MusicTransformer
Music Transformer - project for CM0492
Sequence to sequence transformer using Global Relative Attention for improved complexity.
- Based on Attention is All You Need and Music Transformer.
- Pyorch Transformer implementation based on this tutorial and on this implementation.
- Preprocess implementation repository is here.
Dataset: PIANO-E-COMPETITION
The dataset is a set of piano recordings of a piano competition spanning various years. The music is piano music from the XVIII to XX century: Albeniz, Beethoven, Chopin, etc.
MIDI encoding depends on the type of music:
- live recordings vs. non-live midi composition
- instrumentation
- style
- ...
The MIDI encoding used for this project is the second presented in Music Transformer. Vocabulary is composed by 388+3 tokens:
- 128 NOTE-ON events
- 128 NOTE-OFF events
- 100 TIME-SHIFT events
- 32 VELOCITY bins
- SOS, EOS, PAD tokens
Hyperparameters
- B: Batch size
- L: Length of the sample, better if it does not exceed the length of the shortest midi file
- D: Dimension of the network Must be divisible by H
- d: Dimension of the feedforward layer
- H: Number of Heads on each attention layer Must divide D
- dist: tells the generator to use the whole distribution instead of the most token note to decide the next token in the sequence
Training
Training is done using sliding sequences, as in the tutorial.
- Given a sequence <A, B, C, D>, input sequence is <A, B, C>, target sequence is <B, C, D>
- Input sequences B batches of subsequences of length L randomly sampled from longer sequences of variable length len starting anywhere from index 0 to len-L.
The scheduler is Noam Optimizer, discussed in the paper Attention is All You Need. The Loss function is cross entropy with label smoothing.
Instructions
Download preprocessor (here, same link as before).
Put it in the same directory as the project's
Set the model in config as you prefer.
change DATASET_DIR to your MIDI dataset folder
Go to project directory and execute:
process_midi.py
train.py