autochord 0.1.2

Automatic Chord Recognition library

autochord

Automatic Chord Recognition tools

About

autochord is:

✔ a [Python library] for automatic chord recognition (using TensorFlow)

✔ a Javascript app for visualization of chord transcriptions:

Library Usage

To install library, run:

$ pip install autochord

autochord provides a very simple API for performing chord recognition:

import autochord
autochord.recognize('audio.wav', lab_fn='chords.lab')
# This gives out a list of tuples in the format:
#  (chord start, chord end, chord name)
# e.g.
# [(0.0, 5.944308390022676, 'D:maj'),
#  (5.944308390022676, 7.476825396825397, 'C:maj'),
#  (7.476825396825397, 18.250884353741498, 'D:maj'),
#  (18.250884353741498, 19.736961451247165, 'C:maj')
#  ...
#  (160.49632653061224, 162.30748299319728, 'N')]

Under the hood autochord.recognize() runs the NNLS-Chroma VAMP plugin to extract chroma features from the audio, and feeds it to a Bi-LSTM-CRF model in TensorFlow to recognize the chords. Currently, the model can recognize 25 chord classes: the 12 major triads, 12 minor triads, and no-chord ('N').

OPTIONALLY, you may dump the chords in a .lab file by using the lab_fn parameter. The output file follows the MIREX chord label format.

Upon import autochord takes care of setting up the VAMP plugin and downloading the pre-trained chord recognition model.

The measured test accuracy of the TensorFlow model is 67.33%. That may be enough for some songs, but we can explore in the future how to further improve this.

App Usage

The app is pretty straightforward. First, you need to load the song you wish to visualize the chords of, then you can upload the LAB file for its chord labels. You may use the autochord Python library for this. Optionally, you may load another LAB file for comparison (e.g. ground-truth labels, LAB file from another model's prediction).

Future Improvements

• Integrate everything into a full chord recognition app! For that we need to:
  • convert VAMP plugin to JS module
  • model conversion to TensorFlow.js (as of writing, some CRF operations are not supported by TFJS yet)
  • converting all other Python functions to JS equivalent
• Experimenting with other approaches to improve chord recognition accuracy