GitLab

Move the computation of `#classes -> #bits` to the constructor of WaveRNN and attach it to the instance, so that it can be reused elsewhere.

* Use torchaudio melscale instead of librosa

* Add spectrogram normalization option
Co-authored-by: Ji Chen <jimchen90@devfair0160.h2.fair>

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

* Remove underscore of model name
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>