  In this work, we address the challenge of lyrics alignment, which involves
aligning the lyrics and vocal components of songs. This problem requires the
alignment of two distinct modalities, namely text and audio. To overcome this
challenge, we propose a model that is trained in a supervised manner, utilizing
the cross-correlation matrix of latent representations between vocals and
lyrics. Our system is designed in a hierarchical and cascaded manner. It
predicts synced time first on a sentence-level and subsequently on a
word-level. This design enables the system to process long sequences, as the
cross-correlation uses quadratic memory with respect to sequence length. In our
experiments, we demonstrate that our proposed system achieves a significant
improvement in mean average error, showcasing its robustness in comparison to
the previous state-of-the-art model. Additionally, we conduct a qualitative
analysis of the system after successfully deploying it in several music
streaming services.
