Recent Releases of gladia-torchaudio

gladia-torchaudio - TorchAudio 2.8.0 release

Copy pasting update from https://github.com/pytorch/audio/issues/3902#issuecomment-3160818888:

Deprecated APIs

Most APIs marked as "Drop" are now explicitly deprecated, raising deprecation warnings in the docs, and when using them from Python. They will be removed in the next 2.9 version.

Migration of `load()` and `save()` to TorchCodec

As we mentioned, we are consolidating the decoding and encoding capabilities of PyTorch in TorchCodec.

torchaudio.load() and torchaudio.save() are some of the most popular TorchAudio APIs, so for convenience we are providing torchaudio.load_with_torchcodec() and torchaudio.save_with_torchcodec(), which can largely be used as drop-in replacements. However, we do encourage users to directly migrate to TorchCodec's AudioDecoder() and AudioEncoder().

In future versions, torchaudio.load() and torchaudio.save() will still exist, but their underlying implementation will be relying on torchaudio.load_with_torchcodec() and torchaudio.save_with_torchcodec().

We hope for this migration to be as smooth as possible - most users should just need to pip install torchcodec, and things should still work as-is.

TorchCodec doesn't support Windows yet, but we're working hard on it. Please bear with us.

C++ and CUDA extension

We mentioned that we were exploring options to retain the C++-backed APIs, which are currently slated for deletion. Specifically: forced_align, lfilter, overdrive, RNNT, and CUCTC.

While this isn't something I can assert with 100% certainty, we are now more confident that we'll be able to preserve these extensions by porting them to Pytorch's new "stable ABI" operators. We are actively working on it.

- Python
Published by NicolasHug 7 months ago

gladia-torchaudio - TorchAudio 2.7.1 Release

This release is compatible with PyTorch 2.7.1 There are no new features added.

[!NOTE] We are in the process of refactoring TorchAudio and transitioning it into a maintenance phase. This process will include removing some user-facing features. Our main goals are to reduce redundancies with the rest of the PyTorch ecosystem, make it easier to maintain, and create a version of TorchAudio that is more tightly scoped to its strengths: processing audio data for ML. Please see our community message for more details.

- Python
Published by atalman 9 months ago

gladia-torchaudio - TorchAudio 2.7.0 Release

This release is compatible with PyTorch 2.7. There are no new features added.

[!NOTE] We are in the process of refactoring TorchAudio and transitioning it into a maintenance phase. This process will include removing some user-facing features. Our main goals are to reduce redundancies with the rest of the PyTorch ecosystem, make it easier to maintain, and create a version of TorchAudio that is more tightly scoped to its strengths: processing audio data for ML. Please see our community message for more details.

- Python
Published by NicolasHug 10 months ago

gladia-torchaudio - TorchAudio 2.6.0 Release

This release is compatible with PyTorch 2.6. There are no new features added.

The following fixes / improvement were made:

Fix incorrect audio trimming with negative index https://github.com/pytorch/audio/pull/3860
Fix vad return zero output when nonzero pretriggertime is requested https://github.com/pytorch/audio/pull/3866
ROCM compatibility improvements: https://github.com/pytorch/audio/pull/3840, https://github.com/pytorch/audio/pull/3843

- Python
Published by NicolasHug about 1 year ago

gladia-torchaudio - TorchAudio 2.5.0 Release

This release is compatible with PyTorch 2.5. There are no new features added.

This release contains one improvement:

reduce computations in backprop of lfilter https://github.com/pytorch/audio/pull/3831

- Python
Published by NicolasHug over 1 year ago

gladia-torchaudio - TorchAudio 2.4.1 Release

This release is compatible with PyTorch 2.4.1 patch release. There are no new features added.

- Python
Published by atalman over 1 year ago

gladia-torchaudio - TorchAudio 2.4.0 Release

This release is compatible with PyTorch 2.4. There are no new features added.

This release contains 2 fixes:

Fix view size error when backpropagating through lfilter https://github.com/pytorch/audio/pull/3794
[BC-Breaking] Fix model downloading in bento https://github.com/pytorch/audio/pull/3803

- Python
Published by NicolasHug over 1 year ago

gladia-torchaudio - TorchAudio 2.3.1 Release

This release is compatible with PyTorch 2.3.1 patch release. There are no new features added.

- Python
Published by atalman over 1 year ago

gladia-torchaudio - TorchAudio 2.3.0 Release

This release is compatible with PyTorch 2.3.0 patch release. There are no new features added.

This release contains minor documentation and code quality improvements (#3734, #3748, #3757, #3759)

- Python
Published by ahmadsharif1 almost 2 years ago

gladia-torchaudio - TorchAudio 2.2.2 Release

This release is compatible with PyTorch 2.2.2 patch release. There are no new features added.

- Python
Published by atalman almost 2 years ago

gladia-torchaudio - TorchAudio 2.2.1 Release

This release is compatible with PyTorch 2.2.1 patch release. There are no new features added.

- Python
Published by atalman about 2 years ago

gladia-torchaudio - TorchAudio 2.2.0 Release

New Features

Add path-like object support to StreamReader/Writer https://github.com/pytorch/audio/pull/3608
Introduce trio top-level module, dedicated for core I/O operations (https://github.com/pytorch/audio/pull/3676, https://github.com/pytorch/audio/pull/3680, https://github.com/pytorch/audio/pull/3681, https://github.com/pytorch/audio/pull/3682) Please refer to https://pytorch.org/audio/2.2.0/torio.html for the details.

Bug Fixes

https://github.com/pytorch/audio/pull/3685 Make F.vad return empty tensor for zero valued tensor input

Recipe Updates

https://github.com/pytorch/audio/pull/3631 Fix inconsistent naming

- Python
Published by mthrok about 2 years ago

gladia-torchaudio - TorchAudio 2.1.2 Release

This is a patch release, which is compatible with PyTorch 2.1.2. There are no new features added.

- Python
Published by huydhn about 2 years ago

gladia-torchaudio - v2.1.1

This is a minor release, which is compatible with PyTorch 2.1.1 and includes bug fixes, improvements and documentation updates.

Bug Fixes

Cherry-pick 2.1.1: Fix WavLM bundles (#3665)
Cherry-pick 2.1.1: Add back compression level in i/o dispatcher backend by (#3666)

- Python
Published by mthrok over 2 years ago

gladia-torchaudio - Torchaudio 2.1 Release Note

Hilights

TorchAudio v2.1 introduces the new features and backward-incompatible changes;

[BETA] A new API to apply filter, effects and codec
torchaudio.io.AudioEffector can apply filters, effects and encodings to waveforms in online/offline fashion.
You can use it as a form of augmentation.
Please refer to https://pytorch.org/audio/2.1/tutorials/effector_tutorial.html for the examples.
[BETA] Tools for forced alignment
New functions and a pre-trained model for forced alignment were added.
torchaudio.functional.forced_align computes alignment from an emission and torchaudio.pipelines.MMS_FA provides access to the model trained for multilingual forced alignment in MMS: Scaling Speech Technology to 1000+ languages project.
Please refer to https://pytorch.org/audio/2.1/tutorials/ctcforcedalignmentapitutorial.html for the usage of forced_align function, and https://pytorch.org/audio/2.1/tutorials/forcedalignmentformultilingualdatatutorial.html for how one can use `MMSFA` to align transcript in multiple languages.
[BETA] TorchAudio-Squim : Models for reference-free speech assessment
Model architectures and pre-trained models from the paper TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudio were added. You can use torchaudio.pipelines.SQUIM_SUBJECTIVE and torchaudio.pipelines.SQUIM_OBJECTIVE models to estimate the various speech quality and intelligibility metrics. This is helpful when evaluating the quality of speech generation models, such as TTS.
Please refer to https://pytorch.org/audio/2.1/tutorials/squim_tutorial.html for the detail.
[BETA] CUDA-based CTC decoder
torchaudio.models.decoder.CUCTCDecoder takes emission stored in CUDA memory and performs CTC beam search on it in CUDA device. The beam search is fast. It eliminates the need to move data from CUDA device to CPU when performing automatic speech recognition. With PyTorch's CUDA support, it is now possible to perform the entire speech recognition pipeline in CUDA.
Please refer to https://pytorch.org/audio/2.1/tutorials/asrinferencewithcudactcdecodertutorial.html for the detail.
[Prototype] Utilities for AI music generation
We are working to add utilities that are relevant to music AI. Since the last release, the following APIs were added to the prototype.
Please refer to respective documentation for the usage.
- torchaudio.prototype.chroma_filterbank
- torchaudio.prototype.transforms.ChromaScale
- torchaudio.prototype.transforms.ChromaSpectrogram
- torchaudio.prototype.pipelines.VGGISH
New recipes for training models. Recipes for Audio-visual ASR, multi-channel DNN beamforming and TCPGen context-biasing were added.
Please refer to the recipes
- https://github.com/pytorch/audio/tree/release/2.1/examples/avsr
- https://github.com/pytorch/audio/tree/release/2.1/examples/dnn_beamformer
- https://github.com/pytorch/audio/tree/release/2.1/examples/asr/librispeechconformerrnnt_biasing
Update to FFmpeg support The version of supported FFmpeg libraries was updated.
TorchAudio v2.1 works with FFmpeg 6, 5 and 4.4. The support for 4.3, 4.2 and 4.1 are dropped.
Please refer to https://pytorch.org/audio/2.1/installation.html#optional-dependencies for the detail of the new FFmpeg integration mechanism.
Update to libsox integration
TorchAudio now depends on libsox installed separately from torchaudio. Sox I/O backend no longer supports file-like object. (This is supported by FFmpeg backend and soundfile)
Please refer to https://pytorch.org/audio/2.1/installation.html#optional-dependencies for the detail.

New Features

I/O

Support overwriting PTS in torchaudio.io.StreamWriter (#3135)
Include format information after filter torchaudio.io.StreamReader.get_out_stream_info (#3155)
Support CUDA frame in torchaudio.io.StreamReader filter graph (#3183, #3479)
Support YUV444P in GPU decoder (#3199)
Add additional filter graph processing to torchaudio.io.StreamWriter (#3194)
Cache and reuse HW device context in GPU decoder (#3178)
Cache and reuse HW device context in GPU encoder (#3215)
Support changing the number of channels in torchaudio.io.StreamReader (#3216)
Support encode spec change in torchaudio.io.StreamWriter (#3207)
Support encode options such as compression rate and bit rate (#3179, #3203, #3224)
Add 420p10le support to torchaudio.io.StreamReader CPU decoder (#3332)
Support multiple FFmpeg versions (#3464, #3476)
Support writing opus and mp3 with soundfile (#3554)
Add switch to disable sox integration and ffmpeg integration at runtime (#3500)

Ops

Add torchaudio.io.AudioEffector (#3163, #3372, #3374)
Add torchaudio.transforms.SpecAugment (#3309, #3314)
Add torchaudio.functional.forced_align (#3348, #3355, #3533, #3536, #3354, #3365, #3433, #3357)
Add torchaudio.functional.merge_tokens (#3535, #3614)
Add torchaudio.functional.frechet_distance (#3545)

Models

Add torchaudio.models.SquimObjective for speech enhancement (#3042, 3087, #3512)
Add torchaudio.models.SquimSubjective for speech enhancement (#3189)
Add torchaudio.models.decoder.CUCTCDecoder (#3096)

Pipelines

Add torchaudio.pipelines.SquimObjectiveBundle for speech enhancement (#3103)
Add torchaudio.pipelines.SquimSubjectiveBundle for speech enhancement (#3197)
Add torchaudio.pipelines.MMS_FA Bundle for forced alignment (#3521, #3538)

Tutorials

Add tutorial for torchaudio.io.AudioEffector (#3226)
Add tutorials for CTC forced alignment API (#3356, #3443, #3529, #3534, #3542, #3546, #3566)
Add tutorial for torchaudio.models.decoder.CUCTCDecoder (#3297)
Add tutorial for real-time av-asr (#3511)
Add tutorial for TorchAudio-SQUIM pipelines (#3279, #3313)
Split HW acceleration tutorial into nvdec/nvenc tutorials (#3483, #3478)

Recipe

Add TCPGen context-biasing Conformer RNN-T (#2890)
Add AV-ASR recipe (#3278, #3421, #3441, #3489, #3493, #3498, #3492, #3532)
Add multi-channel DNN beamforming training recipe (#3036)

Backward-incompatible changes

Third-party libraries

In this release, the following third party libraries are removed from TorchAudio binary distributions. TorchAudio now search and link these libraries at runtime. Please install them to use the corresponding APIs.

SoX

libsox is used for various audio I/O, filtering operations.

Pre-built binaries are avaialble via package managers, such as conda, apt and brew. Please refer to the respective documetation.

The APIs affected include;

torchaudio.load ("sox" backend)
torchaudio.info ("sox" backend)
torchaudio.save ("sox" backend)
torchaudio.sox_effects.apply_effects_tensor
torchaudio.sox_effects.apply_effects_file
torchaudio.functional.apply_codec (also deprecated, see below)

Changes related to the removal: #3232, #3246, #3497, #3035

Flashlight Text

flashlight-text is the core of CTC decoder.

Pre-built packages are available on PyPI. Please refer to https://github.com/flashlight/text for the detail.

The APIs affected include;

torchaudio.models.decoder.CTCDecoder

Changes related to the removal: #3232, #3246, #3236, #3339

Kaldi

A custom built libkaldi was used to implement torchaudio.functional.compute_kaldi_pitch. This function, along with libkaldi integration, is removed in this release. There is no replcement.

Changes related to the removal: #3368, #3403

I/O

Switch to the backend dispatcher (#3241)

To make I/O operations more flexible, TorchAudio introduced the backend dispatcher in v2.0, and users could opt-in to use the dispatcher. In this release, the backend dispatcher becomes the default mechanism for selecting the I/O backend.

You can pass backend argument to torchaudio.info, torchaudio.load and torchaudio.save function to select I/O backend library per-call basis. (If it is omitted, an available backend is automatically selected.)

If you want to use the global backend mechanism, you can set the environment variable, TORCHAUDIO_USE_BACKEND_DISPATCHER=0. Please note, however, that this the global backend mechanism is deprecated and is going to be removed in the next release.

Please see #2950 for the detail of migration work.

Remove Tensor binding from StreamReader (#3093, #3272)

torchaudio.io.StreamReader accepted a byte-string wrapped in 1D torch.Tensor object. This is no longer supported. Please wrap the underlying data with io.BytesIO instead.

Make I/O optional arguments kw-only (#3208, #3227)

The optional arguments of add_[audio|video]_stream methods of torchaudio.io.StreamReader and torchaudio.io.StreamWriter are now keyword-only arguments.

Drop the support of FFmpeg < 4.1 (#3561, 3557)

Previously TorchAudio supported FFmpeg 4 (>=4.1, <=4.4). In this release, TorchAudio supports FFmpeg 4, 5 and 6 (>=4.4, <7). With this change, support for FFmpeg 4.1, 4.2 and 4.3 are dropped.

Ops

Use named file in torchaudio.functional.apply_codec (#3397)

In previous versions, TorchAudio shipped custom built libsox, so that it can perform in-memory decoding and encoding. Now, in-memory decoding and encoding are handled by FFmpeg binding, and with the switch to dynamic libsox linking, torchaudio.functional.apply_codec no longer process audio in in-memory fashion. Instead it writes to temporary file. For in-memory processing, please use torchaudio.io.AudioEffector.

Switch to lstsq when solving InverseMelScale (#3280)

Previously, torchaudio.transform.InverseMelScale ran SGD optimizer to find the inverse of mel-scale transform. This approach has number of issues as listed in #2643.

This release switches to use torch.linalg.lstsq.

