GitLab

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2480

Reviewed By: nateanl

Differential Revision: D37249571

Pulled By: carolineechen

fbshipit-source-id: caefeec4253c91f2579655a0c1735edaeed51be9

Summary:
The `LibriLightLimited` dataset is created for fine-tuning SSL models, such as Wav2Vec2 and HuBERT. It is a supervised subset of [Libri-Light](https://github.com/facebookresearch/libri-light) dataset. To distinguish the unsupervised subset and the supervised one, it's clearer to put it in a separate dataset class for fine-tuning purpose.
It contains "10 min", "1 hour", "10 hour" splits.

Pull Request resolved: https://github.com/pytorch/audio/pull/2302

Reviewed By: mthrok

Differential Revision: D36388188

Pulled By: nateanl

fbshipit-source-id: ba49f1c9996be17db5db41127d8ca96224c94249

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2371

Reviewed By: xiaohui-zhang

Differential Revision: D36246167

Pulled By: carolineechen

fbshipit-source-id: 23042a1c393711864a18c9815d248c18d1d258b4

Summary:
The `LibriMix` dataset is missing on the [documentation webpage](https://pytorch.org/audio/stable/datasets.html).

Pull Request resolved: https://github.com/pytorch/audio/pull/2351

Reviewed By: carolineechen

Differential Revision: D35926695

Pulled By: nateanl

fbshipit-source-id: 168aed3bb15510d1b1ec57d77727932e481aca48

Summary:
implementation adapted from [s3prl](https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/quesst14_dtw/dataset.py)

modifying the s3prl downstream expert to [this](https://github.com/carolineechen/s3prl/commit/adc91a53d581a604f495f3795a865d84aa17f1a5) using this dataset implementation produces the same results as using the original s3prl pipeline

Pull Request resolved: https://github.com/pytorch/audio/pull/2290

Reviewed By: nateanl

Differential Revision: D35692551

Pulled By: carolineechen

fbshipit-source-id: 035ad161d4cbbd2072411cfdf89984b73a89868c

Summary:
- Remove unnecessary content list
- Remove legacy description

Pull Request resolved: https://github.com/pytorch/audio/pull/2029

Reviewed By: carolineechen

Differential Revision: D32629917

Pulled By: mthrok

fbshipit-source-id: bc9a9366c681bcf8b74907c2a6459c73fb6a7424

* Add griffinlim and DB_to_amplitude
* Fix Dataset docstring
* Fix other formatting
Co-authored-by: krishnakalyan3 <skalyan@cloudera.com>

* Tweak docstring, audio_ext, load method signature and constructor of VCTK_092

* Add test for VCTK_092 dataset.

* Added version 0.92 of VCTK dataset
Signed-off-by: Abhishek Dubey <abhi.dubey011999@gmail.com>

* 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 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>

* 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>

* formatting.

* update datasets.