GitLab

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

`wavpack` is a format not supported/tested in torchaudio.
Leaving the option blank can cause the issue like #1461 in untested environment.

* Use torchaudio melscale instead of librosa

* 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

* Remove an invalid InverseMel comparison unit test

Similar to #1426 `test_InverseMelScale` in `librosa_compatibility_test` is not
ensuring the comaptibility to librosa. Having this test can give a wrong statement
about the librosa numerical compatibility about the function.

* Add test for InverseMelScale

The new test compares the result of inverse mel scale against the reference spectrogram, so no need to use librosa.
This test serves as more like an insurance that the change to the implementation of InverseMelScale only improves the result, not the other way.

This code was added as a part of investigation of inconsistent behavior
of file-like object support. Now the investigation is done and this code
does not provide much insight yet adding a bunch of lines to the log, so
this PR removes it.

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

This reverts commit 8d2eeb11.

* Added CodeQL and Bandit security checks as GitHub Actions

* Fixed renegerate jinja2 bug

* Removed specific Bandit fixes

* Update spectrogram to use complex
* Update autograd test
* Update TS test
* Update librosa test

1. `F.phase_vocoder` accepts Tensor with complex dtype.
    * The implementation path has been updated from #758 so that they share the same code path by internally converting the input Tensor to complex dtype and performing all the operation on top of it.
    * Adopted `torch.polar` for simpler Tensor generation from magnitude and angle.
2. Updated tests
    * librosa compatibility test for complex dtype and pseudo complex dtype
        * Extracted the output shape check test and moved it to functional so that it will be tested on all the combination of `{CPU | CUDA} x {complex64 | complex128}`
    * TorchScript compatibility test for `F.phase_vocoder` and `T.TimeStretch`.
    * batch consistency test for `T.TimeStretch`.