Models

Improve RNN-T streaming decoding (#3295, #3379)

The infer method of torchaudio.models.RNNTBeamSearch has been updated to accept series of previous hypotheses.

```python

bundle = torchaudio.pipelines.EMFORMERRNNTBASELIBRISPEECH decoder: RNNTBeamSearch = bundle.getdecoder()

hypothesis = None while streaming: ... hypo, state = decoder.infer( features, length, beam_width, state=state, hypothesis=hypothesis, ) ... hypothesis = hypo # Previously this had to be hypothesis = hypo[0] ```

Deprecations

Ops

Update and deprecate torchaudio.functional.apply_codec function (#3386)

Due to the removal of custom libsox binding, torchaudio.functional.apply_codec no longer supports in-memory processing. Please migrate to torchaudio.io.AudioEffector.

Please refer to for the detailed usage of torchaudio.io.AudioEffector.

https://pytorch.org/audio/2.1/generated/torchaudio.io.AudioEffector.html
https://pytorch.org/audio/stable/tutorials/effector_tutorial.html

Bug Fixes

Models

Fix the negative sampling in ConformerWav2Vec2PretrainModel (#3085)
Fix extract_features method for WavLM models (#3350)

Tutorials

Fix backtracking in forced alignment tutorial (#3440)
Fix initialization of get_trellis in forced alignment tutorial (#3172)

Build

Fix MKL issue on Intel mac build (#3307)

I/O

Surpress warning when saving vorbis with sox backend (#3359)
Fix g722 encoding in torchaudio.io.StreamWriter (#3373)
Refactor arg mapping in ffmpeg save function (#3387)
Fix save INT16 sox backend (#3524)
Fix SoundfileBackend method decorators (#3550)
Fix PTS initialization when using NVIDIA encoder (#3312)

Ops

Add non-default CUDA device support to lfilter (#3432)

Improvements

I/O

Set "experimental" automatically when using native opus/vorbis encoder (#3192)
Improve the performance of NV12 frame conversion (#3344)
Improve the performance of YUV420P frame conversion (#3342)
Refactor backend implementations (#3547, #3548, #3549)
Raise an error if torchaudio.io.StreamWriter is not opened (#3152)
Warn if decoding YUV images with different plane size (#3201)
Expose AudioMetadata (#3556)
Refactor the internal of torchaudio.io.StreamReader (#3157, #3170, #3186, #3184, #3188, #3320, #3296, #3328, #3419, #3209)
Refactor the internal of torchaudio.io.StreamWriter (#3205, #3319, #3296, #3328, #3426, #3428)
Refactor the FFmpeg abstraction layer (#3249, #3251)
Migrate the binding of FFmpeg utils to PyBind11 (#3228)
Simplify sox namespace (#3383)
Use const reference in sox implementation (#3389)
Ensure StreamReader returns tensors with requires_grad is False (#3467)
Set the default #threads to 1 in StreamWriter (#3370)
Remove ffmpeg fallback from sox_io backend (#3516)

Ops

Add arbitrary dim Tensor support to maskalongaxis{,_iid} (#3289)
Fix resampling to support dynamic input lengths for onnx exports. (#3473)
Optimize Torchaudio Vad (#3382)

Documentation

Build and use GPU-enabled FFmpeg in doc CI (#3045)
Misc tutorial update (#3449)
Update notes on FFmpeg version (#3480)
Update documentation about dependencies (#3517)
Update I/O and backend docs (#3555)

Tutorials

Update data augmentation tutorial (#3375)
Add more explanation about n_fft (#3442)

Build

Resolve some compilation warnings (#3471)
Use pre-built binaries for ffmpeg extension (#3460)
Add aarch64 workflow (#3553)
Add CUDA 12.1 builds (#3284)
Update CUDA to 12.1 U1 (#3563)

Recipe

Fix Adam and AdamW initializers in wav2letter example (#3145)
Update Librispeech RNNT recipe to support Lightening 2.0 (#3336)
Update HuBERT/SSL training recipes to support Lightning 2.x (#3396)
Add wav2vec2 loss function in selfsupervisedlearning training recipe (#3090)
Add Wav2Vec2DataModule in selfsupervisedlearning training recipe (#3081)

Other

Use FFmpeg6 in build doc (#3475)
Use FFmpeg6 in unit test (#3570)
Migrate torch.norm to torch.linalg.vector_norm (#3522)
Migrate torch.nn.utils.weight_norm to nn.utils.parametrizations.weight_norm (#3523)

- Python
Published by mthrok over 2 years ago

gladia-torchaudio - v2.0.2

TorchAudio 2.0.2 Release Note

This is a minor release, which is compatible with PyTorch 2.0.1 and includes bug fixes, improvements and documentation updates. There is no new feature added.

Bug fix

#3239 Properly set #samples passed to encoder (#3204)
#3238 Fix virtual function issue with CTC decoder (#3230)
#3245 Fix path-like object support in FFmpeg dispatcher (#3243, #3248)
#3261 Use scaleddotproduct_attention in Wav2vec2/HuBERT's SelfAttention (#3253)
#3264 Use scaleddotproduct_attention in WavLM attention (#3252, #3265)

Full Changelog: https://github.com/pytorch/audio/compare/v2.0.1...v2.0.2

- Python
Published by mthrok almost 3 years ago

gladia-torchaudio - Torchaudio 2.0 Release Note

## Highlights TorchAudio 2.0 release includes: - Data augmentation operators, e.g. convolution, additive noise, speed perturbation - WavLM and XLS-R models and pre-trained pipelines - Backend dispatcher powering revised info, load, save functions - Dropped support of Python 3.7 - Added Python 3.11 support

[Beta] Data augmentation operators

The release adds several data augmentation operators under torchaudio.functional and torchaudio.transforms: - torchaudio.functional.add_noise - torchaudio.functional.convolve - torchaudio.functional.deemphasis - torchaudio.functional.fftconvolve - torchaudio.functional.preemphasis - torchaudio.functional.speed - torchaudio.transforms.AddNoise - torchaudio.transforms.Convolve - torchaudio.transforms.Deemphasis - torchaudio.transforms.FFTConvolve - torchaudio.transforms.Preemphasis - torchaudio.transforms.Speed - torchaudio.transforms.SpeedPerturbation

The operators can be used to synthetically diversify training data to improve the generalizability of downstream models.

For usage details, please refer to the documentation for torchaudio.functional and torchaudio.transforms, and tutorial “Audio Data Augmentation”.

[Beta] WavLM and XLS-R models and pre-trained pipelines

The release adds two self-supervised learning models for speech and audio. - WavLM that is robust to noise and reverberation. - XLS-R that is trained on cross-lingual datasets.

Besides the model architectures, torchaudio also supports corresponding pre-trained pipelines: - torchaudio.pipelines.WAVLM_BASE - torchaudio.pipelines.WAVLM_BASE_PLUS - torchaudio.pipelines.WAVLM_LARGE - torchaudio.pipelines.WAV2VEC_XLSR_300M - torchaudio.pipelines.WAV2VEC_XLSR_1B - torchaudio.pipelines.WAV2VEC_XLSR_2B

For usage details, please refer to factory function and pre-trained pipelines documentation.

Backend dispatcher

Release 2.0 introduces new versions of I/O functions torchaudio.info, torchaudio.load and torchaudio.save, backed by a dispatcher that allows for selecting one of backends FFmpeg, SoX, and SoundFile to use, subject to library availability. Users can enable the new logic in Release 2.0 by setting the environment variable TORCHAUDIO_USE_BACKEND_DISPATCHER=1; the new logic will be enabled by default in Release 2.1.

```python

Fetch metadata using FFmpeg

metadata = torchaudio.info("test.wav", backend="ffmpeg")

Load audio (with no backend parameter value provided, function prioritizes using FFmpeg if it is available)

waveform, rate = torchaudio.load("test.wav")

Write audio using SoX

torchaudio.save("out.wav", waveform, rate, backend="sox") ```

Please see the documentation for torchaudio for more details.

Backward-incompatible changes

Dropped Python 3.7 support (#3020) Following the upstream PyTorch (https://github.com/pytorch/pytorch/pull/93155), the support for Python 3.7 has been dropped.
Default to "precise" seek in torchaudio.io.StreamReader.seek (#2737, #2841, #2915, #2916, #2970) Previously, the StreamReader.seek method seeked into a key frame closest to the given time stamp. A new option mode has been added which can switch the behavior to seeking into any type of frame, including non-key frames, that is closest to the given timestamp, and this behavior is now default.
Removed deprecated/unused/undocumented functions from datasets.utils (#2926, #2927) The following functions are removed from datasets.utils
- stream_url
- download_url
- validate_file
- extract_archive.

Deprecations

Ops

Deprecated 'onesided' init param for MelSpectrogram (#2797, #2799) torchaudio.transforms.MelSpectrogram assumes the onesided argument to be always True. The forward path fails if its value is False. Therefore this argument is deprecated. Users specifying this argument should stop specifying it.
Deprecated "sinc_interpolation" and "kaiser_window" option value in favor of "sinc_interp_hann" and "sinc_interp_kaiser" (#2922) The valid values of resampling_method argument of resampling operations (torchaudio.transforms.Resample and torchaudio.functional.resample) are changed. "kaiser_window" is now "sinc_interp_kaiser" and "sinc_interpolation" is "sinc_interp_hann". The old values will continue to work, but users are encouraged to update their code. For the reason behind of this change, please refer #2891.
Deprecated sox initialization/shutdown public API functions (#3010) torchaudio.sox_effects.init_sox_effects and torchaudio.sox_effects.shutdown_sox_effects are deprecated. They were required to use libsox-related features, but are called automatically since v0.6, and the initialization/shutdown mechanism have been moved elsewhere. These functions are now no-op. Users can simply remove the call to these functions.

Models

Deprecated static binding of Flashlight-text based CTC decoder (#3055, #3089) Since v0.12, TorchAudio binary distributions included the CTC decoder based on flashlight-text project. In a future release, TorchAudio will switch to dynamic binding of underlying CTC decoder implementation, and stop shipping the core CTC decoder implementations. Users who would like to use the CTC decoder need to separately install the CTC decoder from the upstream flashlight-text project. Other functionalities of TorchAudio will continue to work without flashlight-text. Note: The API and numerical behavior does not change. For more detail, please refer #3088.

I/O

Deprecated file-like object support in soxio (#3033) As a preparation to switch to dynamically bound libsox, file-like object support in soxio backend has been deprecated. It will be removed in 2.1 release in favor of the dispatcher. This deprecation affects the following functionalities.
- I/O: torchaudio.load, torchaudio.info and torchaudio.save.
- Effects: torchaudio.sox_effects.apply_effects_file and torchaudio.functional.apply_codec. For I/O, to continue using file-like objects, please use the new dispatcher mechanism. For effects, replacement functions will be added in the next release.

Deprecated the use of Tensor as a container for byte string in StreamReader (#3086) torchaudio.io.StreamReader supports decoding media from byte strings contained in 1D tensors of torch.uint8 type. Using torch.Tensor type as a container for byte string is now deprecated. To pass byte strings, please wrap the string with io.BytesIO.

Deprecated	Migration
`data = b"..."` `src = torch.frombuffer(data, dtype=torch.uint8)` `StreamReader(src)`	`data = b"..."` `src = io.BytesIO(data)` `StreamReader(src)`

Bug Fixes

Ops

Fixed contiguous error when backpropagating through torchaudio.functional.lfilter (#3080)

Pipelines

Added layer normalization to wav2vec2 large+ pretrained models (#2873) In self-supervised learning models such as Wav2Vec 2.0, HuBERT, or WavLM, layer normalization should be applied to waveforms if the convolutional feature extraction module uses layer normalization and is trained on a large-scale dataset. After adding layer normalization to those affected models, the Word Error Rate is significantly reduced.

Without the change in #2873, the WER results are: | Model | dev-clean | dev-other | test-clean | test-other | |:------------------------------------------------------------------------------------------------|-----------:|-----------:|-----------:|-----------:| | WAV2VEC2ASRLARGELV60K10M | 10.59| 15.62| 9.58| 16.33| | WAV2VEC2ASRLARGELV60K100H | 2.80| 6.01| 2.82| 6.34| | WAV2VEC2ASRLARGELV60K960H | 2.36| 4.43| 2.41| 4.96| | HUBERTASRLARGE | 1.85| 3.46| 2.09| 3.89| | HUBERTASRXLARGE | 2.21| 3.40| 2.26| 4.05|

After applying layer normalization, the updated WER results are: | Model | dev-clean | dev-other | test-clean | test-other | |:------------------------------------------------------------------------------------------------|-----------:|-----------:|-----------:|-----------:| | WAV2VEC2ASRLARGELV60K10M | 6.77| 10.03| 6.87| 10.51| | WAV2VEC2ASRLARGELV60K100H | 2.19| 4.55| 2.32| 4.64| | WAV2VEC2ASRLARGELV60K960H | 1.78| 3.51| 2.03| 3.68| | HUBERTASRLARGE | 1.77| 3.32| 2.03| 3.68| | HUBERTASRXLARGE | 1.73| 2.72| 1.90| 3.16|

Recipe

Fixed DDP training in HuBERT recipes (#3068) If shuffle is set True in BucketizeBatchSampler, the seed is only the same for the first epoch. In later epochs, each BucketizeBatchSampler object will generate a different shuffled iteration list, which may cause DPP training to hang forever if the lengths of iteration lists are different across nodes. In the 2.0.0 release, the issue is fixed by using the same seed for RNG in all nodes.

IO

Fixed signature mismatch on _fail_info_fileobj (#3032)
Remove unnecessary AVFrame allocation (#3021) This fixes the memory leak reported in torchaudio.io.StreamReader. ## New Features ### Ops
Added CUDA kernel for torchaudio.functional.lfilter (#3018)
Added data augmentation ops (#2801, #2809, #2829, #2811, #2871, #2874, #2892, #2935, #2977, #3001, #3009, #3061, #3072) Introduces AddNoise, Convolve, FFTConvolve, Speed, SpeedPerturbation, Deemphasis, and Preemphasis in torchaudio.transforms, and add_noise, fftconvolve, convolve, speed, preemphasis, and deemphasis in torchaudio.functional. ### Models
Added WavLM model (#2822, #2842)
Added XLS-R models (#2959)

Pipelines

Added WavLM bundles (#2833, #2895)
Added pre-trained pipelines for XLS-R models (#2978)

I/O

Added rgb48le and CUDA p010 support (HDR/10bit) to StreamReader (#3023)
Added fill_buffer method to torchaudio.io.StreamReader (#2954, #2971)
Added buffer_chunk_size=-1 option to torchaudio.io.StreamReader (#2969) When buffer_chunk_size=-1, StreamReader does not drop any buffered frame. Together with the fill_buffer method, this is a recommended way to load the entire media. python reader = StreamReader("video.mp4") reader.add_basic_audio_stream(buffer_chunk_size=-1) reader.add_basic_video_stream(buffer_chunk_size=-1) reader.fill_buffer() audio, video = reader.pop_chunks()
Added PTS support to torchaudio.io.StreamReader (#2975) torchaudio.io.SteramReader now gives PTS (presentation time stamp) of the media chunk it is returning. To maintain backward compatibility, the timestamp information is attached to the returned media chunk. python reader = StreamReader(...) reader.add_basic_audio_stream(...) reader.add_basic_video_stream(...) for audio_chunk, video_chunk in reader.stream(): # Fetch timestamp print(audio_chunk.pts) print(video_chunk.pts) # Chunks behave the same as torch.Tensor. audio_chunk.mean(dim=1)
Added playback function torchaudio.io.play_audio (#3026, #3051) You can play audio with the torchaudio.io.play_audio function. (macOS only)
Added new dispatcher (#3015, #3058, #3073)

Other

Add utility functions to check information about FFmpeg (#2958, #3014) The following functions are added to torchaudio.utils.ffmpeg_utils, which can be used to query into the dynamically linked FFmpeg libraries.
- get_demuxers()
- get_muxers()
- get_audio_decoders()
- get_audio_encoders()
- get_video_decoders()
- get_video_encoders()
- get_input_devices()
- get_output_devices()
- get_input_protocols()
- get_output_protocols()
- get_build_config()

Recipes

Add modularized SSL training recipe (#2876) ## Improvements ### I/O
Refactor StreamReader/Writer implementation
- Refactored StreamProcessor interface (#2791)
- Refactored Buffer implementation (#2939, #2943, #2962, #2984, #2988)
- Refactored AVFrame to Tensor conversions (#2940, #2946)
- Refactored and optimize yuv420p and nv12 processing (#2945)
- Abstracted away AVFormatContext from constructor (#3007)
- Removed unused/redundant things (#2995)
- Replaced torchaudio::ffmpeg namespace with torchaudio::io (#3013)
- Merged pop_chunks implementations (#3002)
- Cleaned up private methods (#3030)
- Moved drain method to private (#2996)
Added logging to torchaudio.io.StreamReader/Writer (#2878)
Fixed the #threads used by FilterGraph to 1 (#2985)
Fixed the default #threads used by decoder to 1 in torchaudio.io.StreamReader (#2949)
Moved libsox integration from libtorchaudio to libtorchaudio_sox (#2929)
Added query methods to FilterGraph (#2976)

Ops

Added logging to MelSpectrogram and Spectrogram (#2861)
Fixed filtering function fallback mechanism (#2953)
Enabled log probs input for RNN-T loss (#2798)
Refactored extension modules initialization (#2968)
Updated the guard mechanism for FFmpeg-related features (#3028)
Updated the guard mechanism for cuda_version (#2952)

Models

Renamed generator to vocoder in HiFiGAN model and factory functions (#2955)
Enforces contiguous tensor in CTC decoder (#3074)

Datasets

Validates the input path in LibriMix dataset (#2944)

Documentation

Fixed docs warnings for conformer w2v2 (#2900)
Updated model documentation structure (#2902)
Fixed document for MelScale and InverseMelScale (#2967)
Updated highlighting in doc (#3000)
Added installation / build instruction to doc (#3038)
Redirect build instruction to official doc (#3053)
Tweak docs around IO (#3064)
Improved docstring about input path to LibriMix (#2937)

Recipes

Simplify train step in Conformer RNN-T LibriSpeech recipe (#2981)
Update WER results for CTC n-gram decoding (#3070)
Update ssl example (#3060)
fix import bug in global_stats.py (#2858)
Fixes examples/sourceseparation for WSJ02mix dataset (#2987)

Tutorials

Added mel spectrogram visualization to Streaming ASR tutorial (#2974)
Fixed mel spectrogram visualization in TTS tutorial (#2989)
Updated data augmentation tutorial to use new operators (#3062)
Fixed hybrid demucs tutorial for CUDA (#3017)
Updated hardware accelerated video processing tutorial (#3050) ### Builds
Fixed USE_CUDA detection (#3005)
Fixed USE_ROCM detection (#3008)
Added M1 Conda builds (#2840)
Added M1 Wheels builds (#2839)
Added CUDA 11.8 builds (#2951)
Switched CI to CUDA 11.7 from CUDA 11.6 (#3031, #3034)
Added python 3.11 support (#3039, #3071)
Updated C++ standard to 17 (#2973)

Tests

Fix integration test for WAV2VEC2ASRLARGELV60K10M (#2910)
Fix CI tests on gpu machines (#2982)
Remove function input parameters from data aug functional tests (#3011)
Reduce the sample rate of some tests (#2963)

Style

Fix type of arguments in torchaudio.io classes (#2913)

- Python
Published by xiaohui-zhang almost 3 years ago

gladia-torchaudio - TorchAudio 0.13.1 Release Note

This is a minor release, which is compatible with PyTorch 1.13.1 and includes bug fixes, improvements and documentation updates. There is no new feature added.

