Orientation for PyTorch users

OpenSoundscape uses PyTorch “under the hood” for machine learning tasks. If you’re familiar with PyTorch, these connections to the OpenSoundscape API should let you start using pieces of OpenSoundscape within your current workflows without much hacking:

• AudioFileDataset and AudioSplittingDataset subclass torch.dataset and include preprocessing and augmentation. If you use them as a drop-in substitution for a PyTorch Dataset, pay attention to your DataLoader’s collate_fn. You can set the collate_fn to opensoundscape.ml.utils.collate_audio_samples_to_tensors to stack samples and labels with a leading batch dimension, as is typical in PyTorch.

• opensoundscape.ml.CNN class’s .network attribute is a PyTorch model object.

• The CNN class implements high-level methods .train(), .predict(), .eval(), and .generate_cams() APIs which do what you expect them to

• integration with the Weights and Biases logging platform is built-in; pass a wandb session object to CNN‘s .train() or .predict() method, and get live sample logging and metrics

• the CNN‘s .preprocessor attribute (which becomes AudioFileDataset/AudioSplittingDataset’s .preprocessor during .train() and .predict()) defines a sequential series of preprocessing & augmentation actions performed on each sample. Specifically, preprocessor.pipeline is a pd.Series and each item is an instance of the opensoundscape.preprocess.action.Action class (or subclass). See the preprocessing tutorial notebook for specifics on modifying preprocessing.