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The WASABI song corpus and knowledge graph for music lyrics analysis


We present the WASABI Song Corpus, a large corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis. More specifically, given that lyrics encode an important part of the semantics of a song, we focus here on the description of the methods we proposed to extract relevant information from the lyrics, such as their structure segmentation, their topics, the explicitness of the lyrics content, the salient passages of a song and the emotions conveyed. The corpus contains 1.73M songs with lyrics (1.41M unique lyrics) annotated at different levels with the output of the above mentioned methods. The corpus labels and the provided methods can be exploited by music search engines and music professionals (e.g. journalists, radio presenters) to better handle large collections of lyrics, allowing an intelligent browsing, categorization and recommendation of songs. We demonstrate the utility and versatility of the WASABI Song Corpus in three concrete application scenarios. Together with the work on the corpus, we present the work achieved to transition the dataset into a knowledge graph, the WASABI RDF Knowledge Graph, and we show how this will enable an even richer set of applications.

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  4. from


  6. Based on language detection performed on the lyrics.

  7. We take the genre of the album as ground truth since song-wise genres are much rarer.

  8. We take the album publication date as proxy since song-wise labels are too sparse.






  14., here is a sample code for lyrics retrieval using this API at


  16. Obtained f-scores ranged between 70.8% for text-based and 75.3% for text-audio-based models.

  17. In our segmentation experiments we found this simple metric to outperform more complex metrics that take into account the phonetics or the syntax.


  19. Labels provided by Deezer. Furthermore, 625k songs have a different status such as unknown or censored version.

  20. Sometimes, a third dimension of dominance is part of the model.


  22. Depiction without scatter plot taken from Parisi et al. (2019)

  23. made with

  24. The software can be downloaded at We used the large model en_core_web_trf which is based on a transformers architecture.

  25. Note that in this Fig. we only show the artists with the most connections. Most connections from Bob Dylan are not visible as they are connected to not visualized nodes.



  28. SCOT (Social Semantic Cloud of Tags) Ontology:

  29. Audio Features Ontology:

  30. OMRAS2 Chord Ontology:



  33. Version 1:, version 2:






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This work is partly funded by the French Research National Agency (ANR) under the WASABI project (contract ANR-16-CE23-0017-01).

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Correspondence to Michael Fell.

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Fell, M., Cabrio, E., Tikat, M. et al. The WASABI song corpus and knowledge graph for music lyrics analysis. Lang Resources & Evaluation (2022).

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  • Corpus (creation, annotation, etc.)
  • Information extraction
  • Information retrieval
  • Knowledge graph
  • Music and song lyrics