torchcodec

We dynamically load the shared object files. See: https://github.com/meta-pytorch/torchcodec/blob/8bbce656797c... Solutions that shell out to the `ffmpeg` binary are not going to perform well.

We tend to be conservative with the benchmarks results that we make public, because all benchmarks are wrong and unfair - they depend too much on the machine capabilities, on software versions, and on the actual decoding patterns that are relevant for the user - none of which can be controlled or fairly captured in a benchmark. That being said, we've got some benchmarks here, with a script that users can run on their own: https://github.com/meta-pytorch/torchcodec/pull/1474. Note that TorchCodec relies on FFmpeg libraries, not the FFmpeg binary itself. The new WavDecoder is faster because it bypasses the FFmpeg libraries code, not because it bypasses loading the FFmpeg binary in memory. Regarding static linking: we stick to dynamic linking to honor the L-GPL license of the FFmpeg libraries. TorchCodec is BSD-licensed, and statically linki

You can get older version of TorchCodec that work with older version of PyTorch, but it unfortunately will not have the new features (HDR video decoding; fast Wav decoding) in the latest release. See the compatbility matrix: https://github.com/meta-pytorch/torchcodec#compatibility-wit... Up until recently, TorchCodec releases worked with one-and-only-one version of PyTorch. This is because up until recently, PyTorch did not have a stable ABI, and we needed to pin TorchCodec releases to PyTorch releases. But! PyTorch now has an excellent Stable ABI (https://github.com/meta-pytorch/torchcodec#compatibility-wit..., https://www.youtube.com/watch?v=HNdEmnvMvGE&t=1s) and TorchCodec is taking advantage of that since version 0.12.