def main():
# declare model
model = PopMusicTransformer(checkpoint='Chinese-piano-checkpoint',
is_training=True)
# prepare data
midi_paths = glob('data/train/*.mid') # you need to revise it
training_data = model.prepare_data(midi_paths=midi_paths)
# check output checkpoint folder
output_checkpoint_folder = 'my-piano' # 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()
def main():
# declare model
model = PopMusicTransformer(checkpoint='REMI-tempo-checkpoint',
is_training=True)
# prepare data
midi_paths = glob('finetune_midis/*.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()
def main():
# declare model
model = PopMusicTransformer(checkpoint='REMI-tempo-checkpoint',
is_training=True,
use_chords=True,
train_from_scratch=False)
# prepare data
midi_paths = glob('midi/**/*.midi', recursive=True) + glob(
'midi/**/*.mid', recursive=True) # you need to revise it
if os.path.exists(
"233.data"
): # Revise the training data save location, as data preprocessing could take hours or even days on commercial hardware.
with open("233.data", "rb") as file:
training_data = pickle.load(file)
else:
training_data = model.prepare_data(midi_paths=midi_paths)
with open("233.data", "wb") as file:
pickle.dump(training_data, file,
protocol=4) # For large preprocessed files
# check output checkpoint folder
####################################
# Restrictions on the folder name is removed.
####################################
output_checkpoint_folder = 'REMI-chord-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()