TorchCodec is a Python library for decoding videos into PyTorch tensors. It aims to be fast, easy to use, and well integrated into the PyTorch ecosystem. If you want to use PyTorch to train ML models on videos, TorchCodec is how you turn those videos into data.
We achieve these capabilities through:
- Pythonic APIs that mirror Python and PyTorch conventions.
- Relying on FFmpeg to do the decoding. TorchCodec uses the version of FFmpeg you already have installed. FMPEG is a mature library with broad coverage available on most systems. It is, however, not easy to use. TorchCodec abstracts FFmpeg's complexity to ensure it is used correctly and efficiently.
- Returning data as PyTorch tensors, ready to be fed into PyTorch transforms or used directly to train models.
Note
TorchCodec is still in early development stage and we are actively seeking feedback. If you have any suggestions or issues, please let us know by opening an issue on our GitHub repository.
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Installation instructions
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:link: install_instructions.html
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How to install TorchCodec
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Getting Started with TorchCodec
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:link: generated_examples/basic_example.html
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A simple video decoding example
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Clip sampling
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:link: generated_examples/sampling.html
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How to sample video clips
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GPU decoding using TorchCodec
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:link: generated_examples/basic_cuda_example.html
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A simple example demonstrating Nvidia GPU decoding
.. toctree:: :maxdepth: 1 :caption: TorchCodec documentation :hidden: Home <self> glossary
.. toctree:: :maxdepth: 1 :caption: Examples and tutorials :hidden: install_instructions generated_examples/index
.. toctree:: :glob: :maxdepth: 1 :caption: API Reference :hidden: api_ref_torchcodec api_ref_decoders api_ref_samplers