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* Change the name of the specgram named `waveform`

`F.sliding_window_cmn` takes a spectrogram as input (of shape
`(..., freq, time)`). However, this spectrogram is named `waveform`.
This appears to be an error, so rename this (and the output tensor) to
reflect that both are spectrograms.

* Correct tensor description in docstring

The output tensor of `F.sliding_window_cmn` is also a spectrogram.
Update the description to reflect this.

`SOX_SIGNED_16BIT_TO_SAMPLE` and `SOX_SIGNED_32BIT_TO_SAMPLE` uses left shift on signed integers, (negative values) which is UB.
This PR replaces them (and other sox macros for `uint8` and `float` as well) with Tensor operations.

* Fix install location and suffix for Windows

* Refactor Kaldi compatibility tests
Co-authored-by: Jeff Hwang <jeffhwang@fb.com>

* Parameterize `test_sliding_window_cmn`

* Extract test naming function

* Pass a spectrogram to `F.sliding_window_cmn`

* Set manual seed for remaining rand calls in suite

[ghstack-poisoned]
Co-authored-by: Jeff Hwang <jeffhwang@fb.com>

Parameterize `test_create_fb` so each set of values are tested
independently. Also explicitly skip on older versions of librosa (< 0.7.2) when
`norm="slaney"`.

* Test with batches of differing items

Issues that occur when different items in a batch influence one another
will not present when a batch is composed of identical items. When
checking the consistency of batched behavior, in order to catch these
issues items should be different.

Thus, use different items for the `functional` batch consistency tests
wherever possible.

* Generate different white noise in each channel

Don't duplicate a single channel multiple times. Since this is used for
testing, generate different noise in each channel so data leakage
between channels can be detected.

* Parameterize batch size in batch consistency tests

Rather than creating a batch of 3 items in each test and slicing it to
test two different batch sizes at once, parameterize the batch size on
the TestFunctional class itself. This will generate a separate set of
tests for each batch size (better isolating failures) and removes a
leaky abstraction where the test calling `assert_batch_consistencies`
had to know to give it a batch size greater than 1.

* Check inputs too, to catch in-place operations

Check inputs to the batch consistency operations too, to ensure any
in-place operations operate the same on items as batches - not just that
they output the same result.

* Use much shorter sample for phaser test

Using a 5-second signal for the phaser test takes a long time on CPU,
much longer than the other batch consistency tests. Use a shorter signal
instead.

* Load dual-channel wav for VAD test

The stereo wav has two channels, slightly offset, so they'll count as
different items.

* Load wav using common_utils, not torchaudio.load

* Test pitch frequency with different freqs per item

The pitch frequency batch test was using the same frequency for each
item, which may not catch data leakage between items within a batch. Use
different frequencies so these kinds of issues would be triggered, just
like the other batch consistency tests.

* Explain justification for single-item batch

Some audio formats like `gsm` does not have valid frame numbers when opened. But `libsox` can properly handle these audios, so checking if `length > 0` is not necessary and too strict.

Numpy is a dependency of pytorch so we should let it get resolved as it
install pytorch
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* Fix fileobj I/O undeterministic behavior

Ever since the file-like object support was added in #1158, the test
was occasionally failing in CI. This PR fixes this.