  The intersection between poetry and music provides an interesting case for
computational creativity, yet remains relatively unexplored. This paper
explores the integration of poetry and music through the lens of beat patterns,
investigating whether a byte-based language model can generate words that fit
specific beat patterns within the context of poetry. Drawing on earlier
studies, we developed a method to train a byte-based transformer model, ByT5,
to align poems with beat patterns. The results demonstrate a high level of beat
alignment while maintaining semantic coherence. Future work will aim to improve
the model's ability to create complete beat-aligned poems.
