Datasets:

Hui519
/

AudioEval

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:

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

@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}
}