A simple application of a multi-layered LSTM Network to generate original Folk-style music which are aesthetically pleasing for the listener.
Here, we have trained a Many-to-Many LSTM Network with musical sequences and the corresponding following note sequences found in the musical notation of a piece composed by an artist. As a result, the Network somehow learns to extract a common 'musical quality' which is preserved in all of the input musical sequences. When we test the network with completely random sequence inputs, the network returns quite interesting and aeshetically pleasing original musical pieces which are completely machine-generated.
There are 86 unique chaaracters in the ABC notation of the musical sequences used as training data for the LSTM Network. A sequence of 64 such characters is used to contruct each of the input sequence used in the training process. To speed up the training process, we train the architecture with batches of 16 input sequences.
The first layer of the architecture is an Embedding layer which maps every input sequence into a 512-dimensional vector. This is followed by a collection of LSTM layers (256 cells each) and Dropout layers. Finally, a 'TimeDistributed' Dense Layer binds the outputs of the LSTM cells in the last layer into a sequence output with Softmax activation. The overall architecture can be clearly seen from the model summary :
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding_1 (Embedding) (16, 64, 512) 44032 _________________________________________________________________ lstm_1 (LSTM) (16, 64, 256) 787456 _________________________________________________________________ dropout_1 (Dropout) (16, 64, 256) 0 _________________________________________________________________ lstm_2 (LSTM) (16, 64, 256) 525312 _________________________________________________________________ dropout_2 (Dropout) (16, 64, 256) 0 _________________________________________________________________ lstm_3 (LSTM) (16, 64, 256) 525312 _________________________________________________________________ dropout_3 (Dropout) (16, 64, 256) 0 _________________________________________________________________ time_distributed_1 (TimeDist (16, 64, 86) 22102 _________________________________________________________________ activation_1 (Activation) (16, 64, 86) 0 ================================================================= Total params: 1,904,214 Trainable params: 1,904,214 Non-trainable params: 0 _________________________________________________________________
We have used the Nottingham Music Database in ABC notation which contains over 1000 folk tunes.
All input data should be placed in the data directory. The example input.txt is taken from the Nottingham Dataset (Cleaned).
To train the model with default settings:
$ python train.py
To sample the model:
$ python sample.py 100
Training loss/accuracy is stored in training_log.csv.
Also, to prevent the loss of intermediate weights during the training epochs of the model, the weights are saved in the model directory. The training process can take a significant amount of time.
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