  GuitarPro format tablatures are a type of digital music notation that
encapsulates information about guitar playing techniques and fingerings. We
introduce ShredGP, a GuitarPro tablature generative Transformer-based model
conditioned to imitate the style of four distinct iconic electric guitarists.
In order to assess the idiosyncrasies of each guitar player, we adopt a
computational musicology methodology by analysing features computed from the
tokens yielded by the DadaGP encoding scheme. Statistical analyses of the
features evidence significant differences between the four guitarists. We
trained two variants of the ShredGP model, one using a multi-instrument corpus,
the other using solo guitar data. We present a BERT-based model for guitar
player classification and use it to evaluate the generated examples. Overall,
results from the classifier show that ShredGP is able to generate content
congruent with the style of the targeted guitar player. Finally, we reflect on
prospective applications for ShredGP for human-AI music interaction.
