multimodal benchmark
MusicCaps is a dataset composed of 5,521 music examples, each labeled with an English aspect list and a free text caption written by musicians. The dataset contains 10-second music clips from AudioSet paired with rich textual descriptions that capture sonic qualities and musical elements like genre, mood, tempo, instrumentation, and rhythm. Created to support research in music-text understanding and generation tasks.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | AC | 32.8% | 100.0% | 1 | C |
The leading models and scores on this benchmark.
What MusicCaps measures and how its scores work.
MusicCaps is a dataset composed of 5,521 music examples, each labeled with an English aspect list and a free text caption written by musicians. The dataset contains 10-second music clips from AudioSet paired with rich textual descriptions that capture sonic qualities and musical elements like genre, mood, tempo, instrumentation, and rhythm. Created to support research in music-text understanding and generation tasks.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MusicCaps.
Qwen2.5-Omni-7B is currently ranked first with 32.8%.
MusicCaps is a dataset composed of 5,521 music examples, each labeled with an English aspect list and a free text caption written by musicians. The dataset contains 10-second music clips from AudioSet paired with rich textual descriptions that capture sonic qualities and musical elements like genre, mood, tempo, instrumentation, and rhythm. Created to support research in music-text understanding and generation tasks.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.