  Guitar tablature is a form of music notation widely used among guitarists. It
captures not only the musical content of a piece, but also its implementation
and ornamentation on the instrument. Guitar Tablature Transcription (GTT) is an
important task with broad applications in music education, composition, and
entertainment. Existing GTT datasets are quite limited in size and scope,
rendering models trained on them prone to overfitting and incapable of
generalizing to out-of-domain data. In order to address this issue, we present
a methodology for synthesizing large-scale GTT audio using commercial acoustic
and electric guitar plugins. We procure SynthTab, a dataset derived from
DadaGP, which is a vast and diverse collection of richly annotated symbolic
tablature. The proposed synthesis pipeline produces audio which faithfully
adheres to the original fingerings and a subset of techniques specified in the
tablature, and covers multiple guitars and styles for each track. Experiments
show that pre-training a baseline GTT model on SynthTab can improve
transcription performance when fine-tuning and testing on an individual
dataset. More importantly, cross-dataset experiments show that pre-training
significantly mitigates issues with overfitting.
