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
www.youtube.com/channel/UCCkFzSvCae8ySmKCCWM5Mpg
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
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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)
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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
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Put your midi data in the folder 'data/original/'
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Group them into folders and name than for example 'style1', 'style2'
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Make sure you have at least 10 midi files per style, otherwise it can't form a test set
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Insert your style names into classes variable in settings.py
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Adjust parameters for training in settings.py
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Make sure you have all these files in the same folder
- 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/
- 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