DeMask is an end-to-end model for enhancing speech while wearing face masks — offering a clear benefit during times when face masks are mandatory in many spaces and for workers who wear face masks on the job. Built with Asteroid, a PyTorch-based audio source separation toolkit, DeMask is trained to recognize distortions in speech created by the muffling from face masks and to adjust the speech to make it sound clearer. 
This submission stood out in particular because it represents both a high-quality idea and an implementation that can be reproduced by other researchers.
Here is an example on how to train a speech separation model in less than 20 lines:
```python
from torch import optim
from pytorch_lightning import Trainer
from asteroid import ConvTasNet
from asteroid.losses import PITLossWrapper
from asteroid.data import LibriMix
from asteroid.engine import System
train_loader, val_loader = LibriMix.loaders_from_mini(task='sep_clean', batch_size=4)