This repository includes our metadata and code for the submission of IMT Atlantique - BRAIN to the DCASE 2020 challenge, Task 1, subtask B. Our technical report is here
For each of the four submitted model :
- metadata of our submissions in yaml files
- Detailed parameter counts, layer-wise (Model_X.csv)
- Detailed loss per category and summary of pruning (log_modelX.txt)
Code is based on pytorch (1.5), sklearn, pandas, numpy, scipy.
Dataset from TAU Urban Acoustic Scenes 2020 3Class can be download at
To resample run,
python resample.py --input_path [path dataset] --output_path [save path]
Note: The output path to the audio dataset has to be specified in create_dataset.py before running the next scripts.
Launch training
python main_training.py --saving [0 (no), 1 (yes)] --model_type ["ModelA", "ModelB", "ModelC", "ModelD"] --lr [learning rate (float)] --epochs=20 --batch_size=64 --da_mode ['cutmix', 'mixup', 'random_crop']
Exemple with ModelB and 5% of the dataset without Data Augmentation (Quick check)
python main_training.py --saving=1 --model_type="ModelB" --lr=1e-3 --epochs=3 --batch_size=32 --frac_data=0.05
Note: Model filename can be found in 'models/' after training with --saving=1
Prune model
python pruning_torch.py --model_filename [Model filename] --da_mode ['cutmix', 'mixup', 'random_crop']
Quantify model
python quantify_torch.py --model_filename [Model filename]
Get statistics of the model
python model_statistics.py --model_filename [Model filename]
Create submission csv file
python create_submission.py --model_filename [Model filename] --eval_csv_path [Path to the evaluation csv file] --eval_data_path [Path to evaluation audio directory]
Pretrained models will be uploaded after the challenge deadline.
- Nicolas Pajusco
- Richard Huang
- Nicolas Farrugia (PI)
Acknowledgments to Carlos Lassance, Ghouthi Boukli Hacene, Vincent Gripon and other members of BRAIN for feedback, comments and informal discussions regarding this submission.