MMAU
A massive multi-task audio understanding and reasoning benchmark comprising 10,000 carefully curated audio clips paired with human-annotated natural language questions spanning speech, environmental sounds, and music. Requires expert-level knowledge and complex reasoning across 27 distinct skills.
Nova 2 Omni from Amazon currently leads the MMAU leaderboard with a score of 0.753 across 2 evaluated AI models.
What MMAU measures
MMAU is a multimodal benchmark that evaluates large language models on multimodal, reasoning, and audio tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.7, with the leader reaching 0.8.
Compare leaders on the best AI for multimodal, best AI for reasoning and best AI for audio leaderboards.
Publication
- Paper
- MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark
- Authors
- S Sakshi, Utkarsh Tyagi, Sonal Kumar, Ashish Seth, and 5 others
- Published
- arXiv
- 2410.19168
Abstract
The ability to comprehend audio--which includes speech, non-speech sounds, and music--is crucial for AI agents to interact effectively with the world. We present MMAU, a novel benchmark designed to evaluate multimodal audio understanding models on tasks requiring expert-level knowledge and complex reasoning. MMAU comprises 10k carefully curated audio clips paired with human-annotated natural language questions and answers spanning speech, environmental sounds, and music. It includes information extraction and reasoning questions, requiring models to demonstrate 27 distinct skills across unique and challenging tasks. Unlike existing benchmarks, MMAU emphasizes advanced perception and reasoning with domain-specific knowledge, challenging models to tackle tasks akin to those faced by experts. We assess 18 open-source and proprietary (Large) Audio-Language Models, demonstrating the significant challenges posed by MMAU. Notably, even the most advanced Gemini Pro v1.5 achieves only 52.97% accuracy, and the state-of-the-art open-source Qwen2-Audio achieves only 52.50%, highlighting considerable room for improvement. We believe MMAU will drive the audio and multimodal research community to develop more advanced audio understanding models capable of solving complex audio tasks.
Nova 2 Omni leads with 75.3%, followed by 
Qwen2.5-Omni-7B at 65.6%.
Progress Over Time
Interactive timeline showing model performance evolution on MMAU
MMAU Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Amazon | — | — | — | ||
| 2 |  Alibaba Cloud / Qwen Team | 7B | — | — |
FAQ
Common questions about MMAU.
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