GitLab

Summary:
`InverseMelScale` is missing from the nightly documentation webpage. `MelScale` is better in Feature Extractions section. This PR moves both documents into Feature Extractions section.

Pull Request resolved: https://github.com/pytorch/audio/pull/2967

Reviewed By: mthrok

Differential Revision: D42387886

Pulled By: nateanl

fbshipit-source-id: cdac020887817ea2530bfb26e8ed414ae4761420

Summary:
The generator part of HiFiGAN model is a vocoder which converts mel spectrogram to waveform. It makes more sense to name it as vocoder for better understanding.

Pull Request resolved: https://github.com/pytorch/audio/pull/2955

Reviewed By: carolineechen

Differential Revision: D42348864

Pulled By: nateanl

fbshipit-source-id: c45a2f8d8d205ee381178ae5d37e9790a257e1aa

Summary:
Closes [T138011314](https://www.internalfb.com/intern/tasks/?t=138011314)
## Description
- Add  bundle `HIFIGAN_GENERATOR_V3_LJSPEECH` to prototypes. The bundle contains pre-trained HiFiGAN generator weights from the [original HiFiGAN publication](https://github.com/jik876/hifi-gan#pretrained-model), converted slightly to fit our model
- Add tests
  - unit tests checking that vocoder and mel-transform implementations in the bundle give the same results as the original ones. Part of the original HiFiGAN code is ported to this repo to enable these tests
  - integration test checking that waveform reconstructed from mel spectrogram by the bundle is close enough to the original
- Add docs

Pull Request resolved: https://github.com/pytorch/audio/pull/2921

Reviewed By: nateanl, mthrok

Differential Revision: D42034761

Pulled By: sgrigory

fbshipit-source-id: 8b0dadeed510b3c9371d6aa2c46ec7d8378f6048

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2935

Reviewed By: mthrok

Differential Revision: D42302275

Pulled By: hwangjeff

fbshipit-source-id: d995d335bf17d63d3c1dda77d8ef596570853638

Summary:
Artifact: [subtractive_synthesis_tutorial](https://output.circle-artifacts.com/output/job/4c1ce33f-834d-48e0-ba89-2e91acdcb572/artifacts/0/docs/tutorials/subtractive_synthesis_tutorial.html)

Pull Request resolved: https://github.com/pytorch/audio/pull/2934

Reviewed By: carolineechen

Differential Revision: D42284945

Pulled By: mthrok

fbshipit-source-id: d255b8e8e2a601a19bc879f9e1c38edbeebaf9b3

Summary:
This commit adds "filter_waveform" prototype function.

This function can apply non-stationary filters across the time.
It also performs cropping at the end to compensate the delay introduced by filtering.
The figure bellow illustrates this.

See [subtractive_synthesis_tutorial](https://output.circle-artifacts.com/output/job/5233fda9-dadb-4710-9389-7e8ac20a062f/artifacts/0/docs/tutorials/subtractive_synthesis_tutorial.html) for example usages.

![figure](https://download.pytorch.org/torchaudio/doc-assets/filter_waveform.png)

Pull Request resolved: https://github.com/pytorch/audio/pull/2928

Reviewed By: carolineechen

Differential Revision: D42199955

Pulled By: mthrok

fbshipit-source-id: e822510ab8df98393919bea33768f288f4d661b2

Summary:
Adds filter design tutorial, which demonstrates `sinc_impulse_response` and `frequency_impulse_response`.

Example:
 - [filter_design_tutorial](https://output.circle-artifacts.com/output/job/bd22c615-9215-4b17-a52c-b171a47f646c/artifacts/0/docs/tutorials/filter_design_tutorial.html)

Pull Request resolved: https://github.com/pytorch/audio/pull/2894

Reviewed By: xiaohui-zhang

Differential Revision: D42117658

Pulled By: mthrok

fbshipit-source-id: f7dd04980e8557bb6f0e0ec26ac2c7f53314ea16

Summary:
Currently, the documentation page for `torchaudio.models` have separate sections for model definitions and factory functions.

