Author: 강성현, 양현서, Richard Novenius

branch: ludrex

• In musicbert/__init__.py, modify the build_model method of class MusicBERTSentencePredictionMultilabelTaskXAI(line 166), following the instruction given in the comment.
• You may either

1. freeze no parameters(default),
2. freeze all parameters in the encoder and only update classifier parameters, or
3. fix $k$ and freeze parameters in encoder layers $0,1,\ldots,k$, where $$0\le k\le \text{(number of encoder layers)}-1.$$ MusicBERT base and small models have 12 and 4 encoder layers, respectively.

• To fine-tune,

bash scripts/classification/train_xai_base_small.sh

• To evaluate fine-tuned models and get test accuracy,

bash scripts/eval_xai.sh

branch: attempt/augmentation

• method:

cd midi_augmentator
pip install -r requirements.txt # mido
python augmentation.py ../processed/segmented_midi/ --pitch_range=-6_12_1 --tempo_range 60_180_60 --velocity_range 50_90_40 

• Generated pitch = -6, -5, -4, -3, ..., +12
• Generated tempo = 60, 120, 180
• Generated velocity = 50, 90
• Generated file name example = {original_filename}_{pitch}_-12_{tempo}_120_{velocity}_90.mid
• Run python generate_total_csv.py segmented_midi/_augmented/ to generate new total.csv
• Then run modified python map_midi_to_label.py to generate midi_label_map_apex_reg_cls.json file.
• Then run modified python -u gen_xai.py xai to generate xai_data_raw_apex_reg_cls_augmented folder.
• Then run modified bash scripts/binarize_xai.sh xai to generate xai_data_bin_apex_reg_cls_augmented folder.
• Then run modified scripts/classification/train_xai_base_small.sh to train the model.