Upload finetune.py

finetune.py

@@ -0,0 +1,45 @@
+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()