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- Addresses #549 #638 #786 
- Add `torchaudio` top level module doc
- Separate `torchaudio` top level module doc from `index.html`
- Add `backend` module doc.
- Remove `-> None` from function signature as it adds noise to documentation
- Changed function argument name of `torchaudio.backend.sox_io_backend.save` from `tensor` to `src`, so that it matches with the reset of backends.
- Tweak bunch of docstrings

* Add libritts

Add LibriTTS dataset draft

* Add libritts

Use two separate ids for utterance_id.

* Update output form

Use full_id as utterance_id.

* Update format

Add space and test black format

* Update test method

* Add audio and text test

Generate audio and test files on-the-fly in test 

* Update format

* Fix test error and remove assets libritts

The test error is fixed by sorting the file in 4th element instead of 2nd element in samples. Since the files are generated on-the-fly, so the the libritts files in assets are removed.

* Add seed in `get_whitenoise` function

* Change utterance to text

Change `_utterance` to `_text`.
Co-authored-by: Ji Chen <jimchen90@devfair0160.h2.fair>

* Add sox_utils module

* Make init/shutdown thread safe

* Add sox effects implementation

* Add test for sox effects

* Update docstrings and add examples

* Add cmu_arctic dataset

* add dataset name

* update audio test file with whitenoise.wav file

* add test text file

* update text method and file name

* update comment

* change datasets order in doc

* add line length
Co-authored-by: Ji Chen <jimchen90@devfair0160.h2.fair>

* Add bass with biquad

* Update functional.py

Add the normalization coefficients

* Update test_sox_compatibility.py

In test_sox_compatibility.py file, I add two bass tests: one test sets gain = 30, atol = 1e-4, the other sets gain = 40, atol = 1.5e-4. The details can be seen in pytorch#676

* Update torchscript_consistency_impl.py

Add torchscript test

* Add flake8 test
Co-authored-by: Ji Chen <jimchen90@devfair0160.h2.fair>

* Added the popular GTZAN dataset:

* Added the GTZAN class in torchaudio.datasets using the same format as the rest of the datasets.
* Added the appropriate test function in test_datasets.py.
* Added the GTZAN class in the datasets.rst documentation file.

* Addressed review issues in PR #668

* Added dummy noise .wav in `test/assets/`
* Removed transforms of input and output from the dataset
  `__init__` function, as well as the corresponding methods.
* Replaced rendundant `filtered` and `subset` methods from
  class initialization and also changed the corresponding
  assertion message.

* Fixed E303: too many blank lines error

* Added GTZAN to __init__.__all__

* Fixed incorrectly not importing GTZAN

* removed duplicate warning

* lint
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>

* Add flanger to functional
Signed-off-by: Bhargav Kathivarapu <bhargavkathivarapu31@gmail.com>

* Add random seed
Signed-off-by: Bhargav Kathivarapu <bhargavkathivarapu31@gmail.com>

* fix flanger
Signed-off-by: Bhargav Kathivarapu <bhargavkathivarapu31@gmail.com>

* shape

* Change bool arguments to strings
Signed-off-by: Bhargav Kathivarapu <bhargavkathivarapu31@gmail.com>

* Refactor tests
Signed-off-by: Bhargav Kathivarapu <bhargavkathivarapu31@gmail.com>
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>

Signed-off-by: Bhargav Kathivarapu <bhargavkathivarapu31@gmail.com>

* add wav2letter model

* add unit_test to model

* add docstrings

* add documentation

* fix minor error, change logic on forward

* update padding same with ceil

* add inline typing and minor fixes to docstrings

* remove python2

* add formula do docstrings, change param name

* add test with mfcc, add pytest

* fix bug, update docstrings

* change parameter name

* initial test, stub function, transform and docstring

* add draft working implementation, update docstrings

* merge VadSate into Vad calss, move Channel into Vad class

* remove functional stub for vad

* add wav file for test

* refactor _measure() to improve performance

* rename argument

* replace copy_ with assignment

* refactor init, update documentation, update test for readability

* clean up default values

* move code from transforms.py to funtional.py and integrate state into a function

* remove Channel state class

* fix calcuation of a flush point

* make multiple channels work

* clean up multi-channel, update test

* rename variables and re-org arguments for _measure

* fix linting errors

* add torchscript consistency test and fix errors

* support and test batch consistency, fix normalization

* update documentation, switch torchscript consistancy test to use transform to improve coverage

* fix linting errors

* remove un-used imports

* address PR comments

* add doc references into rst

* formatting.

* update datasets.

* Add overdrive to functional

* Minor change to overdrive

* Minor change to overdrive

* minor flake8 changes

* changes to make overdrive generic
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>

* Add dcshift to functional

* Doc string change and remove inplace clamp

* Minor Fix to dcshit and separate sox test refactoring

* Minor change to limiter_gain type

* adding dcshift to __all__ in functional

* add cmvn

* Update transforms.rst

add cmvn

* Correct the format

* Correct the format

* Correct the format

* add test unit and cmvn change to cmn

* fix bug
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>

* Add contrast to functional

* add tests for contrast and update functional.rst

* Minor changes to sox and batch tests for contrast

* Add Vol with gain_type amplitude

* add gain in db and add tests

* add gain_type "power" and tests

* add functional DB_to_amplitude

* simplify

* remove functional

* improve docstring

* add to documentation

* add basics for Fade

* add fade possibilities: at start, end or both

* add different types of fade

* add docstrings, add overriding possibility

* remove unnecessary logic

* correct typing

* agnostic to batch size or n_channels

* add batch test to Fade

* add transform to options

* add test_script_module

* add coherency with test batch

* remove extra step for waveform_length

* update docstring

* add test to compare fade with sox

* change name of fade_shape

* update test fade vs sox with new nomenclature for fade_shape

* add Documentation
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>

* 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)