Releases · pytorch/audio

06 Aug 17:08

NicolasHug

v2.8.0

6e1c7fe

TorchAudio 2.8.0 release Latest

Latest

Copy pasting update from #3902 (comment):

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.

Assets 2

04 Jun 18:21

atalman

v2.7.1

95c61b4

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.

Assets 2