praca-magisterska/project
2019-05-30 14:22:53 +02:00
..
__pycache__ -init-first-stage- 2019-05-28 12:40:26 +02:00
data add some examples 2019-05-30 14:22:53 +02:00
generate.py lstm autoencoder, for now with the best results 2019-05-30 12:36:59 +02:00
midi.py delete empty samples - optimized 2019-05-30 13:36:15 +02:00
readme lstm - drop this branch, looking for other way to generate music 2019-05-30 11:23:34 +02:00
settings.py lstm - drop this branch, looking for other way to generate music 2019-05-30 11:23:34 +02:00
settings.pyc -init-first-stage- 2019-05-28 12:40:26 +02:00
train.py lstm autoencoder, for now with the best results 2019-05-30 12:36:59 +02:00

## MUSIC GENERATION USING DEEP LEARNING ##
## AUTHOR: CEZARY PUKOWNIK

files:
 - midi.py - code for data extraction, and midi convertion
 - train.py - code for model definition, and training session
 - generate.py - code for model loading, predicting ang saving to midi_dir
 - settings.py - file where deafult settings are stored
 - readme - this file
 - data/midi - directory where input midi are stored
 - data/models - directory where trained models are stored
 - data/output - directory where generated music is stored
 - data/samples - directory where extracted data from midi is stored
 - data/samples.npz - deprecated

How to use:

1. Use midi.py to export data from midi files

  ./midi.py <midi_folder_path> <output_path>

2. Use train.py to train a model (this can take a while)

  ./train.py <input_training_data> <model_save_path> <epochs>

3. Use generate.py to generate music from trained models

  ./generate.py <trained_model_path> <output_path> <treshold>