add readme file, commented out delete_empty_samples function
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@ -39,6 +39,7 @@ def to_midi(samples, output_path=settings.generated_midi_path, program=0, tempo=
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return_midi = roll.Multitrack(tracks=tracks, tempo=tempo, downbeat=[0, 96, 192, 288], beat_resolution=beat_resolution)
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return_midi = roll.Multitrack(tracks=tracks, tempo=tempo, downbeat=[0, 96, 192, 288], beat_resolution=beat_resolution)
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roll.write(return_midi, settings.generated_midi_path)
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roll.write(return_midi, settings.generated_midi_path)
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# todo: this function is running too slow.
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def delete_empty_samples(sample_pack):
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def delete_empty_samples(sample_pack):
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print('Deleting empty samples...')
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print('Deleting empty samples...')
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temp_sample_pack = sample_pack
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temp_sample_pack = sample_pack
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@ -64,6 +65,7 @@ def main():
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continue
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continue
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sample_pack = np.concatenate((midi_samples, sample_pack), axis=0)
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sample_pack = np.concatenate((midi_samples, sample_pack), axis=0)
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# I commented out this line, because it was too slow
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# sample_pack = delete_empty_samples(sample_pack)
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# sample_pack = delete_empty_samples(sample_pack)
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np.savez_compressed(settings.samples_dir, sample_pack)
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np.savez_compressed(settings.samples_dir, sample_pack)
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16
project/readme
Normal file
16
project/readme
Normal file
@ -0,0 +1,16 @@
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## MUSIC GENERATION USING DEEP LEARNING ##
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## AUTHOR: CEZARY PUKOWNIK
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How to use:
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1. Use midi.py to export data from midi files
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./midi.py export <midi_folder_path> <output_path>
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2. Use train.py to train a model (this can take a while)
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./train.py <input_training_data> <model_save_path>
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3. Use generate.py to generate music from trained models
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./generate.py <model_weights_path> <output_path>
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