The relationships between models and factory functions are not immediately clear.

This commit moves the list of factory functions to the list of models.

After:
 - https://output.circle-artifacts.com/output/job/242a9521-7460-4043-895b-9995bf5093b5/artifacts/0/docs/generated/torchaudio.models.Wav2Vec2Model.html

<img width="1171" alt="Screen Shot 2022-12-08 at 8 41 03 PM" src="https://user-images.githubusercontent.com/855818/206603743-74a6e368-c3cf-4b87-b854-518a95893f06.png">

Pull Request resolved: https://github.com/pytorch/audio/pull/2902

Reviewed By: carolineechen

Differential Revision: D41897800

Pulled By: mthrok

fbshipit-source-id: a3c01d28d80e755596a9bc37c951960eb84870b9

Summary:
Addressed mthrok's comments in https://github.com/pytorch/audio/pull/2833:
- Moved model type from `_params` directly into the bundle definition. For now I defined model type as "WavLM" for WavLM bundles and "Wav2Vec2" for everything else. We can also distinguish between different Wav2Vec2 falvours - Hubert, VoxPopuli etc, but at the moment this won't imply any functional differences, so I didn't do it
- Expanded the title underline to match the title length

Pull Request resolved: https://github.com/pytorch/audio/pull/2895

Reviewed By: nateanl, mthrok

Differential Revision: D41799875

Pulled By: sgrigory

fbshipit-source-id: 0730d4f91ed60e900643bb74d6cccdd7aa5d7b39

Summary:
Part 1 of [T138011314](https://www.internalfb.com/intern/tasks/?t=138011314)

This PR ports the generator part of [HiFi GAN](https://arxiv.org/abs/2010.05646v2) from [the original implementation](https://github.com/jik876/hifi-gan/blob/4769534d45265d52a904b850da5a622601885777/models.py#L75)

Adds tests:
- Smoke tests for architectures V1, V2, V3
- Check that output shapes are correct
- Check that the model is torchscriptable and scripting doesn't change the output
- Check that our code's output matches the original implementation. Here I clone the original repo inside `/tmp` and import necessary objects from inside the test function.  On test teardown I restore `PATH`, but don't remove the cloned code, so that it can be reused on subsequent runs - let me know if removing it would be a better practice

There are no quantization tests, because the model consists mainly of `Conv1d` and `ConvTransposed1d`, and they are [not supported by dynamic quantization](https://pytorch.org/docs/stable/quantization.html)

Pull Request resolved: https://github.com/pytorch/audio/pull/2860

Reviewed By: nateanl

Differential Revision: D41433416

Pulled By: sgrigory

fbshipit-source-id: f135c560df20f5138f01e3efdd182621edabb4f5

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2889

Reviewed By: xiaohui-zhang

Differential Revision: D41760084

Pulled By: hwangjeff

fbshipit-source-id: d2f5253e1fae7e7aafa9fa6043c6a7045c5b33a0

Summary:
Introduces the MUSAN dataset (https://www.openslr.org/17/), which contains music, speech, and noise recordings.

Pull Request resolved: https://github.com/pytorch/audio/pull/2888

Reviewed By: xiaohui-zhang

Differential Revision: D41762164

Pulled By: hwangjeff

fbshipit-source-id: 14d5baaa4d40f065dd5d99bf7f2e0a73aa6c31a9

Summary:
This commit adds `frequency_impulse_response` function, which generates filter from desired frequency response.

[Example](https://output.circle-artifacts.com/output/job/5233fda9-dadb-4710-9389-7e8ac20a062f/artifacts/0/docs/tutorials/filter_design_tutorial.html#frequency-sampling)

Pull Request resolved: https://github.com/pytorch/audio/pull/2879

Reviewed By: hwangjeff

Differential Revision: D41767787

Pulled By: mthrok

fbshipit-source-id: 6d5e44c6390e8cf3028994a1b1de590ff3aaf6c2

Summary:
Adds pre-emphasis and de-emphasis functions.

