The WASABI dataset: cultural, lyrics and audio analysis metadata about 2 million popular commercially released songs

Michel Buffa, Elena Cabrio, Michael Fell, Fabien L. Gandon, Alain Giboin, Romain hennequin, Franck Michel, Johan Pauwels, Guillaume Pellerin, Maroua Tikat, Marco Winckler

Keywords: WASABI, music metadata, lyrics analysis, named entities, linked data

Abstract: Since 2017, the goal of the two-million song WASABI database has been to build a knowledge graph linking collected metadata (artists,discography, producers, dates, etc.) with metadata generated by the analysis of both the songs’ lyrics (topics, places, emotions, structure, etc.) and audio signal (chords, sound, etc.). It relies on natural language processing and machine learning methods for extraction, and semantic Web frameworks for representation and integration. It describes more than 2 millions commercial songs, 200K albums and 77K artists. It can be exploited by music search engines, music professionals (e.g. journalists,radio presenters, music teachers) or scientists willing to analyze popular music published since 1950. It is available under an open license, in multiple formats and with online and open source services including an interactive navigator, a REST API and a SPARQL endpoint.

First Author Is Student: No