This is a write-up of a presentation on generating music in the waveform domain, which was part of a tutorial that I co-presented at ISMIR 2019 earlier this month.
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Many new Transformer architecture improvements have been proposed since my last post on “The Transformer Family” about three years ago. Here I did a big refactoring and enrichment of that 2020 post — restructure the hierarchy of sections and improve many sections with more recent papers. Version 2.0 is a superset of the old version, about twice the length.
Notations Symbol Meaning $d$ The model size / hidden state dimension / positional encoding size.
Large transformer models are mainstream nowadays, creating SoTA results for a variety of tasks. They are powerful but very expensive to train and use. The extremely high inference cost, in both time and memory, is a big bottleneck for adopting a powerful transformer for solving real-world tasks at scale.
Why is it hard to run inference for large transformer models? Besides the increasing size of SoTA models, there are two main factors contributing to the inference challenge (Pope et al.
The end of classical Computer Science is coming, and most of us are dinosaurs waiting for the meteor to hit. I came of age in the 1980s, programming personal computers like the Commodore VIC-20 and…