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* Inverse Mel Scale Implementation

* Inverse Mel Scale Docs

* Better working version.

* GPU fix

* These shouldn't go on git..

* Even better one, but does not support JITability.

* Remove JITability test

* Flake8

* n_stft is a must

* minor clean up of initialization

* Add librosa consistency test

This PR follows up #366 and adds test for `InverseMelScale` (and `MelScale`) for librosa compatibility.

For `MelScale` compatibility test;
1. Generate spectrogram
2. Feed the spectrogram to `torchaudio.transforms.MelScale` instance
3. Feed the spectrogram to `librosa.feature.melspectrogram` function.
4. Compare the result from 2 and 3 elementwise.
Element-wise numerical comparison is possible because under the hood their implementations use the same algorith.

For `InverseMelScale` compatibility test, it is more elaborated than that.
1. Generate the original spectrogram
2. Convert the original spectrogram to Mel scale using `torchaudio.transforms.MelScale` instance
3. Reconstruct spectrogram using torchaudio implementation
3.1. Feed the Mel spectrogram to `torchaudio.transforms.InverseMelScale` instance and get reconstructed spectrogram.
3.2. Compute the sum of element-wise P1 distance of the original spectrogram and that from 3.1.
4. Reconstruct spectrogram using librosa
4.1. Feed the Mel spectrogram to `librosa.feature.inverse.mel_to_stft` function and get reconstructed spectrogram.
4.2. Compute the sum of element-wise P1 distance of the original spectrogram and that from 4.1. (this is the reference.)
5. Check that resulting P1 distance are in a roughly same value range.

Element-wise numerical comparison is not possible due to the difference algorithms used to compute the inverse. The reconstructed spectrograms can have some values vary in magnitude.
Therefore the strategy here is to check that P1 distance (reconstruction loss) is not that different from the value obtained using `librosa`. For this purpose, threshold was empirically chosen

```
print('p1 dist (orig <-> ta):', torch.dist(spec_orig, spec_ta, p=1))
print('p1 dist (orig <-> lr):', torch.dist(spec_orig, spec_lr, p=1))
>>> p1 dist (orig <-> ta): tensor(1482.1917)
>>> p1 dist (orig <-> lr): tensor(1420.7103)
```

This value can vary based on the length and the kind of the signal being processed, so it was handpicked.

* Address review feedbacks

* Support arbitrary batch dimensions.

* Add batch test

* Use view for batch

* fix sgd

* Use negative indices and update docstring

* Update threshold
Co-authored-by: Charles J.Y. Yoon <jaeyeun97@gmail.com>

* Griffin-Lim Transformation Implementation

* Griffin-Lim Docs

* Remove f-string from backwards compatibility

* iSTFT is now jit-able.

* Comment changes

* Functional Implementation & now jitable

* flake8

* Doc & GPU Fix

* Librosa comparison test

* test directly griffinlim's output. tighter atol.

* matching signature to docstring.
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>

* since we no longer use decoration, this fixes #165.

* remove import of _docs.

* sync docs with functionals.

* Adding transforms to documentations. Moving augmentations in transforms.

* Add basic low pass filtering
* Add highpass filtering
* More tests of IIR vs FIR
* Implement convolve function, add tests
* Move lfilter and convolve into functional, more tests
* added additional documentation for convolve and lfilter, renamed functional_filtering to functional_sox_convenience
* Follow naming convention for sample rate in functional
* fix failing vctk manifest test to account for adding more test audios into assets
* Adding documentation for lfilter, biquad, highpass_biquad, lowpass_biquad
* added matrix based implementation of lfilter
* adding python lfilter implementation
* factor out biquad, lowpass, highpass to sox compatibility

Add Kaldi IO as a dependency + put  a wrapper to convert to Tensor + add test to check correct type (#111)