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license: cc-by-nc-4.0
---
# MMAU-Pro: A Challenging and Comprehensive Benchmark for Audio General Intelligence
[](https://www.arxiv.org/pdf/2508.13992) [](https://huggingface.co/datasets/gamma-lab-umd/MMAU-Pro/blob/main/data.zip)
[MMAU-Pro](https://arxiv.org/abs/2508.13992) is the most comprehensive benchmark to date for evaluating **audio intelligence in multimodal models**. It spans speech, environmental sounds, music, and their combinations—covering **49 distinct perceptual and reasoning skills**.
The dataset contains **5,305 expert-annotated question–answer pairs**, with audios sourced directly *from the wild*. It introduces several novel challenges overlooked by prior benchmarks, including:
- Long-form audio understanding (up to 10 minutes)
- Multi-audio reasoning
- Spatial audio perception
- Multicultural music reasoning
- Voice-based STEM and world-knowledge QA
- Instruction-following with verifiable constraints
- Open-ended QA in addition to MCQs
---
🚀 Usage
You can load the dataset via Hugging Face datasets:
```
from datasets import load_dataset
ds = load_dataset("gamma-lab-umd/MMAU-Pro")
```
For evaluation, we provide:
- MCQ scoring via embedding similarity (NV-Embed-v2)
- Open-ended QA with LLM-as-a-judge
- Regex based string matching for Instruction Following
---
🧪 Baselines & Model Performance
We benchmarked 22 leading models on MMAU-Pro.
- Gemini 2.5 Flash (closed-source): 59.2% avg. accuracy
- Audio Flamingo 3 (open-source): 51.7%
- Qwen2.5-Omni-7B: 52.2%
- Humans: ~78%
See full results in the paper.
---
🌍 Multicultural Music Coverage
MMAU-Pro includes music from 8 diverse regions:
• Western, Chinese, Indian, European, African, Latin American, Middle Eastern, Other Asian
This reveals clear biases: models perform well on Western/Chinese but poorly on Indian/Latin American music.
---
📥 Download
- Dataset: [HF](https://huggingface.co/datasets/gamma-lab-umd/MMAU-Pro)
- Paper: [MMAU-Pro](https://arxiv.org/abs/2508.13992)
- Website: [Official Page](https://sonalkum.github.io/mmau-pro/)
- Github: [Git](https://github.com/sonalkum/MMAUPro)
---
🧩 Evaluation
The evaluation code is designed to take in the complete `test.parquet` with predictions in the column `model_ouput`.
```
python evaluate_mmau_pro_comprehensive.py test.parquet --model_output_column model_output
```
---
✍️ Citation
If you use MMAU-Pro, please cite:
```bibtex
@article{kumar2025mmau,
title={MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence},
author={Kumar, Sonal and Sedl{\'a}{\v{c}}ek, {\v{S}}imon and Lokegaonkar, Vaibhavi and L{\'o}pez, Fernando and Yu, Wenyi and Anand, Nishit and Ryu, Hyeonggon and Chen, Lichang and Pli{\v{c}}ka, Maxim and Hlav{\'a}{\v{c}}ek, Miroslav and others},
journal={arXiv preprint arXiv:2508.13992},
year={2025}
}
```
---
🙏 Acknowledgments
Some work was carried out at JSALT 2025. |