Musical genres are inherently ambiguous and difficult to define. Even more so is the task of establishing how genres relate to one another. Yet, genre is perhaps the most common and effective way of describing musical experience. The number of possible genre classifications (e.g. Spotify has over 4000 genre tags, LastFM over 500,000 tags) has made the idea of manually creating music taxonomies obsolete. We propose to use hyperbolic embeddings to learn a general music genre taxonomy by inferring continuous hierarchies directly from the co-occurrence of music genres from a large dataset. We evaluate our learned taxonomy against human expert taxonomies and folksonomies. Our results show that hyperbolic embeddings significantly outperform their Euclidean counterparts (Word2Vec), and also capture hierarchical structure better than various centrality measures in graphs.