Datasets:
pretty_name: FineAudio-Judge Benchmark
language:
- en
- zh
task_categories:
- audio-classification
- audio-text-to-text
tags:
- audio
- text
- audio-language-model
- audio-text-alignment
- instruction-following
- rubric-based-evaluation
- human-preference
- benchmark
configs:
- config_name: rubric_qa
default: true
data_files:
- split: test
path: rubric_qa/rubric_qa.jsonl
- config_name: rubric_qa_grouped
data_files:
- split: test
path: rubric_qa/rubric_qa_grouped.jsonl
- config_name: preference_candidates
data_files:
- split: test
path: human_preference/candidate_groups.jsonl
- config_name: preference_rubrics
data_files:
- split: test
path: human_preference/prompt_rubrics.jsonl
- config_name: preference_pairwise
data_files:
- split: test
path: human_preference/human_preference_pairwise.jsonl
- config_name: preference_ranking
data_files:
- split: test
path: human_preference/human_preference_ranking.jsonl
- config_name: preference_annotations
data_files:
- split: test
path: human_preference/human_preference_annotations.jsonl
FineAudio-Judge Benchmark
FineAudio-Judge is a fine-grained evaluation framework for assessing whether audio matches text descriptions or generation instructions for complex audio scenes. It structurally decomposes the text into events, attributes, and inter-event relations, then converts them into atomic binary rubric questions for audio-based judgments.
This repository provides the FineAudio-Judge evaluation benchmark. It covers real, composed, and generated audio and supports evaluation of rubric-level judgments and agreement with human preferences.
The benchmark contains two evaluation components:
| Component | Main content |
|---|---|
| Rubric-QA | 700 prompt-audio samples, 33,498 rubric questions, and reference yes/no labels obtained by majority vote over five model annotations |
| Human Preference | 100 prompts, each paired with three generated audio candidates (300 in total), together with prompt-derived rubrics and preference annotations from seven human annotators |
All published audio is WAV. JSONL files use repository-relative audio paths. See the README in each component directory for field definitions, label provenance, subset names, consensus rules, and usage notes.
The benchmark is organized by evaluation component and subset.
English and Chinese records are stored together, with the language
of each record indicated by the language field.
Evaluation overview
Rubric-QA
This component evaluates yes/no judgments at the individual rubric level. Each audio–rubric question pair is evaluated independently. The reference label is determined by majority vote across five model annotations. Predictions are compared with these reference labels to compute rubric-level accuracy and yes/no recall.
Human Preference
Each candidate audio is scored by the proportion of rubric questions answered yes. Scores are used to compare and rank candidates for the same prompt, with accuracy measured against human pairwise preferences and three-candidate rankings.
See the Rubric-QA README and Human Preference README for detailed evaluation protocols.
Dataset structure
The benchmark is organized into two components, each with its own data files, audio directory, and README.
.
├── README.md
├── rubric_qa/
│ ├── README.md
│ ├── rubric_qa.jsonl
│ ├── rubric_qa_grouped.jsonl
│ └── audio/
└── human_preference/
├── README.md
├── candidate_groups.jsonl
├── prompt_rubrics.jsonl
├── human_preference_pairwise.jsonl
├── human_preference_ranking.jsonl
├── human_preference_annotations.jsonl
└── audio/
See the Rubric-QA README and Human Preference README for file descriptions and evaluation details.
Quick start
Install the download client:
pip install huggingface_hub
Download the full benchmark:
from huggingface_hub import snapshot_download
dataset_root = snapshot_download(
repo_id="Zixchen/FineAudio-Judge-Benchmark",
repo_type="dataset",
)
print(dataset_root)
The downloaded repository contains both the JSONL records and the referenced
WAV files. Audio paths in the JSONL records are relative to dataset_root.
License / 数据许可声明
本数据集以下文件夹内的数据采用 CC BY-NC 4.0 许可发布。
The data in the following folders is released under the CC BY-NC 4.0 license.
rubric_qa/audio/a2/rubric_qa/audio/a3/rubric_qa/audio/cross_a1/rubric_qa/audio/cross_a2/rubric_qa/audio/qwen_audio_ood/rubric_qa/audio/seedaudio_ood/human_preference/audio/
本数据集 rubric_qa/audio/a1/ 文件夹内的数据筛选自开源的
FineVideo 数据集，使用时请参阅并遵守 FineVideo 的
Terms of Use。
The data in the rubric_qa/audio/a1/ folder is derived from a filtered
subset of the FineVideo dataset; please refer to and comply with the
Terms of Use of FineVideo
when using it.
本项目新增的评测问题及标注采用 CC BY-NC 4.0 许可发布。 该许可仅适用于本项目拥有或有权授予许可的部分， 不替代第三方素材及相关模型或服务的适用许可和使用条款。
Project-created evaluation questions and annotations are released under CC BY-NC 4.0. This license applies only to rights held or licensable by this project and does not replace the applicable licenses and terms of third-party materials or the relevant models and services.
Disclaimer / 免责声明
部分数据来源于第三方开源数据集，不代表本项目的立场或观点。 您对相关内容的复制、使用与分发须严格遵循开源数据集的许可条件， 不得超出许可范围，并遵守适用的法律法规，自行承担相应责任。
Some data is sourced from third-party open-source datasets and does not represent the views or opinions of this project. Your copying, use, and distribution of such content must strictly comply with the license terms of the source datasets and must not exceed the scope of those licenses. You are also responsible for complying with all applicable laws and regulations, and you assume the associated risks and liabilities.