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Summary:
In batch_consistency tests, the `assert_batch_consistency` method only accepts single Tensor, which is not applicable to some methods. For example, `lfilter` and `filtfilt` requires three Tensors as the arguments, hence they don't follow `assert_batch_consistency` in the tests.
This PR refactors the test to accept a tuple of Tensors which have `batch` dimension. For the other arguments like `int` or `str`, they are given as `*args` after the tuple.

Pull Request resolved: https://github.com/pytorch/audio/pull/2245

Reviewed By: mthrok

Differential Revision: D34273035

Pulled By: nateanl

fbshipit-source-id: 0096b4f062fb4e983818e5374bed6efc7b15b056

Summary:
In torchscript_consistency tests, the `func` in each test method only accepts one `tensor` as the argument, for the other arguments of `F.xyz` method, they need to be defined inside the `func`. If there is no `Tensor` argument in `F.xzy`, the tests use a `dummy` tensor which is not used anywhere. In this PR, we refactor ``_assert_consistency`` and ``_assert_consistency_complex`` to accept a tuple of inputs instead of just one `tensor`.

Pull Request resolved: https://github.com/pytorch/audio/pull/2246

Reviewed By: carolineechen

Differential Revision: D34273057

Pulled By: nateanl

fbshipit-source-id: a3900edb3b2c58638e513e1490279d771ebc3d0b

Summary:
In autograd tests, to guarantee the precision, the dtype of Tensors are converted to `torch.float64` if they are real. However, the complex dtype is not considered. This PR adds `self.complex_dtype` support to the inputs.

Pull Request resolved: https://github.com/pytorch/audio/pull/2244

Reviewed By: mthrok

Differential Revision: D34272998

Pulled By: nateanl

fbshipit-source-id: e8698a74d7b8d99ee0fcb5f5cb5f2ffc8c80b9b5

Summary:
Yesterday's release of librosa 0.9.0 made args keyword-only and changed default padding from "reflect" to "zero" for some functions. This PR adjusts callsites in our tutorials and tests accordingly.

Pull Request resolved: https://github.com/pytorch/audio/pull/2208

Reviewed By: mthrok

Differential Revision: D34099793

Pulled By: hwangjeff

fbshipit-source-id: 4e2642cdda8aae6d0a928befaf1bbb3873d229bc

Summary:
Adds parameter `p` to `TimeMasking` to allow for enforcing an upper bound on the proportion of time steps that it can mask. This behavior is consistent with the specifications provided in the SpecAugment paper (https://arxiv.org/abs/1904.08779).

Pull Request resolved: https://github.com/pytorch/audio/pull/2090

Reviewed By: carolineechen

Differential Revision: D33344772

Pulled By: hwangjeff

fbshipit-source-id: 6ff65f5304e489fa1c23e15c3d96b9946229fdcf

Summary:
Pull Request resolved: https://github.com/pytorch/audio/pull/2096

run: `arc lint --apply-patches --paths-cmd 'hg files -I "./**/*.py"'`

Reviewed By: mthrok

Differential Revision: D33297351

fbshipit-source-id: 7bf5956edf0717c5ca90219f72414ff4eeaf5aa8

Following the plan #1337, this commit drops the support for pseudo complex type from `F.phase_vocoder` and `T.TimeStretch`.

Following the plan #1337, this commit drops the support for pseudo complex type from 
`F.spectrogram` and `T.Spectrogram`.

It also deprecates the use of `return_complex` argument.

* put output tensor on device in `get_whitenoise()`

* Update `get_spectrogram()` so that window uses same device as waveform

* put window on proper device in `test_griffinlim()`

Introduce a helper function `torch_script` that performs scripting in the recommended way.

* Add basic filtfilt implementation

* Add filtfilt to functional package; add tests
Co-authored-by: V G <vladislav.goncharenko@phystech.edu>

- Provide InverseSpectrogram module that corresponds to Spectrogram module
- Add length parameter to the forward method in transforms
Co-authored-by: dgenzel <dgenzel@fb.com>
Co-authored-by: Zhaoheng Ni <zni@fb.com>

- Renamed torchaudio.functional.create_fb_matrix to torchaudio.functional.melscale_fbanks.
- Added interface with a warning for create_fb_matrix

Summary:
- Add linear_fbank method
- Add LFCC in transforms

* [BC-Breaking] Default to native complex type when returning raw spectrogram

Part of https://github.com/pytorch/audio/issues/1337 .

- This code changes the return type of spectrogram to be native complex dtype,
when (and only when) returning raw (complex-valued) spectrogram.
- Change `return_complex=False` to `return_complex=True` in spectrogram ops.
- `return_complex` is only effective when `power` is `None`. It is ignored for
cases where `power` is not `None`. Because the returned Tensor is power spectrogram,
which is real-valued Tensors.

* Remove `class FunctionalComplex` header accidentally re-introduced in #1490 

It was reported in #1478 that spectrogram masking operations were done in-place and modified the original input tensors. This PR fixes this behavior and adds tests to ensure that the input tensor is not changed.

- Put functional test logic into one place, `functional_impl.py`
- Tidy imports

* Fixed floor_divide deprecation warnings seen in pytest output

* Fixed warning in test_flanger_triangle_linear

The VAD function trims the input tensor to the first instance of voice
activity on any channel or item. Trimming batches this way may be
undesirable as the item with earliest activity will dominate. Either
way, the batch behaviour does not match the itemwise behaviour.

The VAD batch consistency tests currently pass out of coincidence, but
they specify incorrect behaviour. This commit removes them.

Use shorter input sequences to avoid time out error on CI

Merges lfilter and spectrogram classes together in the common implementation and modifies the cpu and gpu test definitions accordingly