  Automatic drum transcription is a critical tool in Music Information
Retrieval for extracting and analyzing the rhythm of a music track, but it is
limited by the size of the datasets available for training. A popular method
used to increase the amount of data is by generating them synthetically from
music scores rendered with virtual instruments. This method can produce a
virtually infinite quantity of tracks, but empirical evidence shows that models
trained on previously created synthetic datasets do not transfer well to real
tracks. In this work, besides increasing the amount of data, we identify and
evaluate three more strategies that practitioners can use to improve the
realism of the generated data and, thus, narrow the synthetic-to-real transfer
gap. To explore their efficacy, we used them to build a new synthetic dataset
and then we measured how the performance of a model scales and, specifically,
at what value it will stagnate when increasing the number of training tracks
for different datasets. By doing this, we were able to prove that the
aforementioned strategies contribute to make our dataset the one with the most
realistic data distribution and the lowest synthetic-to-real transfer gap among
the synthetic datasets we evaluated. We conclude by highlighting the limits of
training with infinite data in drum transcription and we show how they can be
overcome.
