Abstract Optical Music Recognition aims to transcribe musical manuscript images into digital formats by using automatic methods for enhanced accessibility and preservation. This task is challenging for handwritten historical musical pieces from the Late Middle Ages, Early Renaissance, and previous time periods. This music has the interesting characteristic that both musical and lyrical elements are present with an implicit time alignment between them. This paper introduces techniques for simultaneously transcribing the musical and lyrical elements. We research how to automatically obtain the time alignment for an accurate musicological interpretation. Convolutional and Recurrent Neural Networks and Transformer models are explored for holistically transcribing and aligning historical pieces. This paper explores different techniques to improve the training of the models in limited data scenarios. Experiments are conducted on two different datasets from the same time period. Our findings highlight the potential of Transformer models in overcoming the alignment challenge, providing the best alignment capabilities without compromising the quality of transcriptions and offering a promising direction for future research in the automatic recognition of historical musical documents.