title={Audioeval: Automatic dual-perspective and multi-dimensional evaluation of text-to-audio-generation},
  author={Wang, Hui and Zhao, Jinghua and Cheng, Junyang and Liu, Cheng and Jia, Yuhang and Sun, Haoqin and Zhou, Jiaming and Qin, Yong},
  journal={arXiv preprint arXiv:2510.14570},
  year={2025}
}
```