Demo of generated music after 600 epochs
A layman's attempt at generating music with LSTMs :)
The model is able to train on a large corpus of MIDI data and produce some reasonable sounding MIDI. There is a focus on representing data in a way to allow the generation of chords, and learning of harmony.
- Throw in some midi files into
/data - Tune
main.pyto configure number of epochs - Run the training - note, this is configured to train on an Nvidia GPU - I used an RTX 2070S
- Midi output can be generated using
generate()
The internal representaiton of music is a sequence of chords:
- Chords have root notes (lowest note in the chord) and harmonics (other notes played together with the root note)
- Root notes are represented by its midi value (semitone count in the range 0-127). Specifically, root notes are represented by thes semitone offset from the prior root note. This allows a piece of music to be transposed up and down (change in key) and have the exact same internal representation. This is a nice property to have, as it allows the network to learn harmonic progressions via examples presented in different keys!
- Chords are represented by their shape - distance of each note from the root note, e.g. a major chord is [0, 4, 7]
- The preprocessing routine simplifies chords, constraining them to a maximum size - e.g.
max_chord=3ensures that a chord cannot have more than 3 notes. It also imposes a constraint on the maximum semitone distance of each harmonic -max_dist=11allows for [0, 5, 11] but not [0, 12]. These constraints allow any chord to be represented with a constant number of bits (which seems useful for training a neural network).
- Figure out how to train a model with multiple categorical outputs.
- Figure out how to represent / generate note lengths + delays
- Encode rhythmic structure into the model