| --- |
| 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} |
| } |
| ``` |
|
|
