JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata

Abhinaba Roy, Renhang Liu, Tongyu Lu, Dorien Herremans
2/11/2025
cs.SDcs.AI

No official code found. Best implementation: AMAAI-Lab/JamendoMaxCaps (53 stars, MIT, last commit 2025-05-24).

Abstract

We introduce JamendoMaxCaps, a large-scale music-caption dataset featuring over 362,000 freely licensed instrumental tracks from the renowned Jamendo platform. The dataset includes captions generated by a state-of-the-art captioning model, enhanced with imputed metadata. We also introduce a retrieval system that leverages both musical features and metadata to identify similar songs, which are then used to fill in missing metadata using a local large language model (LLLM). This approach allows us to provide a more comprehensive and informative dataset for researchers working on music-language understanding tasks. We validate this approach quantitatively with five different measurements. By making the JamendoMaxCaps dataset publicly available, we provide a high-quality resource to advance research in music-language understanding tasks such as music retrieval, multimodal representation learning, and generative music models.

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Code Implementations(1)

AMAAI-Lab/JamendoMaxCapsCommunity100%
MIT

JamendoMaxCaps is a large-scale dataset of 362,000 instrumental creative commons tracks

530PythonFeb 11, 20251 years agoMIT
captionsdatasetmusic

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Cite this paper

@article{roy2025jamendomaxcaps,
  title  = {JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata},
  author = {Abhinaba Roy and Renhang Liu and Tongyu Lu and Dorien Herremans},
  year   = {2025},
  eprint = {2502.07461},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.07461v2}
}

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