GitLab

Summary:
Applies new import merging and sorting from µsort v1.0.

When merging imports, µsort will make a best-effort to move associated
comments to match merged elements, but there are known limitations due to
the diynamic nature of Python and developer tooling. These changes should
not produce any dangerous runtime changes, but may require touch-ups to
satisfy linters and other tooling.

Note that µsort uses case-insensitive, lexicographical sorting, which
results in a different ordering compared to isort. This provides a more
consistent sorting order, matching the case-insensitive order used when
sorting import statements by module name, and ensures that "frog", "FROG",
and "Frog" always sort next to each other.

For details on µsort's sorting and merging semantics, see the user guide:
https://usort.readthedocs.io/en/stable/guide.html#sorting

Reviewed By: lisroach

Differential Revision: D36402214

fbshipit-source-id: b641bfa9d46242188524d4ae2c44998922a62b4c

Summary:
Applies the black-fbsource codemod with the new build of pyfmt.

paintitblack

Reviewed By: lisroach

Differential Revision: D36324783

fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc

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

Co-authored-by: Ji Chen <jimchen90@devfair0160.h2.fair>

* Add WaveRNN example

This is the pipeline example based on [WaveRNN model](https://github.com/pytorch/audio/pull/735) in torchaudio. The design of this pipeline is inspired by [#632](https://github.com/pytorch/audio/pull/632). It offers a standardized implementation of WaveRNN vocoder in torchaudio.

* Add utils and readme

The metric logger is added based on the Wav2letter pipeline [#632](https://github.com/pytorch/audio/pull/632). It offers the way to parse the standard output as described in readme.

* Add channel dimension

The channel dimension of waveform in datasets is added to match the input dimensions of WaveRNN model because the channel dimensions of waveform and spectrogram are added in [this part] (https://github.com/pytorch/audio/blob/master/torchaudio/models/_wavernn.py#L281) of WaveRNN model.

* Update date split and transform

The design of dataset structure is discussed in [this comment](https://github.com/pytorch/audio/pull/749#discussion_r454627027

). Now the dataset file has a clearer workflow after using the random-split function instead of walking through all the files. All transform functions are put together inside the transforms block.
Co-authored-by: Ji Chen <jimchen90@devfair0160.h2.fair>