Pull Request resolved: https://github.com/pytorch/audio/pull/2871

Reviewed By: carolineechen

Differential Revision: D41651097

Pulled By: hwangjeff

fbshipit-source-id: 7a3cf6ce68b6ce1b9ae315ddd8bd8ed71acccdf1

Summary:
Adds functions and transforms for speed and speed perturbation (https://www.isca-speech.org/archive/interspeech_2015/ko15_interspeech.html).

Pull Request resolved: https://github.com/pytorch/audio/pull/2829

Reviewed By: xiaohui-zhang

Differential Revision: D41285114

Pulled By: hwangjeff

fbshipit-source-id: 114740507698e01f35d4beb2c568a2479e847506

Summary:
This commit adds `sinc_impulse_response`, which generates windowed-sinc low-pass filters for given cutoff frequencies.

Example usage:
 - [Filter Design Tutorial](https://output.circle-artifacts.com/output/job/c0085baa-5345-4aeb-bd44-448034caa9e1/artifacts/0/docs/tutorials/filter_design_tutorial.html)

Pull Request resolved: https://github.com/pytorch/audio/pull/2875

Reviewed By: carolineechen

Differential Revision: D41586631

Pulled By: mthrok

fbshipit-source-id: a9991dbe5b137b0b4679228ec37072a1da7e50bb

Summary:
This commit adds the tutorial for additive synthesis, using torchaudio's prototype DSP ops.

[Review here](https://output.circle-artifacts.com/output/job/3dc83322-832a-4272-9c13-df752c97b660/artifacts/0/docs/tutorials/additive_synthesis_tutorial.html)

Pull Request resolved: https://github.com/pytorch/audio/pull/2877

Reviewed By: carolineechen

Differential Revision: D41585425

Pulled By: mthrok

fbshipit-source-id: b81283b90e4779c8054fd030a1d8c3d39d676bbd

Summary:
modeled after [paper](https://arxiv.org/pdf/2110.07313.pdf) and internal flow f288347302

internal comparison tests: D40080919

Pull Request resolved: https://github.com/pytorch/audio/pull/2827

Reviewed By: nateanl

Differential Revision: D41569046

Pulled By: carolineechen

fbshipit-source-id: 43c5313074af05972d93da55b2029c746b75c380

Summary:
This commits add tutorial for oscillator_bank and adsr_envelope, which will be a basis for DDSP.

 - [Review here](https://output.circle-artifacts.com/output/job/cf1d3001-88e5-418b-8cf8-ae22b4445dba/artifacts/0/docs/tutorials/oscillator_tutorial.html)

Pull Request resolved: https://github.com/pytorch/audio/pull/2862

Reviewed By: carolineechen

Differential Revision: D41559503

Pulled By: mthrok

fbshipit-source-id: 3f1689186db7d246de14f228fc2f91bf37db98cd

Summary:
Add `extend_pitch` function that can be used for augmenting fundamental frequencies with its harmonic overtones or inharmonic partials. it can be use for amplitude as well.

For example usages, see https://output.circle-artifacts.com/output/job/4ad0c29a-d75a-4244-baad-f5499f11d94b/artifacts/0/docs/tutorials/synthesis_tutorial.html

Part of https://github.com/pytorch/audio/issues/2835
Extracted from https://github.com/pytorch/audio/issues/2808

Pull Request resolved: https://github.com/pytorch/audio/pull/2863

Reviewed By: carolineechen

Differential Revision: D41543880

Pulled By: mthrok

fbshipit-source-id: 4f20e55770b0b3bee825ec07c73f9ec7cb181109

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2836

Reviewed By: carolineechen

Differential Revision: D41208630

Pulled By: nateanl

fbshipit-source-id: 625e1651f0b8a6e20876409739cf7084cb7c748b

Summary:
Add adsr_envelope op, which generates ADSR envelope

* Supports generation of the envelope on GPU
* Supports optional Hold
* Supports polynomial decay

<image src='https://download.pytorch.org/torchaudio/doc-assets/adsr_examples.png'>

Pull Request resolved: https://github.com/pytorch/audio/pull/2859

Reviewed By: nateanl

Differential Revision: D41379601

Pulled By: mthrok

fbshipit-source-id: 3717a6e0360d2a24913c2a836c57c5edec1d7b31

Summary:
This commit adds `oscillator_bank` op, which is the core of (differential) digital signal processing ops.
The implementation itself is pretty simple, sum instantaneous frequencies, take sin and multiply with amplitudes.

