  Multilingual automatic lyrics transcription (ALT) is a challenging task due
to the limited availability of labelled data and the challenges introduced by
singing, compared to multilingual automatic speech recognition. Although some
multilingual singing datasets have been released recently, English continues to
dominate these collections. Multilingual ALT remains underexplored due to the
scale of data and annotation quality. In this paper, we aim to create a
multilingual ALT system with available datasets. Inspired by architectures that
have been proven effective for English ALT, we adapt these techniques to the
multilingual scenario by expanding the target vocabulary set. We then evaluate
the performance of the multilingual model in comparison to its monolingual
counterparts. Additionally, we explore various conditioning methods to
incorporate language information into the model. We apply analysis by language
and combine it with the language classification performance. Our findings
reveal that the multilingual model performs consistently better than the
monolingual models trained on the language subsets. Furthermore, we demonstrate
that incorporating language information significantly enhances performance.