Bug Fix

IO

Make buffer size configurable in ffmpeg file object operations and set size in backend (#2810)
Fix issue with the missing video frame in StreamWriter (#2789)
Fix decimal FPS handling StreamWriter (#2831)
Fix wrong frame allocation in StreamWriter (#2905)
Fix duplicated memory allocation in StreamWriter (#2906) ## Model
Fix HuBERT model initialization (#2846, #2886) ## Recipe
Fix issues in HuBERT fine-tuning recipe (#2851)
Fix automatic mixed precision in HuBERT pre-training recipe (#2854)

- Python
Published by mthrok about 3 years ago

gladia-torchaudio - torchaudio 0.13.0 Release Note

Highlights

TorchAudio 0.13.0 release includes: - Source separation models and pre-trained bundles (Hybrid Demucs, ConvTasNet) - New datasets and metadata mode for the SUPERB benchmark - Custom language model support for CTC beam search decoding - StreamWriter for audio and video encoding

[Beta] Source Separation Models and Bundles

Hybrid Demucs is a music source separation model that uses both spectrogram and time domain features. It has demonstrated state-of-the-art performance in the Sony Music DeMixing Challenge. (citation: https://arxiv.org/abs/2111.03600)

The TorchAudio v0.13 release includes the following features * MUSDB_HQ Dataset, which is used in Hybrid Demucs training (docs) * Hybrid Demucs model architecture (docs) * Three factory functions suitable for different sample rate ranges * Pre-trained pipelines (docs) and tutorial

SDR Results of pre-trained pipelines on MUSDB-HQ test set | Pipeline | All | Drums | Bass | Other | Vocals | | ----- | ----- | ----- | ----- | ----- | ----- | | HDEMUCSHIGHMUSDB* | 6.42 | 7.76 | 6.51 | 4.47 | 6.93 | | HDEMUCSHIGHMUSDB_PLUS** | 9.37 | 11.38 | 10.53 | 7.24 | 8.32 |

* Trained on the training data of MUSDB-HQ dataset. ** Trained on both training and test sets of MUSDB-HQ and 150 extra songs from an internal database that were specifically produced for Meta.

Special thanks to @adefossez for the guidance.

ConvTasNet model architecture was added in TorchAudio 0.7.0. It is the first source separation model that outperforms the oracle ideal ratio mask. In this release, TorchAudio adds the pre-trained pipeline that is trained within TorchAudio on the Libri2Mix dataset. The pipeline achieves 15.6dB SDR improvement and 15.3dB Si-SNR improvement on the Libri2Mix test set.

[Beta] Datasets and Metadata Mode for SUPERB Benchmarks

With the addition of four new audio-related datasets, there is now support for all downstream tasks in version 1 of the SUPERB benchmark. Furthermore, these datasets support metadata mode through a get_metadata function, which enables faster dataset iteration or preprocessing without the need to load or store waveforms.

Datasets with metadata functionality: - LIBRISPEECH (docs) - LibriMix (docs) - QUESST14 (docs) - SPEECHCOMMANDS (docs) - (new) FluentSpeechCommands (docs) - (new) Snips (docs) - (new) IEMOCAP (docs) - (new) VoxCeleb1 (Identification, Verification)

[Beta] Custom Language Model support in CTC Beam Search Decoding

In release 0.12, TorchAudio released a CTC beam search decoder with KenLM language model support. This release, there is added functionality for creating custom Python language models that are compatible with the decoder, using the torchaudio.models.decoder.CTCDecoderLM wrapper.

[Beta] StreamWriter

torchaudio.io.StreamWriter is a class for encoding media including audio and video. This can handle a wide variety of codecs, chunk-by-chunk encoding and GPU encoding.

Backward-incompatible changes

[BC-breaking] Fix momentum in transforms.GriffinLim (#2568) The GriffinLim implementations in transforms and functional used the momentum parameter differently, resulting in inconsistent results between the two implementations. The transforms.GriffinLim usage of momentum is updated to resolve this discrepancy.
Make torchaudio.info decode audio to compute num_frames if it is not found in metadata (#2740). In such cases, torchaudio.info may now return non-zero values for num_frames. ## Bug Fixes
Fix random Gaussian generation (#2639) torchaudio.compliance.kaldi.fbank with dither option produced a different output from kaldi because it used a skewed, rather than gaussian, distribution for dither. This is updated in this release to correctly use a random gaussian instead.
Update download link for speech commands (#2777) The previous download link for SpeechCommands v2 did not include data for the valid and test sets, resulting in errors when trying to use those subsets. Update the download link to correctly download the whole dataset. ## New Features ### IO
Add metadata to source stream info (#2461, #2464)
Add utility function to fetch FFmpeg library versions (#2467)
Add YUV444P support to StreamReader (#2516)
Add StreamWriter (#2628, #2648, #2505)
Support in-memory decoding via Tensor wrapper in StreamReader (#2694)
Add StreamReader Tensor Binding to src (#2699)
Add StreamWriter media device/streaming tutorial (#2708)
Add StreamWriter tutorial (#2698)

Ops

Add ITU-R BS.1770-4 loudness recommendation (#2472)
Add convolution operator (#2602)
Add additive noise function (#2608)

Models

Hybrid Demucs model implementation (#2506)
Docstring change for Hybrid Demucs (#2542, #2570)
Add NNLM support to CTC Decoder (#2528, #2658)
Move hybrid demucs model out of prototype (#2668)
Move convtasnetbase doc out of prototype (#2675)
Add custom lm example to decoder tutorial (#2762)

Pipelines

Add SourceSeparationBundle to prototype (#2440, #2559)
Adding pipeline changes, factory functions to HDemucs (#2547, #2565)
Create tutorial for HDemucs (#2572)
Add HDEMUCSHIGHMUSDB (#2601)
Move SourceSeparationBundle and pre-trained ConvTasNet pipeline into Beta (#2669)
Move Hybrid Demucs pipeline to beta (#2673)
Update description of HDemucs pipelines

Datasets

Add fluent speech commands (#2480, #2510)
Add musdb dataset and tests (#2484)
Add VoxCeleb1 dataset (#2349)
Add metadata function for LibriSpeech (#2653)
Add Speech Commands metadata function (#2687)
Add metadata mode for various datasets (#2697)
Add IEMOCAP dataset (#2732)
Add Snips Dataset (#2738)
Add metadata for Librimix (#2751)
Add file name to returned item in Snips dataset (#2775)
Update IEMOCAP variants and labels (#2778)

Improvements

IO

Replace runtime_error exception with TORCH_CHECK (#2550, #2551, #2592)
Refactor StreamReader (#2507, #2508, #2512, #2530, #2531, #2533, #2534)
Refactor sox C++ (#2636, #2663)
Delay the import of kaldi_io (#2573)

Ops

Speed up resample with kernel generation modification (#2553, #2561) The kernel generation for resampling is optimized in this release. The following table illustrates the performance improvements from the previous release for the torchaudio.functional.resample function using the sinc resampling method, on float32 tensor with two channels and one second duration.

CPU | torchaudio version | 8k → 16k [Hz] | 16k → 8k | 16k → 44.1k | 44.1k → 16k | | ----- | ----- | ----- | ----- | ----- | | 0.13 | 0.256 | 0.549 | 0.769 | 0.820 | | 0.12 | 0.386 | 0.534 | 31.8 | 12.1 |

CUDA | torchaudio version | 8k → 16k [Hz] | 16k → 8k | 16k → 44.1k | 44.1k → 16k | | ----- | ----- | ----- | ----- | ----- | | 0.13 | 0.332 | 0.336 | 0.345 | 0.381 | | 0.12 | 0.524 | 0.334 | 64.4 | 22.8 |

Add normalization parameter on spectrogram and inverse spectrogram (#2554)
Replace assert with raise for ops (#2579, #2599)
Replace CHECK_ by TORCHCHECK (#2582)
Fix argument validation in TorchAudio filtering (#2609)

Models

Switch to flashlight decoder from upstream (#2557)
Add dimension and shape check (#2563)
Replace assert with raise in models (#2578, #2590)
Migrate CTC decoder code (#2580)
Enable CTC decoder in Windows (#2587)

Datasets

Replace assert with raise in datasets (#2571)
Add unit test for LibriMix dataset (#2659)
Add gtzan download note (#2763)

Tutorials

Tweak tutorials (#2630, #2733)
Update ASR inference tutorial (#2631)
Update and fix tutorials (#2661, #2701)
Introduce IO section to getting started tutorials (#2703)
Update HW video processing tutorial (#2739)
Update tutorial author information (#2764)
Fix typos in tacotron2 tutorial (#2761)
Fix fading in hybrid demucs tutorial (#2771)
Fix leaking matplotlib figure (#2769)
Update resampling tutorial (#2773)

Recipes

Use lazy import for joblib (#2498)
Revise LibriSpeech Conformer RNN-T recipe (#2535)
Fix bug in Conformer RNN-T recipe (#2611)
Replace bg_iterator in examples (#2645)
Remove obsolete examples (#2655)
Fix LibriSpeech Conforner RNN-T eval script (#2666)
Replace IValue::toString()->string() with IValue::toStringRef() (#2700)
Improve wav2vec2/hubert model for pre-training (#2716)
Improve hubert recipe for pre-training and fine-tuning (#2744)

WER improvement on LibriSpeech dev and test sets | | Viterbi (v0.12) | Viterbi (v0.13) | KenLM (v0.12) | KenLM (v0.13) | | ----- | ----- | ----- | ----- | ----- | | dev-clean | 10.7 | 10.9 | 4.4 | 4.2 | | dev-other | 18.3 | 17.5 | 9.7 | 9.4 | | test-clean | 10.8 | 10.9 | 4.4 | 4.4 | | test-other | 18.5 | 17.8 | 10.1 | 9.5 |

Documentation

Examples

Add example for Vol transform (#2597)
Add example for Vad transform (#2598)
Add example for SlidingWindowCmn transform (#2600)
Add example for MelScale transform (#2616)
Add example for AmplitudeToDB transform (#2615)
Add example for InverseMelScale transform (#2635)
Add example for MFCC transform (#2637)
Add example for LFCC transform (#2640)
Add example for Loudness transform (#2641)

Other

Remove CTC decoder prototype message (#2459)
Fix docstring (#2540)
Dataset docstring change (#2575)
Fix typo - "dimension" (#2596)
Add note for lexicon free decoder output (#2603)
Fix stylecheck (#2606)
Fix dataset docs parsing issue with extra spaces (#2607)
Remove outdated doc (#2617)
Use double quotes for string in functional and transforms (#2618)
Fix doc warning (#2627)
Update README.md (#2633)
Sphinx-gallery updates (#2629, #2638, #2736, #2678, #2679)
Tweak documentation (#2656)
Consolidate bibliography / reference (#2676)
Tweak badge link URL generation (#2677)
Adopt :autosummary: in torchaudio docs (#2664, #2681, #2683, #2684, #2693, #2689, #2690, #2692)
Update sox info docstring to account for mp3 frame count handling (#2742)
Fix HuBERT docstring (#2746)
Fix CTCDecoder doc (#2766)
Fix torchaudio.backend doc (#2781)

Build/CI

Simplify the requirements to minimum runtime dependencies (#2313)
Bump version to 0.13 (#2460)
Add tagged builds to torchaudio (#2471)
Update config.guess to the latest (#2479)
Pin MKL to 2020.04 (#2486)
Integration test fix deleting temporary directory (#2569)
Refactor cmake (#2585)
Introducing pytorch-cuda metapackage (#2612)
Move xcode to 14 from 12.5 (#2622)
Update nightly wheels to ROCm5.2 (#2672)
Lint updates (#2389, #2487)
M1 build updates (#2473, #2474, #2496, #2674)
CUDA-related updates: versions, builds, and checks (#2501, #2623, #2670, #2707, #2710, #2721, #2724)
Release-related updates (#2489, #2492, #2495, #2759)
Fix Anaconda upload (#2581, #2621)
Fix windows python 3.8 loading path (#2735, #2747)

- Python
Published by carolineechen over 3 years ago

gladia-torchaudio - torchaudio 0.12.1 Release Note

This is a minor release, which is compatible with PyTorch 1.12.1 and include small bug fixes, improvements and documentation update. There is no new feature added.

Bug Fix

#2560 Fix fall back failure in sox_io backend
#2588 Fix hubert fine-tuning recipe bugs

Improvement

#2552 Remove unused boost source code
#2527 Improve speech enhancement tutorial
#2544 Update forced alignment tutorial
#2595 Update data augmentation tutorial

For the full feature of v0.12, please refer to the v0.12.0 release note.

- Python
Published by atalman over 3 years ago

gladia-torchaudio - v0.12.0

TorchAudio 0.12.0 Release Notes

Highlights

TorchAudio 0.12.0 includes the following: * CTC beam search decoder * New beamforming modules and methods * Streaming API

[Beta] CTC beam search decoder

To support inference-time decoding, the release adds the wav2letter CTC beam search decoder, ported over from Flashlight (GitHub). Both lexicon and lexicon-free decoding are supported, and decoding can be done without a language model or with a KenLM n-gram language model. Compatible token, lexicon, and certain pretrained KenLM files for the LibriSpeech dataset are also available for download.

For usage details, please check out the documentation and ASR inference tutorial.

[Beta] New beamforming modules and methods

To improve flexibility in usage, the release adds two new beamforming modules under torchaudio.transforms: SoudenMVDR and RTFMVDR. They differ from MVDR mainly in that they: * Use power spectral density (PSD) and relative transfer function (RTF) matrices as inputs instead of time-frequency masks. The module can be integrated with neural networks that directly predict complex-valued STFT coefficients of speech and noise. * Add reference_channel as an input argument in the forward method to allow users to select the reference channel in model training or dynamically change the reference channel in inference.

Besides the two modules, the release adds new function-level beamforming methods under torchaudio.functional. These include * psd * mvdrweightssouden * mvdrweightsrtf * rtf_evd * rtf_power * apply_beamforming

For usage details, please check out the documentation at torchaudio.transforms and torchaudio.functional and the Speech Enhancement with MVDR Beamforming tutorial.

[Beta] Streaming API

StreamReader is TorchAudio’s new I/O API. It is backed by FFmpeg† and allows users to * Decode various audio and video formats, including MP4 and AAC. * Handle various input forms, such as local files, network protocols, microphones, webcams, screen captures and file-like objects. * Iterate over and decode media chunk-by-chunk, while changing the sample rate or frame rate. * Apply various audio and video filters, such as low-pass filter and image scaling. * Decode video with Nvidia's hardware-based decoder (NVDEC).

For usage details, please check out the documentation and tutorials: * Media Stream API - Pt.1 * Media Stream API - Pt.2 * Online ASR with Emformer RNN-T * Device ASR with Emformer RNN-T * Accelerated Video Decoding with NVDEC

† To use StreamReader, FFmpeg libraries are required. Please install FFmpeg. The coverage of codecs depends on how these libraries are configured. TorchAudio official binaries are compiled to work with FFmpeg 4 libraries; FFmpeg 5 can be used if TorchAudio is built from source.

Backwards-incompatible changes

I/O

MP3 decoding is now handled by FFmpeg in sox_io backend. (#2419, #2428)
- FFmpeg is now used as fallback in sox_io backend, and now MP3 decoding is handled by FFmpeg. To load MP3 audio with torchaudio.load, please install a compatible version of FFmpeg (Version 4 when using an official binary distribution).
- Note that, whereas the previous MP3 decoding scheme pads the output audio, the new scheme does not. As a consequence, the new version returns shorter audio tensors.
- torchaudio.info now returns num_frames=0 for MP3.

Models

Change underlying implementation of RNN-T hypothesis to tuple (#2339)
- In release 0.11, Hypothesis subclassed namedtuple. Containers of namedtuple instances, however, are incompatible with the PyTorch Lite Interpreter. To achieve compatibility, Hypothesis has been modified in release 0.12 to instead alias tuple. This affects RNNTBeamSearch as it accepts and returns a list of Hypothesis instances.

