https://github.com/Ghadjeres/DeepBach
Tip revision: 6d75cb940f3aa53e02f9eade34d58e472e0c95d7 authored by Gaetan Hadjeres on 17 August 2022, 09:49:52 UTC
Merge pull request #85 from andreasjansson/upgrade-cog
Merge pull request #85 from andreasjansson/upgrade-cog
Tip revision: 6d75cb9
deepBach.py
"""
@author: Gaetan Hadjeres
"""
import click
from DatasetManager.chorale_dataset import ChoraleDataset
from DatasetManager.dataset_manager import DatasetManager
from DatasetManager.metadata import FermataMetadata, TickMetadata, KeyMetadata
from DeepBach.model_manager import DeepBach
@click.command()
@click.option('--note_embedding_dim', default=20,
help='size of the note embeddings')
@click.option('--meta_embedding_dim', default=20,
help='size of the metadata embeddings')
@click.option('--num_layers', default=2,
help='number of layers of the LSTMs')
@click.option('--lstm_hidden_size', default=256,
help='hidden size of the LSTMs')
@click.option('--dropout_lstm', default=0.5,
help='amount of dropout between LSTM layers')
@click.option('--linear_hidden_size', default=256,
help='hidden size of the Linear layers')
@click.option('--batch_size', default=256,
help='training batch size')
@click.option('--num_epochs', default=5,
help='number of training epochs')
@click.option('--train', is_flag=True,
help='train the specified model for num_epochs')
@click.option('--num_iterations', default=500,
help='number of parallel pseudo-Gibbs sampling iterations')
@click.option('--sequence_length_ticks', default=64,
help='length of the generated chorale (in ticks)')
def main(note_embedding_dim,
meta_embedding_dim,
num_layers,
lstm_hidden_size,
dropout_lstm,
linear_hidden_size,
batch_size,
num_epochs,
train,
num_iterations,
sequence_length_ticks,
):
dataset_manager = DatasetManager()
metadatas = [
FermataMetadata(),
TickMetadata(subdivision=4),
KeyMetadata()
]
chorale_dataset_kwargs = {
'voice_ids': [0, 1, 2, 3],
'metadatas': metadatas,
'sequences_size': 8,
'subdivision': 4
}
bach_chorales_dataset: ChoraleDataset = dataset_manager.get_dataset(
name='bach_chorales',
**chorale_dataset_kwargs
)
dataset = bach_chorales_dataset
deepbach = DeepBach(
dataset=dataset,
note_embedding_dim=note_embedding_dim,
meta_embedding_dim=meta_embedding_dim,
num_layers=num_layers,
lstm_hidden_size=lstm_hidden_size,
dropout_lstm=dropout_lstm,
linear_hidden_size=linear_hidden_size
)
if train:
deepbach.train(batch_size=batch_size,
num_epochs=num_epochs)
else:
deepbach.load()
deepbach.cuda()
print('Generation')
score, tensor_chorale, tensor_metadata = deepbach.generation(
num_iterations=num_iterations,
sequence_length_ticks=sequence_length_ticks,
)
score.show('txt')
score.show()
if __name__ == '__main__':
main()