| from model import PopMusicTransformer | |
| from glob import glob | |
| import os | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '0' | |
| def main(): | |
| # declare model | |
| model = PopMusicTransformer( | |
| checkpoint='REMI-tempo-checkpoint', | |
| is_training=True) | |
| # prepare data | |
| midi_paths = glob('YOUR PERSOANL FOLDER/*.midi') # you need to revise it | |
| training_data = model.prepare_data(midi_paths=midi_paths) | |
| # check output checkpoint folder | |
| #################################### | |
| # if you use "REMI-tempo-chord-checkpoint" for the pre-trained checkpoint | |
| # please name your output folder as something with "chord" | |
| # for example: my-love-chord, cute-doggy-chord, ... | |
| # if use "REMI-tempo-checkpoint" | |
| # for example: my-love, cute-doggy, ... | |
| #################################### | |
| output_checkpoint_folder = 'REMI-finetune' # your decision | |
| if not os.path.exists(output_checkpoint_folder): | |
| os.mkdir(output_checkpoint_folder) | |
| # finetune | |
| model.finetune( | |
| training_data=training_data, | |
| output_checkpoint_folder=output_checkpoint_folder) | |
| #################################### | |
| # after finetuning, please choose which checkpoint you want to try | |
| # and change the checkpoint names you choose into "model" | |
| # and copy the "dictionary.pkl" into the your output_checkpoint_folder | |
| # ***** the same as the content format in "REMI-tempo-checkpoint" ***** | |
| # and then, you can use "main.py" to generate your own music! | |
| # (do not forget to revise the checkpoint path to your own in "main.py") | |
| #################################### | |
| # close | |
| model.close() | |
| if __name__ == '__main__': | |
| main() | |