README.md

---
license: cc-by-nc-4.0
---
# MMAU-Pro: A Challenging and Comprehensive Benchmark for Audio General Intelligence

[![Paper](https://img.shields.io/badge/arxiv-%20PDF-red)](https://www.arxiv.org/pdf/2508.13992) [![Audios](https://img.shields.io/badge/🔈%20-Audios-blue)](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  

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🚀 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

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🧪 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.

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🌍 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.

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📥 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)

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🧩 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
```

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✍️ 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}
}
```

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🙏 Acknowledgments

Some work was carried out at JSALT 2025.