  Rap generation, which aims to produce lyrics and corresponding singing beats,
needs to model both rhymes and rhythms. Previous works for rap generation
focused on rhyming lyrics but ignored rhythmic beats, which are important for
rap performance. In this paper, we develop DeepRapper, a Transformer-based rap
generation system that can model both rhymes and rhythms. Since there is no
available rap dataset with rhythmic beats, we develop a data mining pipeline to
collect a large-scale rap dataset, which includes a large number of rap songs
with aligned lyrics and rhythmic beats. Second, we design a Transformer-based
autoregressive language model which carefully models rhymes and rhythms.
Specifically, we generate lyrics in the reverse order with rhyme representation
and constraint for rhyme enhancement and insert a beat symbol into lyrics for
rhythm/beat modeling. To our knowledge, DeepRapper is the first system to
generate rap with both rhymes and rhythms. Both objective and subjective
evaluations demonstrate that DeepRapper generates creative and high-quality
raps with rhymes and rhythms. Code will be released on GitHub.
