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torchaudio.datasets
====================

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.. py:module:: torchaudio.datasets

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All datasets are subclasses of :class:`torch.utils.data.Dataset`
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and have ``__getitem__`` and ``__len__`` methods implemented.
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Hence, they can all be passed to a :class:`torch.utils.data.DataLoader`
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which can load multiple samples parallelly using ``torch.multiprocessing`` workers.
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For example: ::
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    yesno_data = torchaudio.datasets.YESNO('.', download=True)
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    data_loader = torch.utils.data.DataLoader(yesno_data,
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                                              batch_size=1,
                                              shuffle=True,
                                              num_workers=args.nThreads)

The following datasets are available:

.. contents:: Datasets
    :local:

All the datasets have almost similar API. They all have two common arguments:
``transform`` and  ``target_transform`` to transform the input and target respectively.


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.. currentmodule:: torchaudio.datasets
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CMUARCTIC
~~~~~~~~~

.. autoclass:: CMUARCTIC
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  :members:
  :special-members: __getitem__
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CMUDict
~~~~~~~~~

.. autoclass:: CMUDict
  :members:
  :special-members: __getitem__


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COMMONVOICE
~~~~~~~~~~~

.. autoclass:: COMMONVOICE
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  :members:
  :special-members: __getitem__
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GTZAN
~~~~~

.. autoclass:: GTZAN
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  :members:
  :special-members: __getitem__
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LIBRISPEECH
~~~~~~~~~~~

.. autoclass:: LIBRISPEECH
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  :members:
  :special-members: __getitem__
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LIBRITTS
~~~~~~~~

.. autoclass:: LIBRITTS
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  :members:
  :special-members: __getitem__
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LJSPEECH
~~~~~~~~

.. autoclass:: LJSPEECH
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  :members:
  :special-members: __getitem__
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SPEECHCOMMANDS
~~~~~~~~~~~~~~

.. autoclass:: SPEECHCOMMANDS
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  :members:
  :special-members: __getitem__
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TEDLIUM
~~~~~~~~~~~~~~

.. autoclass:: TEDLIUM
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  :members:
  :special-members: __getitem__

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VCTK_092
~~~~~~~~

.. autoclass:: VCTK_092
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  :members:
  :special-members: __getitem__
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DR_VCTK
~~~~~~~~

.. autoclass:: DR_VCTK
  :members:
  :special-members: __getitem__


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YESNO
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~~~~~
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.. autoclass:: YESNO
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  :members:
  :special-members: __getitem__