  Time-aligned lyrics can enrich the music listening experience by enabling
karaoke, text-based song retrieval and intra-song navigation, and other
applications. Compared to text-to-speech alignment, lyrics alignment remains
highly challenging, despite many attempts to combine numerous sub-modules
including vocal separation and detection in an effort to break down the
problem. Furthermore, training required fine-grained annotations to be
available in some form. Here, we present a novel system based on a modified
Wave-U-Net architecture, which predicts character probabilities directly from
raw audio using learnt multi-scale representations of the various signal
components. There are no sub-modules whose interdependencies need to be
optimized. Our training procedure is designed to work with weak, line-level
annotations available in the real world. With a mean alignment error of 0.35s
on a standard dataset our system outperforms the state-of-the-art by an order
of magnitude.
