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 linking against the L-GPL FFmpeg libs would not be compliant. Some libraries dynamically link against FFmpeg while still bundling the FFmpeg libraries as .so files in the Python wheel - whether that's still compliant is honestly unclear to me, so we prefer leaving it up for the user to supply their own FFmpeg via pure dynamic linking.