Bug Fixes

Ops

Fix return dtype in MVDR module (#2376)
- In release 0.11, the MVDR module converts the dtype of input spectrum to complex128 to improve the precision and robustness of downstream matrix computations. The output dtype, however, is not correctly converted back to the original dtype. In release 0.12, we fix the output dtype to be consistent with the original input dtype. ### Build
Fix Kaldi submodule integration (#2269)
Pin jinja2 version for build_docs (#2292)
Use sourceforge url to fetch zlib (#2297)

New Features

I/O

Add Streaming API (#2041, #2042, #2043, #2044, #2045, #2046, #2047, #2111, #2113, #2114, #2115, #2135, #2164, #2168, #2202, #2204, #2263, #2264, #2312, #2373, #2378, #2402, #2403, #2427, #2429)
Add YUV420P format support to Streaming API (#2334)
Support specifying decoder and its options (#2327)
Add NV12 format support in Streaming API (#2330)
Add HW acceleration support on Streaming API (#2331)
Add file-like object support to Streaming API (#2400)
Make FFmpeg log level configurable (#2439)
Set the default ffmpeg log level to FATAL (#2447) ### Ops
New beamforming methods (#2227, #2228, #2229, #2230, #2231, #2232, #2369, #2401)
New MVDR modules (#2367, #2368)
Add and refactor CTC lexicon beam search decoder (#2075, #2079, #2089, #2112, #2117, #2136, #2174, #2184, #2185, #2273, #2289)
Add lexicon free CTC decoder (#2342)
Add Pretrained LM Support for Decoder (#2275)
Move CTC beam search decoder to beta (#2410) ### Datasets
Add QUESST14 dataset (#2290, #2435, #2458)
Add LibriLightLimited dataset (#2302)

Improvements

I/O

Use FFmpeg-based I/O as fallback in sox_io backend. (#2416, #2418, #2423) ### Ops
Raise error for resampling int waveform (#2318)
Move multi-channel modules to a separate file (#2382)
Refactor MVDR module (#2383) ### Models
Add an option to use Tanh instead of ReLU in RNNT joiner (#2319)
Support GroupNorm and re-ordering Convolution/MHA in Conformer (#2320)
Add extra arguments to hubert pretrain factory functions (#2345)
Add featuregradmult argument to HuBERTPretrainModel (#2335) ### Datasets
Refactor LibriSpeech dataset (#2387)
Raising RuntimeErrors when datasets missing (#2430)

Performance

Make Pitchshift for faster by caching resampling kernel (#2441) The following table illustrates the performance improvement over the previous release by comparing the time in msecs it takes torchaudio.transforms.PitchShift, after its first call, to perform the operation on float32 Tensor with two channels and 8000 frames, resampled to 44.1 kHz across various shifted steps.

| TorchAudio Version | 2 | 3 | 4 | 5 | | ----- | ----- | ----- | ----- | ----- | | 0.12 | 2.76 | 5 | 1860 | 223 | | 0.11 | 6.71 | 161 | 8680 | 1450 |

Tests

Add complex dtype support in functional autograd test (#2244)
Refactor torchscript consistency test in functional (#2246)
Add unit tests for PyTorch Lightning modules of emformer_rnnt recipes (#2240)
Refactor batch consistency test in functional (#2245)
Run smoke tests on regular PRs (#2364)
Refactor smoke test executions (#2365)
Move seed to setup (#2425)
Remove possible manual seeds from test files (#2436) ### Build
Revise the parameterization of third party libraries (#2282)
Use zlib v1.2.12 with GitHub source (#2300)
Fix ffmpeg integration for ffmpeg 5.0 (#2326)
Use custom FFmpeg libraries for torchaudio binary distributions (#2355)
Adding m1 builds to torchaudio (#2421) ### Other
Add download utility specialized for torchaudio (#2283)
Use module-level __getattr__ to implement delayed initialization (#2377)
Update build_doc job to use Conda CUDA package (#2395)
Update I/O initialization (#2417)
Add Python 3.10 (build and test) (#2224)
Retrieve version from version.txt (#2434)
Disable OpenMP on mac (#2431)

Examples

Ops

Add CTC decoder example for librispeech (#2130, #2161)
Fix LM, arguments in CTC decoding script (#2235, #2315)
Use pretrained LM API for decoder example (#2317) ### Pipelines
Refactor pipelinedemo.py to support variant EMFORMERRNNT bundles (#2203)
Refactor eval and pipelinedemo scripts in emformerrnnt (#2238)
Refactor pipelinedemo script in emformerrnnt recipes (#2239)
Add EMFORMERRNNTBASE_MUSTC into pipeline demo script (#2248) ### Tests
Add unit tests for Emformer RNN-T LibriSpeech recipe (#2216)
Add fixed random seed for Emformer RNN-T recipe test (#2220) ### Training recipes
Add recipe for HuBERT model pre-training (#2143, #2198, #2296, #2310, #2311, #2412)
Add HuBERT fine-tuning recipe (#2352)
Refactor Emformer RNNT recipes (#2212)
Fix bugs from Emformer RNN-T recipes merge (#2217)
Add SentencePiece model training script for LibriSpeech Emformer RNN-T (#2218)
Add training recipe for Emformer RNNT trained on MuST-C release v2.0 dataset (#2219)
Refactor ArgumentParser arguments in emformer_rnnt recipes (#2236)
Add shebang lines to scripts in emformer_rnnt recipes (#2237)
Introduce DistributedBatchSampler (#2299)
Add Conformer RNN-T LibriSpeech training recipe (#2329)
Refactor LibriSpeech Conformer RNN-T recipe (#2366)
Refactor LibriSpeech Lightning datamodule to accommodate different dataset implementations (#2437)

Prototypes

Models

Add Conformer RNN-T model prototype (#2322)
Add ConvEmformer module (streaming-capable Conformer) (#2324, #2358)
Add convtasnetbase factory function to prototype (#2411) ### Pipelines
Add EMFORMERRNNTBASE_MUSTC bundle to torchaudio.prototype (#2241)

Documentation

Add ASR CTC decoding inference tutorial (#2106)
Update context building to not delay the inference (#2213)
Update online ASR tutorial (#2226)
Update CTC decoder docs and add citation (#2278)
[Doc] fix typo and backlink (#2281)
Fix calculation of SNR value in tutorial (#2285)
Add notes about prototype features in tutorials (#2288)
Update README around version compatibility matrix (#2293)
Update decoder pretrained lm docs (#2291)
Add devices/properties badges (#2321)
Fix LibriMix documentation (#2351)
Update wavernn.py (#2347)
Add citations for datasets (#2371)
Update audio I/O tutorials (#2385)
Update MVDR beamforming tutorial (#2398)
Update audio feature extraction tutorial (#2391)
Update audio resampling tutorial (#2386)
Update audio data augmentation tutorial (#2388)
Add tutorial to use NVDEC with Stream API (#2393)
Expand subsections in tutorials by default (#2397)
Fix documentation (#2407)
Fix documentation (#2409)
Dataset doc fixes (#2426)
Update CTC decoder docs (#2443)
Split Streaming API tutorials into two (#2446)
Update HW decoding tutorial and add notes about unseekable object (#2408)

- Python
Published by hwangjeff over 3 years ago

gladia-torchaudio - v0.11.0

torchaudio 0.11.0 Release Note

Highlights

TorchAudio 0.11.0 release includes: - Emformer (paper) RNN-T components, training recipe, and pre-trained pipeline for streaming ASR - Voxpopuli pre-trained pipelines - HuBERTPretrainModel for training HuBERT from scratch - Conformer model for speech recognition - Drop Python 3.6 support

[Beta] Emformer RNN-T

To support streaming ASR use cases, the release adds implementations of Emformer (docs), an RNN-T model that uses Emformer (emformerrnntbase), and an RNN-T beam search decoder (RNNTBeamSearch). It also includes a pipeline bundle (EMFORMERRNNTBASE_LIBRISPEECH) that wraps pre- and post-processing components, the beam search decoder, and the RNN-T Emformer model with weights pre-trained on LibriSpeech, which in whole allow for performing streaming ASR inference out of the box. For reference and reproducibility, the release provides the training recipe used to produce the pre-trained weights in the examples directory.

[Beta] HuBERT Pretrain Model

The masked prediction training of HuBERT model requires the masked logits, unmasked logits, and feature norm as the outputs. The logits are for cross-entropy losses and the feature norm is for penalty loss. The release adds HuBERTPretrainModel and corresponding factory functions (hubertpretrainbase, hubertpretrainlarge, and hubertpretrainxlarge) to enable training from scratch.

[Beta] Conformer (paper)

The release adds an implementation of Conformer (docs), a convolution-augmented transformer architecture that has achieved state-of-the-art results on speech recognition benchmarks.

Backward-incompatible changes

Ops

Removed deprecated F.magphase, F.angle, F.complex_norm, and T.ComplexNorm. (#1934, #1935, #1942)
- Utility functions for pseudo complex types were deprecated in 0.10, and now they are removed in 0.11. For the detail of this migration plan, please refer to #1337.
Dropped pseudo complex support from F.spectrogram, T.Spectrogram, F.phase_vocoder, and T.TimeStretch (#1957, #1958)
- The support for the pseudo complex type was deprecated in 0.10, and now they are removed in 0.11. For the detail of this migration plan, please refer to #1337.
Removed deprecated create_fb_matrix (#1998)
- create_fb_matrix was replaced by melscale_fbanks in release 0.10. It is removed in 0.11. Please use melscale_fbanks. ### Datasets
Removed deprecated VCTK (#1825)
- The original VCTK archive file is no longer accessible. Please migrate to VCTK_092 class for the latest version of the dataset.
Removed deprecated dataset utils (#1826)
- Undocumented methods diskcache_iterator and bg_iterator were deprecated in 0.10. They are removed in 0.11. Please cease the usage of them. ### Models
Removed unused dimension from pretrained Wav2Vec2 ASR (#1914)
- The final linear layer of Wav2Vec2 ASR models included dimensions (<s>, <pad>, </s>, <unk>) that were not related to ASR tasks and not used. These dimensions were removed. ### Build
Dropped support for Python3.6 (#2119, #2139)
- Following the lifecycle of Python-3.6, torchaudio dropped the support for Python 3.6.

New Features

RNN-T Emformer

Introduced Emformer (#1801)
Added Emformer RNN-T model (#2003)
Added RNN-T beam search decoder (#2028)
Cleaned up Emformer module (#2091)
Added pretrained Emformer RNN-T streaming ASR inference pipeline (#2093)
Reorganized RNN-T components in prototype module (#2110)
Added integration test for Emformer RNN-T LibriSpeech pipeline (#2172)
Registered RNN-T pipeline global stats constants as buffers (#2175)
Refactored RNN-T factory function to support num_symbols argument (#2178)
Fixed output shape description in RNN-T docstrings (#2179)
Removed invalid token blanking logic from RNN-T decoder (#2180)
Updated stale prototype references (#2189)
Revised RNN-T pipeline streaming decoding logic (#2192)
Cleaned up Emformer (#2207)
Applied minor fixes to Emformer implementation (#2252)

Conformer

Introduced Conformer (#2068)
Removed subsampling and positional embedding logic from Conformer (#2171)
Moved ASR features out of prototype (#2187)
Passed bias and dropout args to Conformer convolution block (#2215)
Adjusted Conformer args (#2223)

Datasets

Added DR-VCTK dataset (#1819)

Models

Added HuBERT pretrain model to enable training from scratch (#2064)
Added feature mean square value to HuBERT Pretrain model output (#2128)

Pipelines

Added wav2vec2 ASR French pretrained from voxpopuli (#1919)
Added wav2vec2 ASR Spanish pretrained model from voxpopuli (#1924)
Added wav2vec2 ASR German pretrained model from voxpopuli (#1953)
Added wav2vec2 ASR Italian pretrained model from voxpopuli (#1954)
Added wav2vec2 ASR English pretrained model from voxpopuli (#1956)

Build

Added CUDA-11.5 builds to torchaudio (#2067)

Improvements

I/O

Fixed load behavior for 24-bit input (#2084)

Ops

Added OpenMP support (#1761)
Improved MVDR stability (#2004)
Relaxed dtype for MVDR (#2024)
Added warnings in mu_law* for the wrong input type (#2034)
Added parameter p to TimeMasking (#2090)
Removed unused vars from RNN-T loss (#2142)
Removed complex32 dtype in F.griffinlim (#2233)

Datasets

Deprecated data utils (#2073)
Updated URLs for libritts (#2074)
Added subset support for TEDLIUM release3 dataset (#2157)

Models

Replaced dropout with Dropout (#1815)
Inplace initialization of RNN weights (#2010)
Updated to xavieruniform and avoid legacy data.uniform initialization (#2018)
Allowed Tacotron2 decode batch_size 1 examples (#2156)

Pipelines

Added tool to convert voxpopuli model (#1923)
Refactored wav2vec2 pipeline util (#1925)
Allowed the customization of axis exclusion for ASR head (#1932)
Tweaked wav2vec2 checkpoint conversion tool (#1938)
Added melkwargs setting for MFCC in HuBERT pipeline (#1949)

Documentation

Added 0.10.0 to version compatibility matrix (#1862)
Removed MACOSXDEPLOYMENTTARGET (#1880)
Updated intersphinx inventory (#1893)
Updated compatibility matrix to include LTS version (#1896)
Updated CONTRIBUTING with doc conventions (#1898)
Added anaconda stats to README (#1910)
Updated README.md (#1916)
Added citation information (#1947)
Updated CONTRIBUTING.md (#1975)
Doc fixes (#1982)
Added tutorial to CONTRIBUTING (#1990)
Fixed docstring (#2002)
Fixed minor typo (#2012)
Updated audio augmentation tutorial (#2082)
Added Sphinx gallery automatically (#2101)
Disabled matplotlib warning in tutorial rendering (#2107)
Updated prototype documentations (#2108)
Added custom CSS to make signatures appear in multi-line (#2123)
Updated prototype pipeline documentation (#2148)
Tweaked documentation (#2152)

Tests

Refactored integration test (#1922)
Enabled integration tests on CI (#1939)
Removed facebook folder in wav2vec unit tests (#2015)
Temporarily skipped threadpool test (#2025)
Revised Griffin-Lim transform test to reduce execution time (#2037)
Fixed CircleCI test failures (#2069)
Do not auto-skip tests on CI (#2127)
Relaxed absolute tolerance for Kaldi compat tests (#2165)
Added tacotron2 unit test with different batch_size (#2176)

Build

Updated GPU resource class (#1791)
Updated the main version to 0.11.0 (#1793)
Updated windows cuda installer 11.1.0 to 11.1.1 (#1795)
Renamed build_tools to tools (#1812)
Limit Windows GPU testing to CUDA-11.3 only (#1842)
Used cu113 for unittestwindowsgpu (#1853)
USE_CUDA in windows and reduce one vcvarsall (#1854)
Check torch installation before building package (#1867)
Install tools from conda instead of brew (#1873)
Cleaned up setup.py (#1900)
Moved TorchAudio conda package to use pytorch-mutex (#1904)
Updated smoke test docker image (#1905)
Fixed formatting CIRCLECI_TAG when building docs (#1915)
Fetch third party sources automatically (#1966)
Disabled SPHINXOPT=-W for local env (#2013)
Improved installing nightly pytorch (#2026)
Improved cuda installation on windows (#2032)
Refactored the library loading mechanism (#2038)
Cleaned up libtorchaudio customization logic (#2039)
Refactored and functionize the library definition (#2040)
Introduced helper function to define extension (#2077)
Standardized the location of third-party source code (#2086)
Show lint diff with color (#2102)
Updated third party submodule setup (#2132)
Suppressed stderr from subprocess in setup.py (#2133)
Fixed header include (#2135)
Updated ROCM version 4.1 -> 4.3.1 and 4.5 (#2186)
Added "cu102" back (#2190)
Pinned flake8 version (#2191)

Style

Removed trailing whitespace (#1803)
Fixed style checks (#1913)
Resolved lint warning (#1971)
Enabled CLANGFORMAT (#1999)
Fixed style checks in examples/tutorials (#2006)
OSS config for lint checks (#2066)
Excluded sphinx-gallery examples (#2071)
Reverted linting exemptions introduced in #2071 (#2087)
Applied arc lint to pytorch audio (#2096)
Enforced lint checks and fix/mute lint errors (#2116)

Other

Replaced issue templates with new issue forms (#1802)
Notify merger if PR is incorrectly labeled (#1937)
Added script to collect PRs between commits (#1943)
Fixed PR labeling requirement (#1946)
Refactored collecting-PR script for release note (#1951)
Fixed bandit failure (#1960)
Renamed bug fix label (#1961)
Updated PR label notifier (#1964)
Reverted "Update PR label notifier (#1964)" (#1965)
Consolidated network utils (#1974)
Added PR collecting script (#2008)
Re-sync with internal repository (#2017)
Updated script for getting PR merger and labels (#2030)
Fixed third party archive fetch job (#2095)
Use python:3.X Docker image for build doc (#2151)
Updated PR labeling workflow (#2160)
Fixed librosa calls (#2208)

Examples

Ops

Removed the MVDR tutorial in examples (#2109)
Abstracted BucketizeSampler to be usable outside of HuBERT example (#2147)
Refactored BucketizeBatchSampler and HuBERTDataset (#2150)
Removed multiprocessing from audio dataset tutorial (#2163)

Models

Added training recipe for RNN-T Emformer ASR model (#2052)
Added global stats script and new json for LibriSpeech RNN-T training recipe (#2183)

Pipelines

Added preprocessing scripts for HuBERT model training (#1911)
Supported multi-node training for source separation pipeline (#1968)
Added bucketize sampler and dataset for HuBERT Base model training pipeline (#2000)
Added librispeech inference script (#2130)

Other

Added unmaintained warnings (#1813)
torch.quantization -> torch.ao.quantization (#1823)
Use download.pytorch.org for asset URL (#2182)
Added deprecation path for renamed training type plugins (#11227)
Renamed DDPPlugin to DDPStrategy (#11142)

- Python
Published by nateanl almost 4 years ago

gladia-torchaudio - torchaudio v0.10.2 Minor release

This is a minor release compatible with PyTorch 1.10.2.

There is no feature change in torchaudio from 0.10.1. For the full feature of v0.10, please refer to the v0.10.0 release notes.

- Python
Published by atalman about 4 years ago

gladia-torchaudio - torchaudio 0.10.1 Release Note

This is a minor release, which is compatible with PyTorch 1.10.1 and include small bug fix, improvements and documentation update. There is no new feature added.

Bug Fix

#2050 Allow whitespace as TORCH_CUDA_ARCH_LIST delimiter

Improvement

#2054 Fetch third party source code automatically The build process now fetches third party source code (git submodule and cmake external projects)
#2059 Improve documentation

For the full feature of v0.10, please refer to the v0.10.0 release note.

