  Deep learning has recently empowered and democratized generative modeling of
images and text, with additional concurrent works exploring the possibility of
generating more complex forms of data, such as audio. However, the high
dimensionality, long-range dependencies, and lack of standardized datasets
currently makes generative modeling of audio and music very challenging. We
propose to model music as a series of discrete notes upon which we can use
autoregressive natural language processing techniques for successful generative
modeling. While previous works used similar pipelines on data such as sheet
music and MIDI, we aim to extend such approaches to the under-studied medium of
guitar tablature. Specifically, we develop the first work to our knowledge that
models one specific genre as guitar tablature: heavy rock. Unlike other works
in guitar tablature generation, we have a freely available public demo at
https://huggingface.co/spaces/josuelmet/Metal_Music_Interpolator
