2021 · 2026
AutoTab v2
Guitar tablature from audio, via CNN
Keras 3 over CQT features, 6 strings × 21 classes. Modernised in 2026: Python 3.12, uv, CLI, Streamlit app, Docker, pytest, GitHub Actions. Calibrating the silence class raises the F-score roughly threefold — with no retraining.
- 6 Saiten × 21 Klassen
- CQT-Features
- ~3× F-Score
- Python 3.12
- Keras 3
- librosa
- uv
- Streamlit
- Docker
- pytest
- GitHub Actions
Problem
Guitarists learn pieces from tablature, but most recordings don't come with any. Picking the frets out of audio by ear is tedious – and an interesting problem for a neural network, because six strings ring at once.
Solution
A convolutional neural network in Keras, trained on the GuitarSet dataset from Queen Mary University. Constant-Q spectrograms as input, six outputs with 21 classes each – a fret or silence per string. Built in the 2021 bootcamp, fully modernised in 2026: Python 3.12, Keras 3, uv, a CLI, a Streamlit interface, Docker, 18 tests and CI.
Result
The biggest improvement came not from more training but from calibrating the silence class: roughly three times the F-score with the same model. With Demucs the tool now separates the guitar out of full songs, and adapters for six datasets make further training possible.