The goal of the project is to model music compositions by capturing temporal dependencies in classical music composi- tions and eventually generate novel music compositions from the learned model. We use approximately 24000 music com- positions transcribed in ABC notation. We implement count- based n-gram language model, use its results as a baseline for recurrent neural network based methods and assess their abil- ity to generate structurally coherent, human-pleasing music. We achieved test accuracy of 69.78% for char-RNN and 73% for seq2seq model with both methods generating valid music compositions.
Detailed experiments, methodlogies and results are discussed in
In order to install
fiddler run the following command:
python setup.py install
This will install
fiddler as a command line tool.
fiddler train_rnn [options]
train_rnn command supports following options:
Usage: fiddler train_rnn [OPTIONS] Train neural network Options: -f, --file PATH Train Data File Path -b, --batch-size INTEGER Batch size -l, --layers INTEGER Number of layers in the network -r, --learning-rate FLOAT Learning Rate -n, --num-steps INTEGER No. of time steps in RNN -s, --cell-size INTEGER Dimension of cell states -d, --dropout FLOAT Dropout probability for the output -e, --epochs INTEGER No. of epochs to run training for -c, --cell [lstm|gru] Type of cell used in RNN -t, --test-seed TEXT Seed input for printing predicted text after each training step --delim / --no-delim Delimit tunes with start and end symbol --save / --no-save Save model to file --help Show this message and exit.
fiddler is not installed as command-line tool, you can use the same command using
python src/cli.py with same arguments.
Usage: fiddler generate [OPTIONS] Options: -m, --model_path PATH Directory path for saved model --help Show this message and exit.
Manthan Thakar - Character-level Recurrent Neural Network and Sequence to Sequence Implementation
Rashmi Dwarka - Character-level Recurrent Neural Network and Sequence to Sequence Implementation
Tirthraj Parmar - Character-level n-gram language model implementation