- Python
Published by mthrok about 4 years ago

gladia-torchaudio - v0.10.0

torchaudio 0.10.0 Release Note

Highlights

torchaudio 0.10.0 release includes: - New models (Tacotron2, HuBERT) and datasets (CMUDict, LibriMix) - Pretrained model support for ASR (Wav2Vec2, HuBERT) and TTS (WaveRNN, Tacotron2) - New operations (RNN Transducer loss, MVDR beamforming, PitchShift, etc) - CUDA-enabled binaries

[Beta] Wav2Vec2 / HuBERT Models and Pretrained Weights

HuBERT model architectures (“base”, “large” and “extra large” configurations) are added. In addition to that, support for pretrained weights from wav2vec 2.0, Unsupervised Cross-lingual Representation Learning and HuBERT are added.

These pretrained weights can be used for feature extractions and downstream task adaptation. ```python

import torchaudio

Build the model and load pretrained weight.

model = torchaudio.pipelines.HUBERTBASE.getmodel()

Perform feature extraction.

features, lengths = model.extract_features(waveforms)

Pass the features to downstream task

... ```

Some of the pretrained weights are fine-tuned for ASR tasks. The following example illustrates how to use weights and access to associated information, such as labels, which can be used in subsequent CTC decoding steps. (Note: torchaudio does not provide a CTC decoding mechanism.) ```python

import torchaudio

bundle = torchaudio.pipelines.HUBERTASRLARGE

Build the model and load pretrained weight.

model = bundle.get_model() Downloading: 100%|███████████████████████████████| 1.18G/1.18G [00:17<00:00, 73.8MB/s]

Check the corresponding labels of the output.

labels = bundle.get_labels() print(labels) ('~~', '', '~~', '', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z')

Infer the label probability distribution

waveform, sample_rate = torchaudio.load(hello-world.wav')

emissions, _ = model(waveform)

Pass emission to (hypothetical) decoder

transcripts = ctc_decode(emissions, labels) print(transcripts[0]) HELLO WORLD ```

[Beta] Tacotron2 and TTS Pipeline

A new model architecture, Tacotron2 is added, alongside several pretrained weights for TTS (text-to-speech). Since these TTS pipelines are composed of multiple models and specific data processing, so as to make it easy to use associated objects, a notion of bundle is introduced. Bundles provide a common access point to create a pipeline with a set of pretrained weights. They are available under torchaudio.pipelines module. The following example illustrates a TTS pipeline where two models (Tacotron2 and WaveRNN) are used together. ```python

import torchaudio

bundle = torchaudio.pipelines.TACOTRON2WAVERNNCHAR_LJSPEECH

Build text processor, Tacotron2 and vocoder (WaveRNN) model

processor = bundle.gettextpreprocessor() tacotron2 = bundle.gettacotron2() Downloading: 100%|███████████████████████████████| 107M/107M [00:01<00:00, 87.9MB/s] vocoder = bundle.getvocoder() Downloading: 100%|███████████████████████████████| 16.7M/16.7M [00:00<00:00, 78.1MB/s]

text = "Hello World!"

Encode text

input, lengths = processor(text)

Generate (mel-scale) spectrogram

specgram, lengths, _ = tacotron2.infer(input, lengths)

Convert spectrogram to waveform

waveforms, lengths = vocoder(specgram, lengths)

Save audio

torchaudio.save('hello-world.wav', waveforms, vocoder.sample_rate) ```

[Beta] RNN Transducer Loss

The loss function used in the RNN transducer architecture, which is widely used for speech recognition tasks, is added. The loss function (torchaudio.functional.rnnt_loss or torchaudio.transforms.RNNTLoss) supports float16 and float32 logits, has autograd and torchscript support, and can be run on both CPU and GPU, which has a custom CUDA kernel implementation for improved performance.

[Beta] MVDR Beamforming

This release adds support for MVDR beamforming on multi-channel audio using Time-Frequency masks. There are three solutions (refchannel, stvevd, stv_power) and it supports single-channel and multi-channel (perform average in the method) masks. It provides an online option that recursively updates the parameters for streaming audio. Please refer to the MVDR tutorial.

GPU Build

This release adds GPU builds that support custom CUDA kernels in torchaudio, like the one being used for RNN transducer loss. Following this change, torchaudio’s binary distribution now includes CPU-only versions and CUDA-enabled versions. To use CUDA-enabled binaries, PyTorch also needs to be compatible with CUDA.

Additional Features

torchaudio.functional.lfilter now supports batch processing and multiple filters. Additional operations, including pitch shift, LFCC, and inverse spectrogram, are now supported in this release. The datasets CMUDict and LibriMix are added as well.

Backward Incompatible Changes

I/O

Default to PCM_16 for flac on soundfile backend (#1604)
- When saving FLAC format with “soundfile” backend, PCM_24 (the previous default) could cause warping. The default has been changed to PCM_16, which does not suffer this. ### Ops
Default to native complex type when returning raw spectrogram (#1549)
- When power=None, torchaudio.functional.spectrogram and torchaudio.transforms.Spectrogram now defaults to return_complex=True, which returns Tensor of native complex type (such as torch.cfloat and torch.cdouble). To use a pseudo complex type, pass the resulting tensor to torch.view_as_real.
Remove deprecated kaldi.resample_waveform (#1555)
- Please use torchaudio.functional.resample.
Replace waveform with specgram in SlidingWindowCmn (#1859)
- The argument name was corrected to specgram.
Ensure integer input frequencies for resample (#1857)
- Sampling rates were silently cast to integers in the resampling implementation, so it now requires integer sampling rate inputs to ensure expected resampling quality. ### Wav2Vec2
Update extract_features of Wav2Vec2Model (#1776)
- The previous implementation returned outputs from convolutional feature extractors. To match the behavior with the original fairseq’s implementation, the method was changed to return the outputs of the intermediate layers of transformer layers. To achieve the original behavior, please use Wav2Vec2Model.feature_extractor().
Move fine-tune specific module out of wav2vec2 encoder (#1782)
- The internal structure of Wav2Vec2Model was updated. Wav2Vec2Model.encoder.read_out module is moved to Wav2Vec2Model.aux. If you have serialized state dict, please replace the key encoder.read_out with aux.
Updated wav2vec2 factory functions for more customizability (#1783, #1804, #1830)
- The signatures of wav2vec2 factory functions are changed. num_out parameter has been changed to aux_num_out and other parameters are added before it. Please update the code from wav2vec2_base(num_out) to wav2vec2_base(aux_num_out=num_out).

Deprecations

Add melscale_fbanks and deprecate create_fb_matrix (#1653)
- As linear_fbanks is introduced, create_fb_matrix is renamed to melscale_fbanks. The original create_fb_matrix is now deprecated. Please use melscale_fbanks.
Deprecate VCTK dataset (#1810)
- This dataset has been taken down and is no longer available. Please use VCTK_092 dataset.
Deprecate data utils (#1809)
- bg_iterator and diskcache_iterator are known to not improve the throughput of data loaders. Please cease their usage.

New Features

Models

Tacotron2 - Add Tacotron2 model (#1621, #1647, #1844) - Add Tacotron2 loss function (#1764) - Add Tacotron2 inference method (#1648, #1839, #1849) - Add phoneme text preprocessing for Tacotron2 (#1668) - Move Tacotron2 out of prototype (#1714)

HuBERT - Add HuBERT model architectures (#1769, #1811)

Pretrained Weights and Pipelines

Add pretrained weights for wavernn (#1612) 
Add Tacotron2 pretrained models (#1693) 
Add HUBERT pretrained weights (#1821, #1824) 
Add pretrained weights from wav2vec2.0 and XLSR papers (#1827) 
Add customization support to wav2vec2 labels (#1834) 
Default pretrained weights to eval mode (#1843) 
Move wav2vec2 pretrained models to pipelines module (#1876) 
Add TTS bundle/pipelines (#1872) 
Fix vocoder interface (#1895) 
Fix Phonemizer download (#1897)

RNN Transducer Loss

Add reduction parameter for RNNT loss (#1590) 
Rename RNNT loss C++ parameters (#1602) 
Rename transducer to RNNT (#1603) 
Remove gradient variable from RNNT loss Python code (#1616) 
Remove reuselogitsfor_grads option for RNNT loss (#1610) 
Remove fusedlogsoftmax option from RNNT loss (#1615) 
RNNT loss resolve null gradient (#1707) 
Move RNNT loss out of prototype (#1711)

MVDR Beamforming

Add MVDR module to example (#1709) 
Add normalization to steering vector solutions in MVDR Module (#1765) 
Move MVDR and PSD modules to transforms (#1771) 
Add MVDR beamforming tutorial to example directory (#1768)

Ops

Add edit_distance (#1601) 
Add PitchShift to functional and transform (#1629) 
Add LFCC feature to transforms (#1611) 
Add InverseSpectrogram to transforms and functional (#1652)

Datasets

Add CMUDict dataset (#1627) 
Move LibriMix dataset to datasets directory (#1833)

Improvements

I/O

Make buffer size for function info configurable (#1634)

Ops

Replace deprecated AutoNonVariableTypeMode (#1583) 
Remove lazy behavior from MelScale (#1636) 
Simplify axis value checks (#1501) 
Use at::parallel_for in lfilter core loop (#1557) 
Add filterbanks support to lfilter (#1587) 
Add batch support to lfilter (#1638) 
Use integer rates in pitch shift resample (#1861)

Models

Rename infer method to forward for WaveRNNInferenceWrapper (#1650) 
Refactor WaveRNN infer and move it to the codebase (#1704) 
Make the core wav2vec2 factory function public (#1829) 
Refactor WaveRNNInferenceWrapper (#1845) 
Store n_bits in WaveRNN (#1847) 
Replace custom padding with torch’s native impl (#1846) 
Avoid concatenation in loop (#1850) 
Add lengths param to WaveRNN.infer (#1851) 
Add sample rate to wav2vec2 bundle (#1878) 
Remove factory functions of Tacotron2 and WaveRNN (#1874)

Datasets

Fix encoding of CMUDict data reading (#1665) 
Rename utterance to transcript in datasets (#1841) 
Clean up constructor of CMUDict (#1852)

Performance

Refactor transforms.Fade on GPU computation (#1871)

CUDA Tensor shape | [1,4,8000] | [1,4,16000] | [1,4,32000] -- | -- | -- | -- 0.10 | 119 | 120 | 123 0.9 | 160 | 184 | 240

Unit: msec

Examples

Add text preprocessing utilities for TTS pipeline (#1639) 
Replace simple_ctc with Python greedy decoder (#1558) 
Add an inference example for WaveRNN (#1637) 
Refactor coding style for WaveRNN example (#1663) 
Add style checks on example files on CI (#1667) 
Add Tacotron2 training script (#1642) 
Add an inference example for Tacotron2 (#1654) 
Fix Tacotron2 inference example (#1716) 
Fix WaveRNN training example (#1740) 
Training recipe for ConvTasNet on Libri2Mix dataset (#1757)

Build

Update skipIfNoCuda decorator and force GPU tests in GPU CIs (#1559) 
Temporarily pin nightly version on Linux/macOS CPU unittest (#1598) 
Temporarily pin nightly version on Linux GPU unitest (#1606) 
Revert CI hot fix (#1614) 
Expose USE_CUDA in build (#1609) 
Pin MKL to 2021.2.0 (#1655) 
Simplify extension initialization (#1649) 
Synchronize extension initialization mechanism with fbcode (#1682) 
Ensure we’re propagating BUILD_VERSION (#1697) 
Guard Kaldi’s version generation (#1715) 
Update sphinx to 3.5.4 (#1685) 
Default to BUILD_SOX=1 in non-Windows systems (#1725) 
Add CUDA install step to Win Packaging jobs (#1732) 
setup.py should parse TORCHCUDAARCH_LIST (#1733) 
Simplify the extension initialization process (#1734) 
Fix CUDA build logic for _torchaudio.so (#1737) 
Enable Linux wheel/conda GPU package builds (#1730) 
Increase nooutputtimeout to 20m for WinConda (#1738) 
Build torchaudio for 11.3 as well (#1747) 
Upload wheels to respective folders (#1751) 
Extract PyBind11 feature implementations (#1739) 
Update the way to access libsox global config (#1755) 
Fix ROCM build error (#1729) 
Fix compile warnings (#1762) 
Migrate CircleCI docker image (#1767) 
Split extension into custom impl and Python wrapper libraries (#1752) 
Put libtorchaudio in lib directory (#1773) 
Update win gpu image from previous to stable (#1786) 
Set libtorch audio suffix as pyd on Windows (#1788) 
Fix build on Windows with CUDA (#1787) 
Enable audio windows cuda tests (#1777) 
Set release and base PyTorch version (#1816) 
Exclude prototype if it is in release (#1870) 
Log prototype exclusion (#1882) 
Update prototype exclusion (#1885) 
Remove alpha from version number (#1901)

Testing

Migrate resample tests from kaldi to functional (#1520) 
Add autograd gradcheck test for RNN transducer loss (#1532) 
Fix HF wav2vec2 test (#1585) 
Update unit test CUDA to 10.2 (#1605) 
Fix CircleCI unittest environemnt 
Remove skipIfRocm from testfileobjflac in soundfile.save_test (#1626) 
MFCC test refactor (#1618) 
Refactor RNNT Loss Unit Tests (#1630) 
Reduce sample rate to avoid test time out (#1640) 
Refactor text preprocessing tests in Tacotron2 example (#1635) 
Move test initialization logic to dedicated directory (#1680) 
Update pitch shift batch consistency test (#1700) 
Refactor scripting in test (#1727) 
Update the version of fairseq used for testing (#1745) 
Put output tensor on proper device in get_whitenoise (#1744) 
Refactor batch consistency test in transforms (#1772) 
Tweak test name by appending factory function name (#1780) 
Enable audio windows cuda tests (#1777) 
Skip hubertasrxlarge TS test on Windows (#1800) 
Skip hubert_xlarge TS test on Windows (#1807)

Others

Remove unused files (#1588) 
Remove residuals for removed modules (#1599) 
Remove torchscript bc test references (#1623) 
Remove torchaudio._internal.fft module (#1631)

Misc

Rename master branch to main (#1649) 
Fix Python spacing (#1670) 
Lint fix (#1726) 
Add .gitattributes (#1731) 
Style fixes (#1766) 
Update reference from master to main elsewhere (#1784)

Bug Fixes

Fix models import (#1664) 
Fix HF model integration (#1781)

Documentation

README Updates 
- Update README (#1544) 
- Remove NumPy dependency from README (#1582) 
- Fix typos and sentence structure in README.md (#1633) 
- Update and move convention section to CONTRIBUTING.md (#1635) 
- Remove unnecessary README (#1728) 
- Add link to TTS colab example to README (#1748) 
- Fix typo in source separation README (#1774) 
Docstring Changes 
- Set removal version of pseudo complex support (#1553) 
- Update docs (#1584) 
- Add return type in doc for RNNT loss (#1591) 
- Improve RNNT loss docstrings (#1642) 
- Add documentation for CMUDict’s property (#1683) 
- Refactor lfilter docs (#1698) 
- Standardize optional types in docstrings (#1746) 
- Fix return type of wav2vec2 model (#1790) 
- Add equations to MVDR docstring (#1789) 
- Standardize tensor shapes format in docs (#1838) 
- Add license to pre-trained model doc (#1836) 
- Update Tacotron2 docs (#1840) 
- Fix PitchShift docstring (#1866) 
- Update descriptions of lengths parameters (#1890) 
- Standardization and minor fixes (#1892) 
- Update models/pipelines doc (#1894) 
Docs formatting 
- Remove override CSS (#1554) 
- Add prototype.tacotron2 page to docs (#1695) 
- Add doc for InverseSepctrogram (#1706) 
- Add sections to transforms docs (#1720) 
- Add edit_distance to documentation with a new category Metric (#1743) 
- Fix model subsections (#1775) 
- List all the pre-trained models on right bar (#1828) 
- Put pretrained weights to subsection (#1879) 
Examples (see #1564) 
- Add example code for Resample (#1644) 
- Fix examples in transforms (#1646) 
- Add example for ComplexNorm (#1658) 
- Add example for MuLawEncoding (#1586) 
- Add example for Spectrogram (#1566) 
- Add example for GriffinLim (#1671) 
- Add example for MuLawDecoding (#1684) 
- Add example for Fade transform (#1719) 
- Update RNNT loss docs and add example (#1835) 
- Add SpecAugment figure/citation (#1887) 
- Add filter bank figures (#1891)

- Python
Published by carolineechen over 4 years ago

gladia-torchaudio - torchaudio 0.9.1 Minor bugfix release

This release depends on pytorch 1.9.1 No functional changes other than minor updates to CI rules.

- Python
Published by malfet over 4 years ago

gladia-torchaudio - v0.9.0

torchaudio 0.9.0 Release Note

Highlights

torchaudio 0.9.0 release includes:

Lots of performance improvements. (filtering, resampling, spectral operation)
Popular wav2vec2.0 model architecture.
Improved autograd support.