Following the magenta implementation, amplitudes for frequency range outside of [-Nyquist, Nyquist] \
are suppressed.

The differentiability is tested within frequency range of [- Nyquist, Nyquist], and amplitude range of [-5, 5], which should be enough.

For example usages:
 - https://output.circle-artifacts.com/output/job/129f3e21-41ce-406b-bc6b-833efb3c3141/artifacts/0/docs/tutorials/oscillator_tutorial.html
 - https://output.circle-artifacts.com/output/job/129f3e21-41ce-406b-bc6b-833efb3c3141/artifacts/0/docs/tutorials/synthesis_tutorial.html

Part of https://github.com/pytorch/audio/issues/2835
Extracted from https://github.com/pytorch/audio/issues/2808

Pull Request resolved: https://github.com/pytorch/audio/pull/2848

Reviewed By: carolineechen

Differential Revision: D41353075

Pulled By: mthrok

fbshipit-source-id: 80e60772fb555760f2396f7df40458803c280225

Summary:
Closes T136364380, follow-up to https://github.com/pytorch/audio/issues/2822

- Added "base", "base+", and "large" bundles for WavLM
- Expanded `wav2vec2_pipeline_test.py` to include the new bundles
- Added the new bundles to docs in `pipelines.rst`

Pull Request resolved: https://github.com/pytorch/audio/pull/2833

Reviewed By: nateanl

Differential Revision: D41194796

Pulled By: sgrigory

fbshipit-source-id: bf8e96c05b6a81ac5c5a014c46adeeac12685328

Summary:
* Add the new official torchaudio logo to documentation/README.
* Add a page for download logo.

https://output.circle-artifacts.com/output/job/e9eb1292-7c10-4fef-adc3-ad568802aa59/artifacts/0/docs/index.html

<img width="1068" alt="Screen Shot 2022-11-14 at 10 30 27 AM" src="https://user-images.githubusercontent.com/855818/201738349-9e248f15-dce2-4931-9066-aa898a53d6ad.png">

https://output.circle-artifacts.com/output/job/e9eb1292-7c10-4fef-adc3-ad568802aa59/artifacts/0/docs/logo.html

<img width="617" alt="Screen Shot 2022-11-14 at 10 30 47 AM" src="https://user-images.githubusercontent.com/855818/201738420-ad0fda2f-f310-4802-851c-bbdf6c84c045.png">

Pull Request resolved: https://github.com/pytorch/audio/pull/2802

Reviewed By: carolineechen

Differential Revision: D41295277

Pulled By: mthrok

fbshipit-source-id: 6615d00799c9611f875e8485459d800e350b3486

Summary:
follow up to https://github.com/pytorch/audio/issues/2823
- move bark spectrogram to prototype
- decrease autograd test tolerance (passing on circle ci)
- add diagram for bark fbanks

cc jdariasl

Pull Request resolved: https://github.com/pytorch/audio/pull/2843

Reviewed By: nateanl

Differential Revision: D41199522

Pulled By: carolineechen

fbshipit-source-id: 8e6c2e20fb7b14f39477683b3c6ed8356359a213

Summary:
I have added BarkScale transform, which can transform a regular Spectrogram into a BarkSpectrograms similar to MelScale. ahmed-fau opened this requirement in December 2021 with the number (https://github.com/pytorch/audio/issues/2103). The new functionality includes three different well-known approximations of the Bark scale.

