MMAR: A Challenging Benchmark for Deep Reasoning in Speech, Audio, Music, and Their Mix.

A benchmark designed to evaluate the deep reasoning capabilities of Audio-Language Models (ALMs) across massive multi-disciplinary tasks. Comprises 1,000 meticulously curated audio-question-answer triplets covering speech, audio, music, and their mix collected from real-world internet videos.

Best published scores.

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Neighbouring benchmarks.

Other Audio-Language Models datasets on Codesota.

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Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

• 01A public checkpoint or API endpoint
• 02A reproduction script with frozen commit + seed
• 03Declared evaluation environment (Python, deps)
• 04One row per metric declared by this dataset
• 05A contact so we can follow up on discrepancies