RNNMusicSynthesis

Author

Sravan Jayanthi

Music Synthesis Machine Learning Model

The goal is to create a machine learning model that can generate near-authentic classical music. This model is designed utilizing Recurrent Neural Networks as the first iteration in a series of different models to be tested to accomplish the goal. This model preprocesses the wave based input in the form of midi files and encodes it based on the duration and chord of the notes being played. Then, the music derived data is organized into batches to be used to train the Recurrent Nerual Network model so that given a sequence of priming notes, it can generate a realistic sequence of chords that form into a melodic song. The newly created music is then decoded so that the encoding utilized in the model are translated back into their representative oscillating musical notes.

Description

This project contains a model training script and a music prediction script along with assosciated encoding, decoding, and vocabulary implementations.

File/Directory	Purpose
`model.py`	Train the RNN model
`predict.py`	Generate sequence of music
`vocab.py`	Parameters for encoding
`encode.py`	Codify the musical notes
`decode.py`	Translate back to notes
`music/`	Classical music input
`generated/`	Synthesized music output
`training_checkpoints/`	Trained model weights
`stats/`	Sample translations

Usage

In order to utilize the machine learning model, a repository of music should be identified from which the model will gather its training data from.

Port the collection of music in the form of .mid or .midi files into the generated/ folder
Select a sample to be used as a primer for the model to generate music and place in the generated/sample folder
Tune the RNN model in model.py with the desired training parameters specifying the size and scope of the algorithm
Execute the script model.py with the requisite dependencies installed, this will generate the model weights which will be stored in the training_checkpoints/ folder
Tune the prediction iteration of the trained model in predict.py with the desired parameters
Execute the script predict.py which will sample the primer and synthesize a new song which will be written in the generated/ folder
Play your wonderful artistic piece and enjoy!

Dependencies

Tensorflow
Music21

Code

Sample code of predicting notes based off of previously played music.

    predictions = tf.squeeze(predictions, 0)
    predictions = predictions / TEMPERATURE
    predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()
    input_eval = tf.expand_dims([predicted_id], 0)
    music_generated.append(predicted_id)

License

MIT

SVJayanthi / RNNMusicSynthesis