[Beta] Wav2Vec2.0 Model

This release includes model architectures from wav2vec2.0 paper with utility functions that allow importing pretrained model parameters published on fairseq and Hugging Face Hub. Now you can easily run speech recognition with torchaudio. These model architectures also support TorchScript, and you can deploy them with ONNX or in non-Python environments, such as C++, Android and iOS. Please checkout our C++, Android and iOS examples. The following snippets illustrate how to create a deployable model.

```python

Import fine-tuned model from Hugging Face Hub

import transformers from torchaudio.models.wav2vec2.utils import importhuggingfacemodel

original = Wav2Vec2ForCTC.frompretrained("facebook/wav2vec2-base-960h") imported = importhuggingface_model(original) ```

```python

Import fine-tuned model from fairseq

import fairseq from torchaudio.models.wav2vec2.utils import importfairseqmodel

Original, , _ = fairseq.checkpointutils.loadmodelensembleandtask( ["wav2vecsmall960h.pt"], argoverrides={'data': "<datadir>"}) imported = importfairseqmodel(original[0].w2v_encoder) ```

```python

Build uninitialized model and load state dict

from torchaudio.models import wav2vec2_base

model = wav2vec2base(numout=32) model.loadstatedict(imported.state_dict())

Quantize / script / optimize for mobile

quantizedmodel = torch.quantization.quantizedynamic( model, qconfigspec={torch.nn.Linear}, dtype=torch.qint8) scriptedmodel = torch.jit.script(quantizedmodel) optimizedmodel = optimizeformobile(scriptedmodel) optimizedmodel.save("modelfordeployment.pt") ```

Filtering Improvement

The internal implementation of lfilter has been updated to support autograd on both CPU and CUDA. Additionally, the performance on CPU is significantly improved. These improvements also apply to biquad variants.

The following table illustrates the performance improvements compared against the previous releases. lfilter was applied on float32 tensors with one channel and different number of frames.

torchaudio version	256	512	1024
0.9	0.282	0.381	0.564
0.8	0.493	0.780	1.37
0.7	5.42	10.8	22.3

Unit: msec

Complex Tensor Migration

torchaudio has functions that handle complex-valued tensors. In early days when PyTorch did not have a complex dtype, torchaudio adopted the convention to use an extra dimension to represent real and imaginary parts. In PyTorch 1.6, new dtyps, such as torch.cfloat and torch.cdouble were introduced to represent complex values natively. (In the following, we refer to torchaudio’s original convention as pseudo complex types, and PyTorch’s native dtype as native complex types.)

As the native complex types have become mature and stable, torchaudio has started to migrate complex functions to use the native complex type. In this release, the internal implementation was updated to use the native complex types, and interfaces were updated to allow passing/receiving native complex type directly. Users can choose to keep using the pseudo complex type or opt in to use native complex type. However, please note that the use of the pseudo complex type is now deprecated. These functions are tested to support TorchScript and autograd. For the detail of this migration plan, please refer to #1337.

Additionally, switching the internal implementation to the native complex types improved the performance. Since the internal implementation uses native complex type regardless of which complex type is passed/returned, users will automatically benefit from this performance improvement.

The following table illustrates the performance improvements from the previous release by comparing the time it takes for complex transforms to perform operation on float32 Tensor with two channels and 256 frames.

CPU

torchaudio version	`Spectrogram`	`TimeStretch`	`GriffinLim`
0.9	0.229	12.6	3320
0.8	0.283	126	5320

Unit: msec

CUDA

torchaudio version	`Spectrogram`	`TimeStretch`	`GriffinLim`
0.9	0.195	0.599	36
0.8	0.219	0.687	60.2

Unit: msec

Improved Autograd Support

Along with the work of Complex Tensor Migration and Filtering Improvement mentioned above, more tests were added to ensure the autograd support. Now the following operations are guaranteed to support autograd up to second order.

Functionals

lfilter
allpass_biquad
biquad
band_biquad
bandpass_biquad
bandrefect_biquad
bass_biquad
equalizer_biquad
treble_biquad
highpass_biquad
lowpass_biquad

Transforms

AmplitudeToDB
ComputeDeltas
Fade
GriffinLim
TimeMasking
FrequencyMasking
MFCC
MelScale
MelSpectrogram
Resample
SpectralCentroid
Spectrogram
SlidingWindowCmn
TimeStretch*
Vol

NOTE:

Autograd test for transforms also covers the following functionals.
- amplitude_to_DB
- spectrogram
- griffinlim
- resample
- phase_vocoder*
- mask_along_axis_iid
- mask_along_axis
- gain
- spectral_centroid
torchaudio.transforms.TimeStretch and torchaudio.functional.phase_vocoder call atan2, which is not differentiable around zero. Therefore these functions are differentiable only when the input spectrogram does not contain values around zero.

[Beta] Resampling Improvement

In release 0.8, the resampling operation was vectorized and its performance improved. In this release, the implementation of the resampling algorithm has been further revised.

Kaiser window has been added for a wider range of resampling quality.
rolloff parameter has been added for anti-aliasing control.
torchaudio.transforms.Resample precomputes the kernel using float64 precision and caches it for even faster operation.
New entry point, torchaudio.functional.resample has been added and the original entry point, torchaudio.compliance.kaldi.resample_waveform is deprecated.

The following table illustrates the performance improvements from the previous release by comparing the time it takes for torchaudio.transforms.Resample to complete the operation on float32 tensor with two channels and one-second duration.

CPU

torchaudio version	8k → 16k [Hz]	16k → 8k	16k → 44.1k	44.1k → 16k
0.9	0.192	0.559	0.478	0.467
0.8	0.537	0.753	43.9	17.6

Unit: msec

CUDA

torchaudio version	8k → 16k	16k → 8k	16k → 44.1k	44.1k → 16k
0.9	0.203	0.172	0.213	0.212
0.8	0.860	0.559	116	46.7

Unit: msec

Improved Windows Support

torchaudio implements some operations in C++ for reasons such as performance and integration with third-party libraries. This C++ module was only available on Linux and macOS. In this release, Windows packages also come with C++ module.

This C++ module in Windows package includes the efficient filtering implementation mentioned above, however, “sox_io” backend and torchaudio.functional.compute_kaldi_pitch are not included.

I/O Functions Migration

Since the 0.6 release, we have continuously improved I/O functionality. Specifically, in 0.8 the default backend has been changed from “sox” to “sox_io”, and the similar API change has been applied to “soundfile” backend. The 0.9 release concludes this migration by removing the deprecated backends. For the detail please refer to #903.

Backward Incompatible Changes

I/O

Deprecated backends and functions were removed (#1311, #1329, #1362)
- Please see #903 for the migration.
Added validation of the number of channels when saving GSM (#1384)
- Please make sure that signal has only one channel when saving into GSM.

Ops

Removed deprecated normalized argument from torchaudio.functional.griffinlim (#1369)
- This argument was never used. Please remove the argument from your call.
Renamed torchaudio.functional.sliding_window_cmn arg for correctness (#1347)
- The first argument is supposed to spectrogram. If you have used keyword argument waveform=..., please change it to specgram=...
Changed torchaudio.transforms.Resample to precompute and cache the resampling kernel. (#1499, #1514)
- To use the transform in devices other than CPU, please move the instantiated object to the target device. python resampler = torchaudio.transforms.Resample(orig_freq=8000, new_freq=44100) resampler.to(torch.device("cuda"))

Dataset

Removed deprecated arguments from CommonVoice (#1534)
- torchaudio no longer supports programmatic download of Common Voice dataset. Please remove the arguments from your code.

Deprecations

Deprecated the use of pseudo complex type (#1445, #1492)
- torchaudio is adopting native complex type and the use of pseudo complex type and the related utility functions are now deprecated. Please refer to #1337 for the migration process.
Deprecated torchaudio.compliance.kaldi.resample_waveform (#1533)
- Please use torchaudio.functional.resample.
torchaudio.transforms.MelScale now expects valid n_stft value (#1515)
- Please provide a valid value to n_stft.

New Features

[Beta] Wav2Vec2.0

Added wav2vec2.0 model (#1529)
Added wav2vec2.0 HuggingFace importer (#1530)
Added wav2vec2.0 fairseq importer (#1531)
Added speech recognition C++ example (#1538)
- Please refer to C++ example for the detail.

Filtering

Added C++ implementation of torchaudio.functional.lfilter (#1319)
Added autograd support to torchaudio.functional.lfilter (#1310, #1441)

[Beta] Resampling

Added torchaudio.functional.resample (#1402)
Added rolloff parameter (#1488)
Added kaiser window support to resampling (#1509)
Added kernel caching mechanism in torchaudio.transforms.Resample (#1499, #1514, #1556)
Skip resampling when sampling rate is not changed (#1537)

Native Complex Tensor

Added complex tensor support to torchaudio.functional.phase_vocoder and torchaudio.transforms.TimeStretch (#1410)
Added return_complex to torchaudio.functional.spectrogram and torchaudio.transforms.Spectrogram (#1366, #1551)

Improvements

I/O

Added file path to I/O error messages (#1523)
Added __str__ override to AudioMetaData for easy print (#1339)
Fixed uninitialized variable in sox/utils.cpp (#1306)
Replaced UB sox conversion macros with tensor op (#1370)
Removed check_length from validate_input_file (#1312)

Ops

Added warning for non-integer resampling frequencies (#1490)
Adopted native complex tensors in torchaudio.functional.griffinlim (#1368)
Prohibited scripting torchaudio.transforms.MelScale when n_stft is invalid (#1505)
Added input dimension check to VAD (#1513)
Added HTK-compatible option to Mel-scale conversion (#593)

Models

Added vanilla DeepSpeech model (#1399)

Datasets

Fixed checksum for the YESNO dataset (#1405)

Misc

Added missing transforms to __all__ (#1458)
Removed reference_cast in make_boxed_from_unboxed_functor (#1300)
Removed unused normalized constant from torchaudio.transforms.GriffinLim (#1433)
Removed unused helper function (#1396)

Examples

Added libtorchaudio C++ example (#1349)
Refactored libtorchaudio example (#1486)
Replaced librosa's Mel scale conversion with torchaudio’s in WaveRNN example (#1444)

Build

Updated config.guess to support source build in recent architectures (#1484)
Explicitly disabled wavpack when building SoX (#1462)
Added ROCm support to source build (#1411)
Added Windows C++ binary build (#1345, #1371)
Made kaldi selective in build (#1342)
Made sox selective (#1338)

Testing

Added autograd test for torchaudio.functional.lfilter and biquad variants (#1400, #1438)
Added autograd test for transforms (overview: #1414)
- torchaudio.transforms.FrequencyMasking (#1498)
- torchaudio.transforms.SlidingWindowCmn (#1482)
- torchaudio.transforms.MelScale (#1467)
- torchaudio.transforms.Vol (#1460)
- torchaudio.transforms.TimeStretch (#1420)
- torchaudio.transforms.AmplitudeToDB (#1447)
- torchaudio.transforms.GriffinLim (#1421)
- torchaudio.transforms.SpectralCentroid (#1425)
- torchaudio.transforms.ComputeDeltas (#1422)
- torchaudio.transforms.Fade (#1424)
- torchaudio.transforms.Resample (#1416)
- torchaudio.transforms.MFCC (#1415)
- torchaudio.transforms.Spectrogram / MelSpectrogram (#1340)
Added test for a batch of different items in the functional batch consistency test. (#1315)
Added test for validating torchaudio.functional.lfilter shape (#1360)
Added TorchScript test for torchaudio.functional.resample (#1516)
Added TorchScript test for torchaudio.functional.phase_vocoder (#1379)
Added steps to save and load the scripted object in TorchScript (#1446)
Added GPU support to functional tests (#1475)
Added GPU support to transform librosa compatibility test (#1439)
Added GPU support to functional librosa compatibility test (#1436)
Improved HTTP fetch test reliability (#1512)
Refactored functional batch consistency test (#1341)
Refactored test classes for complex (#1491)
Refactored sox_io load test (#1394)
Refactored Kaldi compatibility tests (#1359)
Refactored functional test (#1435, #1463)
Refactored transform tests (#1356)
Refactored librosa compatibility test (#1350)
Refactored sox compatibility test (#1344)
Refactored librosa compatibility test (#1259)
Removed the use I/O functions in batch consistency test (#1521)
Removed skipIfNoSoxBackend (#1390)
Removed VAD from batch consistency tests (#1451)
Replaced deprecated floor_divide with div (#1455)
Replaced torch.assert_allclose with assertEqual (#1387)
Shortened torchaudio.functional.lfilter autograd tests input size (#1443)
Updated torchaudio.transforms.InverseMelScale comparison test (#1437)

Bug Fixes

Updated torchaudio.transforms.TimeMasking and torchaudio.transforms.FrequencyMasking to perform out-of-place masking (#1481)
Annotate power of torchaudio.transforms.MelSpectrogram as float only (#1572)

Performance

Adopted torch.nn.functional.conv1d in torchaudio.functional.lfilter (#1318)
Added C++ implementation of torchaudio.functional.overdrive (#1299)

Documentation

Update docs (#1550)
Reformat resample docs (#1548)
Updated resampling documentation (#1519)
Added the clarification that sox_effects.apply_effects_tensor is CPU-only (#1459)
Removed instructions on using external sox (#1365, #1281)
Added navigation with left/right arrow keys (#1336)
Fixed docstring of sliding_window_cmn (#1383)
Update contributing guide (#1372)
Fix broken links in contribution guide (#1361)
Added Windows build instructions (#1440)
Fixed typo (#1471, #1397, #1293)
Added WER to readme in wav2letter pipeline (#1470)
Fixed wav2letter usage example (#1060)
Added Google Analytics support (#1466)

- Python
Published by mthrok over 4 years ago

gladia-torchaudio - v0.8.1

Highlights

This release depends on pytorch 1.8.1.

Bug Fixes

Added back support for 24-bit signed LPCM wav via sox_io backend. (#1389)

- Python
Published by vincentqb almost 5 years ago

gladia-torchaudio - v0.8.0

Highlights

This release supports Python 3.9.

I/O Improvements

Continuing from the previous release, torchaudio improves the audio I/O mechanism. In this release, we have four major updates.

Backend migration. We have migrated the default backend for audio I/O. The new default backend is “soxio” (for Linux/macOS). The interface for “soundfile” backend has been also changed to align that of “soxio”. Following the change of default backends, the legacy backend/interface have been marked as deprecated. The legacy backend/interface are still accessible, though it is strongly discouraged to use them. For the detail on the migration, please refer to #903.
File-like object support. We have added file-like object support to I/O functions and soxeffects. You can perform the info, load, save and `applyeffects_fileoperation on file-like objects.``python

Query audio metadata over HTTP

Will only fetch the first few kB

with requests.get(URL, stream=True) as response: metadata = torchaudio.info(response.raw)

Load audio from tar file

No need to extract TAR file.

with tarfile.open(TARPATH, mode='r') as tarfile: fileobj = tarfile.extractfile(SAMPLETARITEM) waveform, samplerate = torchaudio.load(fileobj)

Saving to Bytes buffer

Using BytesIO, you can perform in-memory encoding/decoding.

buffer_ = io.BytesIO() torchaudio.save(buffer, waveform, samplerate, format="wav")

Apply effects (lowpass filter / resampling) while loading audio from S3

client = boto3.client('s3') response = client.getobject(Bucket=S3BUCKET, Key=S3KEY) waveform, samplerate = torchaudio.soxeffects.applyeffect_file( response['Body'], [["lowpass", "-1", "300"], ["rate", "8000"]]) ```
[Beta] Codec Application. Built upon the file-like object support, we added functional.apply_codec function, which can degrades audio data by applying audio codecs supported by “sox_io” backend, in in-memory fashion. ```python

Apply MP3 codec

degraded = F.applycodec( waveform, samplerate, format="mp3", compression=-9)

Apply GSM codec

degraded = F.applycodec(waveform, samplerate, format="gsm") ```
Encoding options. We have added encoding options to save function of new backends. Now you can change the format and encodings with format, encoding and bits_per_sample options ```python

Save without any encoding option.

The function will pick the encoding which the provided data fit

For Tensor of float32 type, that is 32-bit floating-point PCM.

torchaudio.save("data.wav", waveform, sample_rate)

Save as 16-bit signed integer Linear PCM

The resulting file occupies half the storage but loses precision

torchaudio.save( "data.wav", waveform, samplerate, encoding="PCMS", bitspersample=16) ```
More format support to "soxio"’s save function. We have added support for GSM, HTK, AMB, and AMR-NB formats to "soxio"’s save function.

Switch to CMake-based build

torchaudio was utilizing CMake to build third party dependencies. Now torchaudio uses CMake to build its C++ extension. This will open the door to integrate torchaudio in non-Python environments (such as C++ applications and mobile). We will work on adding example applications and mobile integrations in upcoming releases.

Backwards Incompatible Changes

Removed deprecated transform and target_transform arguments from VCTK and YESNO datasets. (#1120) If you were relying on the previous behavior, we recommend that you apply the transforms in the collate function.
Removed torchaudio.datasets.utils.walk_files (#1111) and replaced by Path and glob. (#1069, #1101). If you relied on the function, we recommend that you use glob instead.
Removed torchaudio.data.utils.unicodecsvreader. (#1086) If you relied on the function, we recommend that you replace by csv.reader.
Disabled CommonVoice download as users are required to sign user agreement. Please download and extract the dataset manually, and replace the root argument by the subfolder for the version and language of interest, see #1082 for more details. (#1018, #1079, #1080, #1082)
Removed legacy sox effects (#977, #1001). Please migrate to applyeffectsfile or applyeffectstensor.
Switched the default backend to the ones with new interfaces (#978). If you were relying on the previous behavior, you can return to the previous behavior by following instructions in #975 for one more release.

New Features

Added GSM, HTK, AMB, AMR-NB and AMR-WB format support to “sox_io” backend. (#1276, #1291, #1277, #1275, #1066)
Added encoding options (format, bitspersample and encoding) to save function. (#1226, #1177, #1129, #1104)
Added new attributes (bitspersample and encoding) to the info function return type (AudioMetaData) (#1177, #1206, #1324)
Added format override to libsox-based file input. (load, info, soxeffects.applyeffects_file) (#1104)
Added file-like object support in “soxio”, and “soundfile” backend and soxeffects.applyeffectsfile. (#1115)
[Beta] Added the Kaldi Pitch feature. (#1243, #1260)
[Beta] Added the SpectralCentroid transform. (#1167, #1216, #1316)
[Beta] Added codec transformation apply_codec. (#1200)

Improvements

Exposed normalization method to Mel transforms. (#1212)
Exposed additional STFT arguments to Spectrogram (#892) and to MelSpectrogram (#1211).
Added support for pathlib.Path to applyeffectsfile (#1048) and to CMUARCTIC (#1025), YESNO (#1015), COMMONVOICE (#1027), VCTK and LJSPEECH (#1028), GTZAN (#1032), SPEECHCOMMANDS (#1039), TEDLIUM (#1045), LIBRITTS and LIBRISPEECH (#1046).
Added SpeechCommands train/valid/test split. (#966, #1012)

Internals

Replaced if-elseif-else with switch in sox C++ code. (#1270)
Refactored C++ interface for soxio's getinfofile (#1232) and getencodinginfo (#1233).
Add explicit functional import in init. (#1228)
Refactored YESNO dataset (#1127), LJSPEECH dataset (#1143).
Removed Python 2.7 reference from setup.py. (#1182)
Merged flake8 configurations into single .flake8 file. (#1172, #1214)
Updated calls to torch.stft to use return_complex=True. (#1096, #1013)
Cleaned up handling of optional args in C++ with c10:optional. (#1043)
Removed unused imports in sox effects. (#1052)
Introduced functional submodule to organize functionals. (#1003)
[Testing] Refactored MelSpectrogram librosa compatibility test to decouple from other tests. (#1267)
[Testing] Moved batch tests for functionals. (#1254)
[Testing] Refactored tests for backend (#1239) and for functionals (#1237).
[Testing] Removed dependency on pytest from testing (#1157, #1188)
[Testing] Refactored unitests for VCTK (#1134), SPEECHCOMMANDS (#1136), LIBRISPEECH (#1140), TEDLIUM (#1135), LJSPEECH (#1138), LIBRITTS (#1139), CMUARCTIC (#1147), GTZAN(#1148), COMMONVOICE and YESNO (#1133).
[Testing] Removed dependency on COMMONVOICE dataset from tests. (#1132)
[Build] Fixed Python 3.9 support (#1242)
[Build] Switched to cmake for build. (#1187, #1246, #1249)
[Build] Restructured C++ code to allow per file registration of custom ops. (#1221)
[Build] Added logging to sox/CMakeLists.txt. (#1190)
[Build] Disabled C++11 ABI when necessary for libtorch compatibility. (#880)
[Build] Reorganized libsox source and build directory to accommodate additional third party code. (#1161, #1176)
[Build] Refactored sox source files and moved into dedicated subfolder. (#1106)
[Build] Enabled custom clean function for python setup.py clean. (#1142)
[CI] Documented undocumented parameters. Added CI check. (#1248)
[CI] Fixed sphinx warnings in documentation. Turned warnings into errors. (#1247)
