  Lyrics alignment in long music recordings can be memory exhaustive when
performed in a single pass. In this study, we present a novel method that
performs audio-to-lyrics alignment with a low memory consumption footprint
regardless of the duration of the music recording. The proposed system first
spots the anchoring words within the audio signal. With respect to these
anchors, the recording is then segmented and a second-pass alignment is
performed to obtain the word timings. We show that our audio-to-lyrics
alignment system performs competitively with the state-of-the-art, while
requiring much less computational resources. In addition, we utilise our lyrics
alignment system to segment the music recordings into sentence-level chunks.
Notably on the segmented recordings, we report the lyrics transcription scores
on a number of benchmark test sets. Finally, our experiments highlight the
importance of the source separation step for good performance on the
transcription and alignment tasks. For reproducibility, we publicly share our
code with the research community.
