Code of the lileonardo team for the 2021 Emotion and Theme Recognition in Music task of MediaEval 2021

Related tags

Deep Learningemotion-theme-recognition
Overview

Emotion and Theme Recognition in Music

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).

Requirements

  • python >= 3.7
  • pip install -r requirements.txt in a virtual environment
  • Download data from the MTG-Jamendo Dataset in data/jamendo. Audio files go to data/jamendo/mp3 and melspecs to data/jamendo/melspecs.
  • Process 128 bands mel spectrograms and store them in data/jamendo/melspecs2 by running: 
    python preprocess.py experiments/preprocessing/melspecs2.json

Usage

Run python main.py experiments/DIR where DIR contains the parameters.

Parameters are overridable by command line arguments:

python main.py --help
usage: 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)

Ensemble predictions

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.npy
python 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.npy
python average.py --outputs predictions/convs.npy predictions/filters.npy --targets predictions/targets.npy
Owner
Vincent Bour
Vincent Bour
GitHub Repository
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