[CI] Print CPU info before running unit test. (#1218)
[CI] Fixed clang-format job and fixed newly detected formatting issues. (#981, #1198, #1222)
[CI] Updated unit test base Docker image. (#1193)
[CI] Disabled CCI cache which is now known to be flaky. (#1189)
[CI] Disabled torchscript BC test which is known to fail. (#1192)
[CI] Stripped version suffix for pytorch. (#1185)
[CI] Ran smoke test with CPU package for pytorch due to known issue with CUDA 11. (#1105)
[CI] Added missing empty line at the end of config.yml. (#1020)
[CI] Added automatic documentation build and push to branch in CI. (#1006, #1034, #1041, #1049, #1091, #1093, #1098, #1100, #1121)
[CI] Ran GPU test for all pull requests and fixed current setup. (#998, #1014, #1191)
[CI] Skipped tests that is known to fail on macOS Python 3.6/3.7. (#999)
[CI] Changed the order of installation and aligned with Windows. (#987)
[CI] Fixed documentation rendering by using Sphinx 2.4.4. (#974)
[Doc] Added subcategories to functional documentation. (#1325)
[Doc] Added a version selector in documentation. (#1273)
[Doc] Updated compilation recommendation in README. (#1263)
[Doc] Added CONTRIBUTING.md. (#1241)
[Doc] Added instructions to install parametrized package. (#1164)
[Doc] Fixed the return type for load functions. (#1122)
[Doc] Added missing modules and minor fixes. (#1022, #1056, #1117)
[Doc] Fixed spelling and links in README. (#1029, #1037, #1062, #1110, #1261)
[Doc] Grouped filtering functionals in documentation page. (#1005, #1004)
[Doc] Updated the compatibility matrix with torchaudio 0.7 (#979)
[Doc] Added description of prototype/beta/stable features. (#968)

Bug Fixes

Fixed amplitudetoDB clamping behaviour on batches. (#1113)
Disabled audio devices in sox builds which could interfere in the build process when detected. (#1153)
Fixed COMMONVOICE for French where the audio file extension was missing on load. (#1126)
Disabled OpenMP support for libsox which can produce errors when used in DataLoader. (#1026)
Fixed noisedowntime argument in VAD by properly propagating it. (#1017)
Removed print-freq option to compute validation loss at each epoch in wav2letter pipeline. (#997)
Migrated from torch.rfft to torch.fft.rfft and cfloat following change in pytorch. (#941)
Fixed interactive ASR demo to aligned with latest version of FAIRSeq. (#996)

Deprecations

The normalized argument is unused and will be removed from griffinlim. (#1036)
The previous sox and soundfile backend remain available for one release, see #903 for details. (#975)

Performance

Added C++ lfilter core loop for faster iteration on CPU. (#1244)
Leveraged julius resampling implementation to make resampling faster. (#1087)

- Python
Published by vincentqb almost 5 years ago

gladia-torchaudio - v0.7.2

Highlights

This release introduces support for python 3.9. There is no 0.7.1 release, and the following changes are compared to 0.7.0.

Improvements

Add python 3.9 support (#1061)

Bug Fixes

Temporarily disable OpenMP support for libsox (#1054)

Deprecations

Disallow download=True in CommonVoice (#1076)

- Python
Published by vincentqb about 5 years ago

gladia-torchaudio - v0.7.0

Highlights

Example Pipelines

torchaudio is expanding its support for models and end-to-end applications. Please file an issue on github to provide feedback on them.

Speech Recognition: Building on the addition of the Wav2Letter model for speech recognition in the last release, we added a training example pipelines for speech recognition that uses the LibriSpeech dataset.
Text-to-Speech: With the goal of supporting text-to-speech applications, we added a vocoder based on the WaveRNN model. WaveRNN model is based on the implementation from this repository. The original implementation was introduced in "Efficient Neural Audio Synthesis". We provide an example training pipeline in the example folder that uses the LibriTTS dataset added to torchaudio in this release.
Source Separation: We also support source separation with the addition of the ConvTasNet model, based on the paper "Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation." An example training pipeline is provided with the wsj0-mix dataset.

I/O Improvements

As you are likely already aware from the last release we’re currently in the process of making sox_io, which ships with new features such as TorchScript support and performance improvements, the new default. If you want to benefit from these features now, we encourage you to migrate. For more information see issue #903.

Backwards Incompatible Changes

Switched all %-based string formatting to str.format to adopt changes in PyTorch, leading to improved error messages for TorchScript (#850)
Split sox_utils.list_formats() for read and write (#811)
Made directory traversal order alphabetical and breadth-first, consistent across operating systems (#814)
Changed GTZAN so that it only traverses filenames belonging to the dataset (#791)

New Features

Added ConvTasNet model (#920, #933) with pipeline (#894)
Added canonical pipeline with wav2letter (#632)
The WaveRNN model (#705, #797, #801, #810, #836) is available with a canonical pipeline (#749, #802, #831, #863)
Added all 3 releases of tedlium dataset (#882, #934, #945, #895)
Added VCTK_092 dataset (#812)
Added LibriTTS (#790, #820)
Added SPHERE support to sox_io backend (#871)
Added torchscript sox effects (#760)
Added a flag to change the interface of soundfile backend to the one identical to sox_io backend. (#922)

Improvements

Added soundfile compatibility backend. (#922)
Improved the speed of torchaudio.compliance.kaldi.fbank (#947)
Improved the speed of phaser (#660)
Added warning when a Mel filter is all zero (#914)
Added pathlib.Path support to sox_io backend (#907)
Simplified C++ registration with TORCH_LIBRARY (#840)
Merged sox effect and sox_io C++ implementation (#779)

Internal

CI: Added test to validate torchscript backward compatibility (#838)
CI: Used mocked datasets to test CMUArctic (#829), CommonVoice (#827), Speech Commands (#824), LJSpeech (#826), LibriSpeech (#825), YESNO (#792, #832)
CI: Made *nix unit test fail if C++ extension is not available (#847, #849)
CI: Separated I/O in testing. (#813, #773, #783)
CI: Added smoke tests to sox_io and sox_effects (#806)
CI: Tested utilities have been refactored (#805, #808, #809, #817, #822, #831)
Doc: Added how to run tests (#843)
Doc: Added 0.6.0 to version matrix in README (#833)

Bug Fixes

Fixed device in interactive ASR example (#900)
Fixed incorrect extension parsing (#885)
Fixed dither with noise_shaping = True (#865)
Run unit test with non-editable installation (#845), and set zip_safe = False to disable egg installation (#842)
Sorted GTZAN dataset and use on-the-fly data in GTZAN test (#819)

Deprecations

Removed istft wrapper in favor of torch.istft. (#841)
Deprecated SoxEffect and SoxEffectsChain (#787)
I/O: Deprecated sox backend. (#904)
I/O: Deprecated the current interface of soundfile. (#922)
I/O: Deprecated load_wav functions. (#905)

- Python
Published by vincentqb over 5 years ago

gladia-torchaudio - v0.6.0

Highlights

torchaudio now includes a new model module (with wav2letter included), new functionals (contrast, cvm, dcshift, overdrive, vad, phaser, flanger, biquad), datasets (GTZAN, CMU), and a new optional sox backend with support for torchscript. torchaudio now also supports Windows, with the soundfile backend.

torchaudio requires python 3.6 or more recent.

Backwards Incompatible Changes

We reorganized the C++ resources (#630) and replaced C++ bindings for sox_effects init/list/shutdown with torch binding (#748).
We removed code specific to python 2 (#691), and we no longer tests against python 2 (#575) and 3.5 (#577)

New Features

We now support Windows. (#604, #637, #642, #655, #743)
We now have a model module which includes wav2letter. (#462, #722)
We added the GTZAN and CMU datasets. (#668, #710)
We now have the contrast functional (#551), cvm (#540), dcshift (#558), overdrive (#569), vad (#578, #599), phaser (#587, #607, #702), flanger (#651, #702), biquad (#661).
We added a new sox_io backend (#718, #728, #734, #727, #763, #752, #731, #732, #726, #780) that is compatible with torchscript with a new AudioMetaData class (#761).
MelSpectrogram now has power and normalized parameters (#633), and slaney normalization (#589, #641).
lfilter now has a clamp option. (#600)
Griffin-Lim can now have zero momentum. (#601)
slidingwindowcmn now supports batching. (#570)
Downloaded datasets now verify checksums. (#499)

Improvements

We added ogg/vorbis/opus support to binary distribution (#750, #755).
We replaced the use of torch.norm in spectrogram to improve performance (#747).
We now use fused operations in lfilter for faster computation. (#517, #564)
STFT is now called directly from torchaudio. (#531)
We redesigned the backend mechanism to support torchscript, by restructuring the code (#695, #696, #700, #706, #707, #698), adding dynamic listing (#697)
torchaudio can be built along with sox, or can use external sox. (#625, #669, #739)
We redesigned the sox_effects module. (#708)
We added more details to compilation instructions. (#667)
We updated the README with instructions on changing the backend. (#553)
We now have a version compatibility matrix in README. (#685)
We now use cmake to build third party libraries (#753).
We now use CircleCI instead of travis (#576, #584, #598, #603, #636, #738) and we test on GPU (#586, #777).
We run the test suite against nightlies. (#538, #678)
We redesigned our test suite: with new helper functions (#514, #519, #521, #565, #616, #690, #692, #694), standard pytorch test utilities (#513, #640, #643, #645, #646, #652, #650, #712), separated CPU and GPU tests (#513, #528, #644), more descriptive names (#532), clearer organization (#539, #541, #542, #664, #672, #687, #703, #716, #732), standardized name (#559), and backend aware (#719). This is detailed in a new README for testing (#566, #759).
We now support typing, for datasets (#511, #522), for backends (#527), for init (#526), and inline (#530), with mypy configuration (#524, #544, #590).

Bug Fixes

We removed in place operations so that Griffin-Lim can be backpropagated through. (#730)
We fixed kaldi MFCC on GPU. (#681)
We removed multiple definitions of SoxEffect in C++. (#635)
We fixed the docstring of masking. (#612)
We replaced views by reshape for batching. (#594)
We fixed missing conda environment when testing in python 3.8. (#582)
We ensure that sox is not exposed in windows. (#579)
We corrected the instructions to install nightlies. (#547, #552)
We fix the seed of maskalongiid. (#529)
We correctly report GPU tests as skipped instead of passed. (#516)

Deprecations

Since sox_effects is now automatically initialized and shutdown (#572, #693), we are deprecating these functions (#709).
ISTFT is migrating to torch. (#523)

- Python
Published by vincentqb over 5 years ago

gladia-torchaudio - v0.5.1

Highlights

Updated pinned version of PyTorch to v1.5.1

- Python
Published by seemethere over 5 years ago

gladia-torchaudio - v0.5.0

Highlights

torchaudio includes new transforms (e.g. Griffin-Lim and inverse Mel scale), new filters (e.g. all pass, fade, band pass/reject, band, treble, deemph, riaa), and datasets (LJ Speech and SpeechCommands).

Backwards Incompatible Changes

torchaudio no longer supports python 2. We removed future and six imports. We added inline typing. (#413, #478, #479, #482, #486)
We fixed CommonVoice dataset download, and updated to the latest version. (#498)
We now skip data point with missing data in VCTK dataset. (#484)

New Features

We now have the Vol transforms, and DBtoamplitude.(#468, #469)
We now have the InverseMelScale (#448)
We now have the Griffin-Lim functional. (#365)
We now support allpass, fade, bandpass, bandreject, band, treble, deemph, riaa. (#444, #449, #464, #470, #508)
We now offer LJSpeech and SpeechCommands datasets. (#439, #437)

Improvements

We added inline typing to SoxEffects and Kaldi compliance. (#490, #497)
We refactored the tests. (#480, #485, #496, #491, #501, #502, #503, #506, #507, #509)
We now run tests with sox only when sox is available. (#419)
We extended batch support to MelScale, MelSpectrogram, MFCC, Resample. (#391, #435)
The speed of torchaudio.functional.istft was improved. (#471)
We now have transform and functional tests for AmplitudeToDB. (#463)
We now ignore pycharm and OSX files in git. (#461)
TimeStretch now has a batch test. (#459)
Docstrings in transforms were polished. (#442)
TimeStretch and AmplitudeToDB are now torch.nn.Module. (#456)
Resample is now jitable. (#441)
We support python 3.8. (#397)
Add cuda test for complex norm. (#421)
Dither is jitable with the latest version of pytorch. (#417)
Batching uses view instead of reshape. (#409)
We refactored the jitability test. (#395)
In .circleci, we removed a conditional block that wasn't doing anything. (#399)
We now have Windows CI for building. (#394 and #398)
We corrected the use of standard variable names in code. (#393)
We adopted native-Python code generation convention. (#378)
torchaudio.istft creates tensors directly on device. (#377)
torchaudio.compliance.kaldi.resample_waveform is now jitable. (#362)
The runtime of torchaudio.functional.lfilter was decreased. (#374)

Bug Fixes

We fixed flake8 errors. (#504, #505)
We fixed Windows test by only testing with cpu-only binaries. (#489)
Spelling correction in docstrings for transforms.FrequencyMasking and transforms.TimeMasking. (#474)
In .circleci, we switched to use token for conda uploads. (#460)
The default value of dither parameter was changed. (#453)
TimeStretch moves device correctly. (#457)
Adding dev-other option in librispeech. (#433)
In build script, we install the correct version of pytorch for pip. (#412)
Upgrading dataset DeprecationWarning to UserWarning so that the user gets the warning. (#402)
Make power of spectrogram a float to work with complex norm. (#392)
Fix random seed for flaky test_griffinlim test. (#388)
Apply 'nightly' branch filter to binary uploads. (#385)
Fixed build errors: added explicitly utf8 decoration, added explicit utf8encoder definition if not available, explicitly cast to int. (#380)

Deprecations

None

- Python
Published by vincentqb almost 6 years ago

gladia-torchaudio - v0.4.0

torchaudio 0.4 improves on current transformations, datasets, and backend support.

We introduce an interactive speech recognition demo. (#266, #229, #248)
SoX is now optional, and a new extensible backend dispatch mechanism exposes SoundFile as an alternative to SoX.
The interface for datasets has been unified. This enables the addition of two large datasets: LibriSpeech and Common Voice.
New filters such as biquad, data augmentation such as time and frequency masking, and transforms such as gain and dither, and new feature computation such as deltas, are now available.
Transformations now support batches and are jitable.

We would like to thank again our contributors and the wider community for their significant contributions to this release. In particular we'd like to thank @keunwoochoi, @ksanjeevan, and all the other maintainers and contributors of torchaudio-contrib for their significant and valuable additions around augmentations (#285) and batching (#327).

Breaking Changes

torchaudio now requires PyTorch 1.3.0 or newer, see https://pytorch.org/ for installation instructions. (#312)
We make jit compilation optional for functions and use nn.Module where possible. (#314, #326, #342, #369)
By unifying the interface for datasets, we changed the interface for VCTK and YESNO (#303, #316). In particular, the construction parameters downsample, transform, target_transform, and return_dict are being deprecated.
SoxEffectsChain.EFFECTSAVAILABLE replaced by SoxEffectsChain().EFFECTSAVAILABLE (#355)
This is the last version to support Python 2.

New Features

SoX is now optional, and a new extensible backend dispatch mechanism exposes SoundFile as an alternative to SoX. This makes it possible to use torchaudio even when SoX or SoundFile are not installed or available. (#355)
We now have a unified dataset interface that loads in memory only one item at a time enabling new large datasets: LibriSpeech and CommonVoice. (#303, #316, #330)
We introduce a pitch detection algorithm: torchaudio.functional.detect_pitch_frequency. (#313, #322)
We offer data augmentations in torchaudio.transforms: TimeStretch, FrequencyMasking, TimeMasking. (#285, #333, #348)
We introduce a complex norm transform: torchaudio.transform.ComplexNorm. (#285, #333)
We now have a new audio feature generation for computing deltas: torchaudio.functional.compute_deltas. (#268, #326)
We introduce torchaudio.functional.gain and torchaudio.functional.dither (#319, #360). We welcome work to continue the effort to implement features available in SoX, see #260.
We now include equalizer_biquad (#315, #340), lowpass_biquad, highpass_biquad (#275), lfilter, and biquad (#275, #291, #326) in torchaudio.functional.
MFCC is available as torchaudio.functional.mfcc. (#228)

Improvements

We now support batching in transforms. (#327, #337, #404)
Functions are now jitable, and nn.Module is used where possible. (#314, #326, #342, #362, #369, #395)
Downloads of large files are now automatically resumed with new download function. (#320)
New tests for ISTFT are added. (#279)
We introduce nightly builds. (#301)
We now have smoke tests for builds. (#346, #359)

Bug Fixes

Fix mismatch between MelScale and librosa. (#294)
Fix torchaudio.compliance.kaldi.resample_waveform where internal variables where not moved to the GPU when used. (#277)
Fix a bug that occurred when importing torchaudio built outside of a git repository. (#276)
Fix istft where the dtype and device of parameters were not created on the same device as the tensor provided by the user. (#264)
Fix size mismatch when saving and loading from state dictionary (load_state_dict). (#246)
Clarified internal naming convention within transforms and functionals. (#298)
Fix build script to be more tolerant to download drops. (#280, #284, #305)
Correct documentation for SoxEffectsChain. (#283)
Fix resample error with cuda tensors. (#277)
Fix error when importing version outside of git. (#276)
Fix missing asound in linux build. (#254)
Fix deprecated torch. (#254)
Fix link in README. (#253)
Fix window device in ISTFT. (#240)
Documentation: Fix range in documentation for torchaudio.load to [-1, 1]. (#283)

- Python
Published by vincentqb about 6 years ago

gladia-torchaudio - v0.3.2

This release is to update the dependency to PyTorch 1.3.1.

- Python
Published by vincentqb about 6 years ago

gladia-torchaudio - v0.3.1

This release is to update the dependency to PyTorch 1.3.0.

Minor Fix

Updated settings for curl in build scripts (#280, #284, #297).

- Python
Published by vincentqb about 6 years ago

gladia-torchaudio - v0.3.0 Standardization, JIT/CUDA Support, Kaldi Compliance Interface, ISTFT

Highlights

torchaudio as an extension of PyTorch

torchaudio has been redesigned to be an extension of PyTorch and part of the domain APIs (DAPI) ecosystem. Domain specific libraries such as this one are kept separated in order to maintain a coherent environment for each of them. As such, torchaudio is an ML library that provides relevant signal processing functionality, but it is not a general signal processing library. The full rationale of this new standardization can be found in the README.md.

In light of these changes some transforms have been removed or have different argument names and conventions. See the section on backwards breaking changes for a migration guide.

We provide binaries via pip and conda. They require PyTorch 1.2.0 and newer. See https://pytorch.org/ for installation instructions.

Community

We would like to thank our contributors and the wider community for their significant contributions to this release. We are happy to see an active community around torchaudio and are eager to further grow and support it.

In particular we'd like to thank @keunwoochoi, @ksanjeevan, and all the other maintainers and contributors of torchaudio-contrib for their significant and valuable additions around standardization and the support of complex numbers (https://github.com/pytorch/audio/pull/131, https://github.com/pytorch/audio/issues/110, https://github.com/keunwoochoi/torchaudio-contrib/issues/61, https://github.com/keunwoochoi/torchaudio-contrib/issues/36).

Kaldi Compliance Interface

An implementation of basic transforms with a Kaldi-like interface.

We added the functions spectrogram, fbank, and resample_waveform (https://github.com/pytorch/audio/pull/119, https://github.com/pytorch/audio/pull/127, and https://github.com/pytorch/audio/pull/134). For more details see the documentation on torchaudio.compliance.kaldi which mirrors the arguments and outputs of Kaldi features.

As an example we can look at the sinc interpolation resampling similar to Kaldi’s implementation. In the figure below, the blue dots are the original signal and red dots are the downsampled signal with half the original frequency. The red dot elements are approximately every other original element.

resampling

python specgram = torchaudio.compliance.kaldi.spectrogram(waveform, frame_length=...) fbank = torchaudio.compliance.kaldi.fbank(waveform, num_mel_bins=...) resampled_waveform = torchaudio.compliance.kaldi.resample_waveform(waveform, orig_freq=...)

Inverse short time Fourier transform

Constructing a signal from a spectrogram can be used in applications like source separation or to generate audio signals to listen to. More specifically torchaudio.functional.istft is the inverse of torch.stft. It has the same parameters (+ additional optional parameter of length) and returns the least squares estimation of an original signal.

