The transforms can be provided to :py:class:`audtorch.datasets` as an argument and work on the data before it will be returned.
Note
All of the transforms work currently only with :py:obj:`numpy.array` as inputs, not :py:obj:`torch.Tensor`.
.. automodule:: audtorch.transforms
.. autoclass:: Compose :members:
.. autoclass:: Crop :members:
.. autoclass:: RandomCrop :members:
.. autoclass:: Pad :members:
.. autoclass:: RandomPad :members:
.. autoclass:: Replicate :members:
.. autoclass:: RandomReplicate :members:
.. autoclass:: Expand :members:
.. autoclass:: Downmix :members:
.. autoclass:: Upmix :members:
.. autoclass:: Remix :members:
.. autoclass:: Normalize :members:
.. autoclass:: Resample :members:
.. autoclass:: Spectrogram :members:
.. autoclass:: LogSpectrogram :members:
.. autoclass:: RandomAdditiveMix :members: