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  # MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark
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- [![Paper](https://img.shields.io/badge/arxiv-%20PDF-red)](https://arxiv.org/pdf/2506.04779)
 
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  ![Pipeline](intro.png)
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  ## Overview of MMSU:
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  MMSU (Massive Multi-task Spoken Language Understanding and Reasoning Benchmark) is a comprehensive benchmark for evaluating fine-grained spoken language understanding and reasoning in multimodal models.
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- The benchmark covers 47 sub-tasks spanning perception and reasoning across phonetics, prosody, semantics, sociolinguistics, and rhetoric. It contains 5,000 carefully constructed audio-question-answer pairs, with speech collected from diverse authentic recordings.
 
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  ![Pipeline](benchmark.png)
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  ## Citation
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  ```
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  @article{wang2025mmsu,
 
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  # MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark
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+ [![Paper](https://img.shields.io/badge/arxiv-%20PDF-red)](https://arxiv.org/pdf/2506.04779) [![Project](https://img.shields.io/badge/Project-Page-green)](https://github.com/dingdongwang/MMSU_Bench)
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+
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  ![Pipeline](intro.png)
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  ## Overview of MMSU:
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  MMSU (Massive Multi-task Spoken Language Understanding and Reasoning Benchmark) is a comprehensive benchmark for evaluating fine-grained spoken language understanding and reasoning in multimodal models.
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+
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+ The benchmark covers **47 sub-tasks** spanning perception and reasoning across phonetics, prosody, semantics, sociolinguistics, and rhetoric. It contains **5,000 carefully constructed audio-question-answer pairs**, with speech collected from diverse authentic recordings.
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  ![Pipeline](benchmark.png)
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+ ## Usage
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+ You can load the dataset via Hugging Face datasets:
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+ ```
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+ from datasets import load_dataset
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+ ds = load_dataset("ddwang2000/MMSU")
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+ ```
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+ For evaluation, please refer to [**GitHub Code**](https://github.com/dingdongwang/MMSU_Bench)
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  ## Citation
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  ```
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  @article{wang2025mmsu,