Pull Request resolved: https://github.com/pytorch/audio/pull/2823

Reviewed By: nateanl

Differential Revision: D41162100

Pulled By: carolineechen

fbshipit-source-id: b2670c4972e49c9ef424da5d5982576f7a4df831

Summary:
internal comparison tests: D40080919

follow up PR for pretrained models https://github.com/pytorch/audio/issues/2827

Pull Request resolved: https://github.com/pytorch/audio/pull/2826

Reviewed By: nateanl

Differential Revision: D41160061

Pulled By: carolineechen

fbshipit-source-id: f3c478b28c235af53d1d8e21b573c53684a63ac4

Summary:
Closes T136364380

Added [WavLM Model](https://github.com/microsoft/UniSpeech/tree/main/WavLM):
- Added `WavLMSelfAttention` class (from [original implementation](https://github.com/microsoft/UniSpeech/blob/2e9dde8bf815a5f5fd958e3435e5641f59f96928/WavLM/modules.py)) and adjusted existing Encoder and Transformer classes to be compatible with it
- Added factory functions `wavlm_model`, `wavlm_base`, `wavlm_large` to `models/wav2vec2/model.py`
- Added bundles for base and large models to pipelines. **TODO**: pre-trained model weights are not yet uploaded to `download.pytorch.org`, permissions not granted yet.

## Tests
- Expanded HuggingFace integration tests to cover WavLM. For there tests, added JSON configs for base and large models from HF ([base](https://huggingface.co/microsoft/wavlm-base/blob/main/config.json), [large](https://huggingface.co/microsoft/wavlm-large/blob/main/config.json)) into test assets
- Expanded TorchScript and quantization tests to cover WavLM

## Comments
There are a few workarounds I had to introduce:
- Quantization tests for WavLM were breaking down at [`torch.cat`](https://github.com/pytorch/audio/pull/2822/files#diff-6f1486901c94320ec0610a460dc674638fab9d104a61564ff7b59353a8b8547cR466) ~~until I excluded the arguments of `torch.cat` from quantization [here](https://github.com/pytorch/audio/pull/2822/files#diff-6f1486901c94320ec0610a460dc674638fab9d104a61564ff7b59353a8b8547cR368-R369). I haven't found a better way to fix it, let me know if there is one~~ The reason for this seems to be that quantization replaces `.bias` and `.weight` attributes of a `Linear` module with methods. Since we are using weights and biases directly, the code was break. The final solution suggested by nateanl was to define attention weights and biases directly in `WavLMSelfAttention`, skipping the `Linear` layers
- ~~WavLM uses position embedding in the first layer of encoder, but not in the subsequent ones.  So [UniSpeech](https://github.com/microsoft/UniSpeech/blob/2e9dde8bf815a5f5fd958e3435e5641f59f96928/WavLM/modules.py#L342) and [HF](https://github.com/huggingface/transformers/blob/b047472650cba259621549ac27b18fd2066ce18e/src/transformers/models/wavlm/modeling_wavlm.py#L441-L442) implementations only create this embedding module in the layers where it's used. However, we can't do this here because it breaks TorchScript. So as a solution I add a dummy `Identity` module to `WavLMSelfAttention` when the actual embedding is not needed: [here](https://github.com/pytorch/audio/pull/2822/files#diff-6f1486901c94320ec0610a460dc674638fab9d104a61564ff7b59353a8b8547cR361-R368).~~ Thanks nateanl for resolving this!
- I had to add dummy `position_bias` and `key_padding_mask` arguments to `SelfAttention.forward` to make TorchScript tests pass. Since both `SelfAttention` and `WavLMSelfAttention` are called from `EncoderLayer`, they need to have compatible signatures. Having a variable number of arguments with `**kwargs` or checking object class doesn't seem to work with TorchScript, so I instead made both types of attention accept `position_bias` and `key_padding_mask` arguments.

Nit: do we still need to specify `__all__` if there are no wildcard imports in `__init__.py`, e.g. in `torchaudio/models/__init__.py`?

Pull Request resolved: https://github.com/pytorch/audio/pull/2822

Reviewed By: nateanl

Differential Revision: D41121855

Pulled By: sgrigory

fbshipit-source-id: 9f4f787e5810010de4e74cb704063a26c66767d7

Summary:
Adds `torch.nn.Module`-based implementations for convolution and FFT convolution.

