# MMAU ## Overview MMAU (Massive Multitask Audio Understanding) is a comprehensive benchmark for evaluating audio understanding capabilities of multimodal large language models across diverse audio tasks. ## Task Description - **Task Type**: Audio Understanding (Multiple Choice) - **Input**: Audio recordings with multiple-choice questions - **Output**: Correct answer choice (A/B/C/D) - **Categories**: Speech, Sound, Music ## Key Features - Large-scale audio understanding benchmark - Covers multiple audio domains (speech, environmental sounds, music) - Multiple-choice format with 4 options - Includes both mini and full test sets - Per-category accuracy reporting ## Evaluation Notes - Default configuration uses **test_mini** split - Primary metric: **Accuracy** (exact match on predicted letter) - Reports overall accuracy and per-task-category accuracy - Prompt includes chain-of-thought instruction ## Properties | Property | Value | |----------|-------| | **Benchmark Name** | `mmau` | | **Dataset ID** | [lmms-lab/mmau](https://modelscope.cn/datasets/lmms-lab/mmau/summary) | | **Paper** | N/A | | **Tags** | `Audio`, `MCQ` | | **Metrics** | `acc` | | **Default Shots** | 0-shot | | **Evaluation Split** | `test_mini` | ## Data Statistics *Statistics not available.* ## Sample Example *Sample example not available.* ## Prompt Template **Prompt Template:** ```text Answer the following multiple choice question based on the audio content. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering. {question} {choices} ``` ## Usage ### Using CLI ```bash evalscope eval \ --model YOUR_MODEL \ --api-url OPENAI_API_COMPAT_URL \ --api-key EMPTY_TOKEN \ --datasets mmau \ --limit 10 # Remove this line for formal evaluation ``` ### Using Python ```python from evalscope import run_task from evalscope.config import TaskConfig task_cfg = TaskConfig( model='YOUR_MODEL', api_url='OPENAI_API_COMPAT_URL', api_key='EMPTY_TOKEN', datasets=['mmau'], limit=10, # Remove this line for formal evaluation ) run_task(task_cfg=task_cfg) ```