Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription

Music transcription plays a pivotal role in Music Infor-
mation Retrieval (MIR), particularly for stringed instru-
ments like the guitar, where symbolic music notations such
as MIDI lack crucial playability information. This con-
tribution introduces the Fretting-Transformer, an encoder-
decoder model that utilizes a T5 transformer architecture to
automate the transcription of MIDI sequences into guitar
tablature. By framing the task as a symbolic translation
problem, the model addresses key challenges, including
string-fret ambiguity and physical playability. The pro-
posed system leverages diverse datasets, including DadaGP,
GuitarToday, and Leduc, with novel data pre-processing
and tokenization strategies. We have developed metrics for
tablature accuracy and playability to quantitatively evalu-
ate the performance. The experimental results demonstrate
that the Fretting-Transformer surpasses baseline methods
like A∗ and commercial applications like Guitar Pro. The
integration of context-sensitive processing and tuning/capo
conditioning further enhances the model’s performance,
laying a robust foundation for future developments in auto-
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mated guitar transcription.