Pull Request resolved: https://github.com/pytorch/audio/pull/2811

Reviewed By: carolineechen

Differential Revision: D40881937

Pulled By: hwangjeff

fbshipit-source-id: bfe8969e6178ad4f58981efd4b2720ac006be8de

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2825

Reviewed By: carolineechen

Differential Revision: D40954522

Pulled By: mthrok

fbshipit-source-id: 433fb856a74a340af4d49e5c65a6270f0b00c835

Summary:
PyTorch logo is included in pytorch doc theme, (and cannot be changed without custom CSS) so no need to have them here.

Pull Request resolved: https://github.com/pytorch/audio/pull/2824

Reviewed By: carolineechen

Differential Revision: D40954564

Pulled By: mthrok

fbshipit-source-id: 5e9a91fddcc92c141baf1996f721c09c037fb003

Summary:
<img width="756" alt="Screen Shot 2022-11-01 at 3 32 58 PM" src="https://user-images.githubusercontent.com/855818/199173348-f463ae71-438c-4dad-a481-b65522a8e52f.png">

Pull Request resolved: https://github.com/pytorch/audio/pull/2812

Reviewed By: carolineechen

Differential Revision: D40919942

Pulled By: mthrok

fbshipit-source-id: 18e5a709c262fb0b15ada0d303f1d0dee033beb1

Summary:
This commit re-organizes the tutorials.

1. Put all the tutorials in the left bar and make the section **folded by default**.
2. Add pytorch/tutorials-like cards in index
3. Move feature classifications to a dedicated page.

https://output.circle-artifacts.com/output/job/1f1a04a5-137e-428d-9da4-c46f59eeffa4/artifacts/0/docs/index.html

<img width="1073" alt="Screen Shot 2022-10-28 at 7 34 29 AM" src="https://user-images.githubusercontent.com/855818/198410686-3ef40ad2-c9c9-443c-800e-6e51e1b6a491.png">

Pull Request resolved: https://github.com/pytorch/audio/pull/2767

Reviewed By: carolineechen

Differential Revision: D40627547

Pulled By: mthrok

fbshipit-source-id: 098b825f242e91919126014abdab27852304ae64

Summary:
address https://github.com/pytorch/audio/issues/2780

Pull Request resolved: https://github.com/pytorch/audio/pull/2781

Reviewed By: carolineechen, mthrok

Differential Revision: D40556794

Pulled By: nateanl

fbshipit-source-id: b24912489d41e5663b4b4dcfb8be743fb962097e

Summary:
* Document `__call__` instead of `__init__`
* List CTCHypothesis first as it is used in combination with CTCDecoder
* Fix indentation of score method docstring

Pull Request resolved: https://github.com/pytorch/audio/pull/2766

Reviewed By: carolineechen

Differential Revision: D40349388

Pulled By: mthrok

fbshipit-source-id: 5e512e6c2b29d3533eb62d09b289154ccd1abf4c

Summary:
Adding and updating author information.

Pull Request resolved: https://github.com/pytorch/audio/pull/2764

Reviewed By: carolineechen

Differential Revision: D40332427

Pulled By: mthrok

fbshipit-source-id: 4f04c7351386c122e3b0a45c2ed1757a04b7dc9a

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2738

Reviewed By: carolineechen

Differential Revision: D40238099

Pulled By: nateanl

fbshipit-source-id: c5cc94c2a348a6ef34c04b8dd26114ecb874d73e

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2732

Reviewed By: nateanl

Differential Revision: D40186996

Pulled By: nateanl

fbshipit-source-id: a0ad325b7153c9e580dad2c515730dadbe8840c4

Summary:
* Add HW encoding to HW tutorial

https://colab.research.google.com/drive/1DDah_IaGULEO66CfQWltRqaVheBkiXdN#scrollTo=eXzKSVrHk1vS

Pull Request resolved: https://github.com/pytorch/audio/pull/2739

Reviewed By: hwangjeff

Differential Revision: D40197086

Pulled By: hwangjeff

fbshipit-source-id: 1780a5419f6705f7c24ba96bd46c3310438af7db