  Automatic music transcription (AMT) is one of the most challenging tasks in
the music information retrieval domain. It is the process of converting an
audio recording of music into a symbolic representation containing information
about the notes, chords, and rhythm. Current research in this domain focuses on
developing new models based on transformer architecture or using methods to
perform semi-supervised training, which gives outstanding results, but the
computational cost of training such models is enormous.
  This work shows how to employ easily generated synthesized audio data
produced by software synthesizers to train a universal model. It is a good base
for further transfer learning to quickly adapt transcription model for other
instruments. Achieved results prove that using synthesized data for training
may be a good base for pretraining general-purpose models, where the task of
transcription is not focused on one instrument.
