GitLab

Differential Revision: D50677606

Pull Request resolved: https://github.com/pytorch/audio/pull/3681

Differential Revision: D49965263

Pull Request resolved: https://github.com/pytorch/audio/pull/3639

Summary:
* Merge backend doc into torchaudio toplevel doc
* Update backend, dispatcher, installation doc

Pull Request resolved: https://github.com/pytorch/audio/pull/3555

Reviewed By: huangruizhe

Differential Revision: D48326812

Pulled By: mthrok

fbshipit-source-id: cc0d7326eacfebd341323b5d613ca1777255748b

Summary:
Currently, AudioEffector always resample to the original sample rate. It is more flexible to allow overriding this to any sample rate.

Pull Request resolved: https://github.com/pytorch/audio/pull/3374

Differential Revision: D46235358

Pulled By: mthrok

fbshipit-source-id: 39a5d4e38d9b90380da31d0ce9ee8090668b54e4

Summary:
When encoding audio with mulaw, the resulting data does not have header, and the StreamReader defaults to 16k Hz, which can strech/shrink the resulting waveform.

Pull Request resolved: https://github.com/pytorch/audio/pull/3372

Reviewed By: hwangjeff

Differential Revision: D46234772

Pulled By: mthrok

fbshipit-source-id: 942c89a8cfe29b0b6f57b3e5b6c9dfd3524ca552

Summary:
This commit adds a new feature AudioEffector, which can be used to
apply various effects and codecs to waveforms in Tensor.

Under the hood it uses StreamWriter and StreamReader to apply
filters and encode/decode.

This is going to replace the deprecated `apply_codec` and
`apply_sox_effect_tensor` functions.

It can also perform online, chunk-by-chunk filtering.

Tutorial to follow.

closes https://github.com/pytorch/audio/issues/3161

Pull Request resolved: https://github.com/pytorch/audio/pull/3163

Reviewed By: hwangjeff

Differential Revision: D44576660

Pulled By: mthrok

fbshipit-source-id: 2c5cc87082ab431315d29d56d6ac9efaf4cf7aeb