No description, website, or topics provided.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.

README.md

MIDI-VAE

Paper

MIDI-VAE: MODELING DYNAMICS AND INSTRUMENTATION OF MUSIC WITH APPLICATIONS TO STYLE TRANSFER

Paper accepted at 19th International Society for Music Information Retrieval Conference (ISMIR), Paris, France, September 2018

Music Samples

www.youtube.com/channel/UCCkFzSvCae8ySmKCCWM5Mpg

Dataset

All the music pieces we used for generating the audio samples on Youtube and the evaluation in the paper can be downloaded here: https://goo.gl/sNpgQ7

Preparation

  • Install common libraries like numpy matplotlib pickle numpy progressbar sklearn scipy csv keras tensorflow theano (some functions are only supported with theano because of recurrentshop)

  • Make sure you have installed the following packages https://github.com/craffel/pretty-midi https://github.com/farizrahman4u/recurrentshop/tree/master/recurrentshop https://github.com/nschloe/matplotlib2tikz

  • Put your midi data in the folder 'data/original/'

  • Group them into folders and name than for example 'style1', 'style2'

  • Make sure you have at least 10 midi files per style, otherwise it can't form a test set

  • Insert your style names into classes variable in settings.py

  • Adjust parameters for training in settings.py

  • Make sure you have all these files in the same folder

Training

  • Run either vae_training.py to use the full MIDI-VAE model or
  • Run any of the style classifiers pitch_classifier.py, velocity_classifer.py or instrument_classifer.py

The models will be stored in the automatically generated folder models/

Evaluation

  • Change the model_name and epoch of your MIDI-VAE model that you want to evaluate
  • Change the model names and epochs and weights for all the style classifiers
  • Make sure you have set the same parameters as were used during training
  • Run vae_evaluation.py