The repository contains code for the submission of the lileonardo team to the 2021 Emotion and Theme Recognition in Music task of MediaEval 2021 (results).
python >= 3.7pip install -r requirements.txtin a virtual environment- Download data from the MTG-Jamendo Dataset in
data/jamendo. Audio files go todata/jamendo/mp3and melspecs todata/jamendo/melspecs. - Process 128 bands mel spectrograms and store them in
data/jamendo/melspecs2by running:python preprocess.py experiments/preprocessing/melspecs2.json
Run python main.py experiments/DIR where DIR contains the parameters.
Parameters are overridable by command line arguments:
python main.py --helpusage: main.py [-h] [--data_dir DATA] [--num_workers NUM] [--restart_training] [--restore_name NAME]
[--num_epochs EPOCHS] [--learning_rate LR] [--weight_decay WD] [--dropout DROPOUT]
[--batch_size BS] [--manual_seed SEED] [--model MODEL] [--loss LOSS]
[--calculate_stats]
DIRECTORY
Train according to parameters in DIRECTORY
positional arguments:
DIRECTORY path of the directory containing parameters
optional arguments:
-h, --help show this help message and exit
--data_dir DATA path of the directory containing data (default: data)
--num_workers NUM number of workers for dataloader (default: 4)
--restart_training overwrite previous training (default is to resume previous training)
--restore_name NAME name of checkpoint to restore (default: last)
--num_epochs EPOCHS override number of epochs in parameters
--learning_rate LR override learning rate
--weight_decay WD override weight decay
--dropout DROPOUT override dropout
--batch_size BS override batch size
--manual_seed SEED override manual seed
--model MODEL override model
--loss LOSS override loss
--calculate_stats recalculate mean and std of data (default is to calculate only when they
don't exist in parameters)
The predictions are averaged by running:
python average.py --outputs experiments/convs-m96*/predictions/test-last-swa-outputs.npy --targets experiments/convs-m96*/predictions/test-last-swa-targets.npy --preds_path predictions/convs.npypython average.py --outputs experiments/filters-m128*/predictions/test-last-swa-outputs.npy --targets experiments/filters-m128*/predictions/test-last-swa-targets.npy --preds_path predictions/filters.npypython average.py --outputs predictions/convs.npy predictions/filters.npy --targets predictions/targets.npy