```python torch.manualseed(0) nfft = 5 waveform = torch.rand(2, 5) stft = torch.stft(waveform, nfft=nfft) approxwaveform = torchaudio.functional.istft(stft, nfft=n_fft, length=waveform.size(1))

waveform tensor([[0.4963, 0.7682, 0.0885, 0.1320, 0.3074], [0.6341, 0.4901, 0.8964, 0.4556, 0.6323]]) approx_waveform tensor([[0.4963, 0.7682, 0.0885, 0.1320, 0.3074], [0.6341, 0.4901, 0.8964, 0.4556, 0.6323]]) ```

Breaking Changes

Removed Compose: Please use core abstractions such as nn.Sequential() or a for-loop over a list of transforms.
SPECTROGRAM, F2M, and MEL have been removed. Please use Spectrogram, MelScale, and MelSpectrogram
Removed formatting transforms ( LC2CL and BLC2CBL): While the LC layout might be common in signal processing, support for it is out of scope of this library and transforms such as LC2CL only aid their proliferation. Please use transpose if you need this behavior.
Removed Scale, PadTrim, DownmixMono: Please use division in place of Scale torch.nn.functional.pad/trim in place of PadTrim , torch.mean on the channel dimension in place of DownmixMono.
torchaudio.legacy has been removed. Please use torchaudio.load and torchaudio.save
Spectrogram used to be of dimension (channel, time, freq) and is now (channel, freq, time). Similarly for MelScale, MelSpectrogram, and MFCC, time is the last dimension. Please see our README for an explanation of the rationale behind these changes. Please use transpose to get the previous behavior.
MuLawExpanding was renamed to MuLawDecoding as the inverse of MuLawEncoding ( https://github.com/pytorch/audio/pull/159)
SpectrogramToDB was renamed to AmplitudeToDB ( https://github.com/pytorch/audio/pull/170). The input does not necessarily have to be a spectrogram and as such can be used in many more cases as the name should reflect.

New Features

torchaudio.compliance.kaldi.spectrogram (https://github.com/pytorch/audio/pull/119)
torchaudio.compliance.kaldi.fbank (https://github.com/pytorch/audio/pull/127 )
torchaudio.compliance.kaldi.resample_waveform (https://github.com/pytorch/audio/pull/134)
torchaudio.transforms.Resample(https://github.com/pytorch/audio/pull/134)
torchaudio.functional.istft ( https://github.com/pytorch/audio/pull/135 )
torchaudio.functional.complex_norm (https://github.com/pytorch/audio/pull/131)
torchaudio.functional.angle (https://github.com/pytorch/audio/pull/131)
torchaudio.functional.magphase (https://github.com/pytorch/audio/pull/131)
torchaudio.functional.phase_vocoder (https://github.com/pytorch/audio/pull/131)

Performance

JIT and CUDA

JIT support added to Spectrogram, AmplitudeToDB, MelScale, MelSpectrogram, MFCC, MuLawEncoding, and MuLawDecoding. (https://github.com/pytorch/audio/pull/118)
CUDA support added to Spectrogram, AmplitudeToDB, MelScale, MelSpectrogram, MFCC, MuLawEncoding, and MuLawDecoding (https://github.com/pytorch/audio/pull/118)

Bug Fixes

Fix test_transforms.py where double tensors were compared with floats (https://github.com/pytorch/audio/pull/132)
- Fix vctk.read_audio (issue https://github.com/pytorch/audio/issues/143) as there were issues with downsampling using SoxEffectsChain (https://github.com/pytorch/audio/pull/145)
Fix segfault passing null to sox_close (https://github.com/pytorch/audio/pull/174)

- Python
Published by jamarshon over 6 years ago

gladia-torchaudio - torchaudio's First Official Release (v0.2.0)

Background

The goal of this release is to fix the current API as there will be future changes that breaking backward compatibility in order to improve the library as more thought is given to design, capabilities, and usability.

While this release is compatible with all currently known PyTorch versions (<=1.2.0), the available binaries will only require Pytorch 1.1.0. Installation commands:

```bash

Wheels for Python 2 are NOT supported

Python 3.5

$ pip3 install http://download.pytorch.org/whl/torchaudio-0.2-cp35-cp35m-linuxx8664.whl

Python 3.6

$ pip3 install http://download.pytorch.org/whl/torchaudio-0.2-cp36-cp36m-linuxx8664.whl

Python 3.7

$ pip3 install http://download.pytorch.org/whl/torchaudio-0.2-cp37-cp37m-linuxx8664.whl ```

What's new?

Fixed broken tests and setup automatic testing environment
Read in Kaldi files (“.ark”, “.scp”)
Separation of state and computation into transforms.py and functional.py
Loading and saving to file
Datasets VCTK and YESNO
SoxEffects and SoxEffectsChain in torchaudio.sox_effects

CI and Testing

A continuous integration (Travis CI) has been setup in https://github.com/pytorch/audio/pull/117. This means all the tests have been fixed and their status can be checked in https://travis-ci.org/pytorch/audio. The test files have to be run separately via buildtools/travis/testscript.sh because closing sox after a test file is completed prevents it from being reopened. The testing framework is pytest.

```bash

Run the whole test suite

$ buildtools/travis/testscript.sh

Run an individual test

$ python -m pytest test/test_transforms.py ```

Kaldi IO

Kaldi IO has been added as an optional dependency in https://github.com/pytorch/audio/pull/111. torchaudio provides a simple wrapper around this by converting the np.ndarray into torch.Tensor. Functions include: read_vec_int_ark, read_vec_flt_scp, read_vec_flt_ark, read_mat_scp, and read_mat_ark.

```python

read ark to a 'dictionary'

d = { u:d for u,d in torchaudio.kaldiio.readvecintark(file) } ```

Separation of State and Computation

In https://github.com/pytorch/audio/pull/105, the computations have been moved into functional.py. The reasoning behind this is that tracking state is a separate problem by itself and should be separate from computing a function. It also allows us to annotate the functional as weak scriptable, which in turn allows us to utilize the JIT and create efficient code. The functional itself might then also be used by other functionals, which is much easier and more efficient than having another Module create an instance of the class. This also makes it easier to implement performance improvements and create a generic API. If someone implements a function that adheres to the contract of your functional, it can be an immediate drop-in. This is important if we want to support different backends (e.g. move a functional entirely into C++).

```python

torchaudio.transforms.Spectrogram(n_fft=...)(waveform) torchaudio.functional.spectrogram(waveform, …) ```

Loading and saving to file

Tensors can be read and written to various file formats (e.g. “mp3”, “wav”, etc.) through torchaudio. python sound, sample_rate = torchaudio.load(‘input.wav’) torchaudio.save(‘output.wav’, sound)

Transforms and functionals

Transforms ```python class Compose(object): def init(self, transforms): def call(self, audio):

class Scale(object): def init(self, factor=2**31): def call(self, tensor):

class PadTrim(object): def init(self, maxlen, fillvalue=0, channelsfirst=True): def _call__(self, tensor):

class DownmixMono(object): def init(self, channelsfirst=None): def _call__(self, tensor):

class LC2CL(object): def call(self, tensor):

def SPECTROGRAM(args, *kwargs):

class Spectrogram(object): def init(self, nfft=400, ws=None, hop=None, pad=0, window=torch.hannwindow, power=2, normalize=False, wkwargs=None): def call(self, sig):

def F2M(args, *kwargs):

class MelScale(object): def init(self, nmels=128, sr=16000, fmax=None, fmin=0., nstft=None): def call(self, spec_f):

class SpectrogramToDB(object): def init(self, stype="power", topdb=None): def _call__(self, spec):

class MFCC(object): def init(self, sr=16000, nmfcc=40, dcttype=2, norm='ortho', logmels=False, melkwargs=None): def _call__(self, sig):

class MelSpectrogram(object): def init(self, sr=16000, nfft=400, ws=None, hop=None, fmin=0., fmax=None, pad=0, nmels=128, window=torch.hannwindow, wkwargs=None): def _call__(self, sig):

def MEL(args, *kwargs):

class BLC2CBL(object): def call(self, tensor):

class MuLawEncoding(object): def init(self, quantizationchannels=256): def _call__(self, x):

class MuLawExpanding(object): def init(self, quantizationchannels=256): def _call_(self, xmu): ```

Functional ```python def scale(tensor, factor): # type: (Tensor, int) -> Tensor

def padtrim(tensor, chdim, maxlen, lendim, fill_value): # type: (Tensor, int, int, int, float) -> Tensor

def downmixmono(tensor, chdim): # type: (Tensor, int) -> Tensor

def LC2CL(tensor): # type: (Tensor) -> Tensor

def spectrogram(sig, pad, window, n_fft, hop, ws, power, normalize): # type: (Tensor, int, Tensor, int, int, int, int, bool) -> Tensor

def createfbmatrix(nstft, fmin, fmax, nmels): # type: (int, float, float, int) -> Tensor

def melscale(specf, fmin, fmax, n_mels, fb=None): # type: (Tensor, float, float, int, Optional[Tensor]) -> Tuple[Tensor, Tensor]

def spectrogramtoDB(spec, multiplier, amin, dbmultiplier, topdb=None): # type: (Tensor, float, float, float, Optional[float]) -> Tensor

def createdct(nmfcc, n_mels, norm): # type: (int, int, string) -> Tensor

def MFCC(sig, melspect, logmels, s2db, dct_mat): # type: (Tensor, MelSpectrogram, bool, SpectrogramToDB, Tensor) -> Tensor

def BLC2CBL(tensor): # type: (Tensor) -> Tensor

def mulawencoding(x, qc): # type: (Tensor, int) -> Tensor

def mulawexpanding(x_mu, qc): # type: (Tensor, int) -> Tensor ```

Datasets VCTK and YESNO

All datasets are subclasses of torch.utils.data.Dataset i.e, they have __getitem__ and __len__ methods implemented. Hence, they can all be passed to a torch.utils.data.DataLoader which can load multiple samples parallelly using torch.multiprocessing workers. For example: ```python yesnodata = torchaudio.datasets.YESNO('.', download=True) dataloader = torch.utils.data.DataLoader(yesnodata, batchsize=1, shuffle=True, num_workers=args.nThreads)

``` The two datasets available are VCTK and YESNO. They download the datasets and preprocess them so that the loaded data is in convenient format.

SoxEffects and SoxEffectsChain

SoxEffects and SoxEffectsChain in torchaudio.sox_effects expose sox operations through a Python interface. Various useful effects like downmixing a multichannel signal or resampling a signal can be done here. python torchaudio.initialize_sox() E = torchaudio.sox_effects.SoxEffectsChain() E.append_effect_to_chain("rate", [16000]) # resample to 16000hz E.append_effect_to_chain("channels", ["1"]) # mono signal E.set_input_file(fn) waveform, sample_rate = E.sox_build_flow_effects() torchaudio.shutdown_sox()

- Python
Published by jamarshon over 6 years ago

Recent Releases of gladia-torchaudio

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gladia-torchaudio - v2.0.2

TorchAudio 2.0.2 Release Note

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gladia-torchaudio - Torchaudio 2.0 Release Note

[Beta] Data augmentation operators

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Fetch metadata using FFmpeg

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