---
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

[Code](https://github.com/QwenAudio/FunResearch/tree/main/FineAudio-Judge) |
[Model](https://huggingface.co/Zixchen/FineAudio-Judge)

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](rubric_qa/README.md) | 700 prompt-audio samples, 33,498 rubric questions, and reference yes/no labels obtained by majority vote over five model annotations |
| [Human Preference](human_preference/README.md) | 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](rubric_qa/README.md) and
[Human Preference README](human_preference/README.md)
for detailed evaluation protocols.

## Dataset structure

The benchmark is organized into two components, each with its own data files,
audio directory, and README.

```text
.
├── 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](rubric_qa/README.md) and
[Human Preference README](human_preference/README.md) for file descriptions
and evaluation details.

## Quick start

Install the download client:

```bash
pip install huggingface_hub
```

Download the full benchmark:

```python
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](https://creativecommons.org/licenses/by-nc/4.0/) 许可发布。

The data in the following folders is released under the
[CC BY-NC 4.0](https://creativecommons.org/licenses/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](https://huggingface.co/datasets/HuggingFaceFV/finevideo#terms-of-use-for-finevideo)。

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](https://huggingface.co/datasets/HuggingFaceFV/finevideo#terms-of-use-for-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.