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For PyTorch media decoding and encoding, use TorchCodec; for tensor transformations, use TorchVision for images and video or TorchAudio for audio. The old I/O APIs did not all disappear at once: video decoding and encoding were removed from TorchVision in version 0.26, image I/O was later deprecated, and TorchAudio announced a separate deprecation and removal schedule for its media I/O APIs.
Which PyTorch library should I use to decode video or audio?
Choose by operation, not just by media type. TorchCodec handles media decoding and encoding; TorchVision and TorchAudio continue to provide transformations for tensors. That distinction matters because decoding gives you tensor data to work with, but it does not replace the preprocessing or transformations your model pipeline may need.
| Task | Recommended library | Role |
|---|---|---|
| Decode or encode images, video, or audio | TorchCodec | Convert media to tensors and tensors back to media. |
| Transform image or video tensors | TorchVision | Image and video transformations, including v2 transforms. |
| Transform audio tensors | TorchAudio | Audio transformations, including transforms such as MelSpectrogram. |
The PyTorch overview also points to ecosystem libraries such as Hugging Face libraries for functions like models and datasets. These complement the media I/O and transformation roles rather than changing which library handles decoding.
How the division works in a pipeline
Video: decode frames, then transform them
A typical flow is to use TorchCodec’s VideoDecoder to retrieve a batch of frames, then apply TorchVision v2 transforms to the resulting tensor. The decoder and transformation library therefore serve separate stages of the pipeline.
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Audio: decode samples, then transform them
For audio, use TorchCodec’s AudioDecoder to decode the media and TorchAudio’s MelSpectrogram or another appropriate transform to process the tensor. These are examples of the intended division of labor, not claims about comparative speed.
Why PyTorch consolidated media I/O
Before consolidation, media entry points were spread across TorchVision and TorchAudio, with overlapping APIs and multiple backends. TorchVision offered io.read_video() and io.VideoReader(), with Python PyAV, C++ FFmpeg, and CUDA/NVCUVID backends. The PyAV backend was available out of the box; the other backends required source builds and were tied to a particular FFmpeg version. TorchAudio offered StreamReader, StreamWriter, and audio decoding utilities with FFmpeg, libsoundfile, and libsox backends. Image decoding also lived in TorchVision.
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The PyTorch authors say consolidating these paths makes it possible to focus optimization work in one implementation, reduce duplicated maintenance, and make library roles more predictable. They characterize TorchCodec as generally more performant than the earlier TorchVision and TorchAudio implementations, particularly for CUDA video decoding. That is the project authors’ characterization, not an independent comparative benchmark.
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What changed, and when
The transition happened in stages. Treat each library’s versioned notes as distinct: a planned deprecation or removal in one project does not establish that every related API vanished at the same time.
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| Library and change | Version or date | What it means |
|---|---|---|
| TorchCodec launch announcement described planned future audio support and broader consolidation. | December 11, 2024 | The announcement reflects an earlier roadmap, not the later scope described by PyTorch. |
| TorchAudio announced deprecation of decoding and encoding APIs, while identifying transforms, functional APIs, models, pipelines, and Kaldi compliance APIs that would remain. | Deprecation from version 2.8; removal planned in version 2.9 | This is the announced schedule. Check the documentation for the specific TorchAudio release in your environment before relying on a particular API’s status. |
| TorchVision announced deprecation of video decoding and encoding. | Beginning with version 0.22; planned removal in 0.24 | The announcement was made in April 2025; the later release notes record the actual removal in 0.26. |
TorchVision release notes state that video utilities, including torchvision.io.video.*, read_video, write_video, and VideoReader, were removed. |
Version 0.26 | For video I/O migration, use TorchCodec rather than expecting those utilities in TorchVision 0.26. |
| TorchVision image decoding and encoding were later marked deprecated; the release notes point to TorchCodec 0.16 or later and a migration guide. | Later release notes; see the TorchVision release history for the applicable release | Image I/O had a separate transition from video I/O. |
The October 2026 PyTorch overview describes TorchCodec as handling image, video, and audio decoding and encoding. It also says TorchCodec, TorchVision, and TorchAudio are ABI stable and do not need to match every PyTorch release. ABI stability does not remove the need to check installation instructions and migration guidance for your actual environment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to migrate from torchvision.io or TorchAudio media decoding
- Identify the operation. Separate media decoding or encoding from tensor transformations in your code. Replace the former with TorchCodec; keep the appropriate TorchVision or TorchAudio transformations.
- Check the API’s release status. For TorchVision video APIs, consult the official release notes; video decoding and encoding utilities were removed in 0.26. TorchVision image I/O was deprecated later, with the notes pointing to TorchCodec 0.16 or later and a migration guide.
- Check TorchAudio’s specific release. Its project announcement planned deprecation beginning in 2.8 and removal in 2.9. Consult the TorchAudio project announcement and current package documentation for the version you install.
- Replace the media I/O stage. Use the appropriate TorchCodec decoder or encoder for the media and direction of conversion. For example, a video path can retrieve frames with
VideoDecoder; an audio path can decode withAudioDecoder. - Retain tensor processing. Apply TorchVision transformations to image and video tensors or TorchAudio transformations to audio tensors after decoding. Test the complete path with your inputs and deployment environment, including codec availability.
For the versioned transition context, see the TorchVision future-focus announcement and the TorchAudio future announcement.
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What to check for codecs, CPU, and CUDA
TorchCodec documents both CPU decoding and CUDA acceleration for high-throughput workloads. CUDA is optional, not a general requirement for decoding. Codec availability depends on the FFmpeg version installed in the environment, so do not assume every codec or backend is present in every installation.
- Codec support: verify that the installed FFmpeg version provides the codec your media uses.
- Execution path: use CPU decoding where it fits your workload; consider CUDA when throughput needs justify the GPU execution path.
- Seeking behavior: frames can be retrieved by index or presentation time, but accurate seeking can be slower in some cases. Approximate seeking is discussed as a performance direction, not a universal guarantee.
- Hardware choice: evaluate workload throughput, cost, memory, and deployment constraints before choosing a CUDA-capable GPU. The cited PyTorch material names no specific GPU model or universal hardware requirement.
The TorchCodec launch announcement describes these capabilities and caveats. Its discussion does not establish a universal speedup for every codec, input, or deployment.
What this consolidation does not mean
- It does not mean TorchVision and TorchAudio are obsolete: their tensor transformation APIs remain useful in their respective media domains.
- It does not mean every older I/O API was removed simultaneously; the projects had distinct schedules and version histories.
- It does not mean CUDA is necessary; CPU decoding is supported.
- It does not guarantee the same codec availability or performance in every environment; installed FFmpeg, hardware, workload, and seeking requirements matter.
In an October 5, 2026 PyTorch post, Meta authors Nicolas Hug and Scott Schneider put the recommendation directly: “If you need to decode or encode media, whether it’s images, video, or audio, use TorchCodec.”
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