SSC#

class tonic.datasets.SSC(save_to: str, split: str = 'train', transform: Optional[Callable] = None, target_transform: Optional[Callable] = None)[source]#

Spiking Speech Commands

@article{cramer2020heidelberg,
  title={The heidelberg spiking data sets for the systematic evaluation of spiking neural networks},
  author={Cramer, Benjamin and Stradmann, Yannik and Schemmel, Johannes and Zenke, Friedemann},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2020},
  publisher={IEEE}
}
Parameters:

  • save_to (string) – Location to save files to on disk. Will put files in an ‘hsd’ subfolder.

  • split (string) – One of ‘train’, ‘test’ or ‘valid’.

  • transform (callable, optional) – A callable of transforms to apply to the data.

  • target_transform (callable, optional) – A callable of transforms to apply to the targets/labels.

Returns:

A dataset object that can be indexed or iterated over. One sample returns a tuple of (events, targets).