Source: torchcodec
Section: python
Priority: optional
Maintainer: Debian Deep Learning Team <debian-ai@lists.debian.org>
Uploaders: Mo Zhou <lumin@debian.org>
Build-Depends: debhelper-compat (= 13),
               dh-python,
               dh-sequence-python3,
               python3-all-dev,
               python3-setuptools,
               python3-torch,
               libtorch-dev,
               libfmt-dev,
               python3-pybind11,
               pybind11-dev,
               cmake,
               pkgconf,
               libavcodec-dev,
               libavdevice-dev,
               libavfilter-dev,
               libavformat-dev,
               libavutil-dev,
               libswresample-dev,
               libswscale-dev,
               python3-pytest <!nocheck>,
               python3-numpy <!nocheck>,
               python3-pil <!nocheck>,
               python3-torchvision <!nocheck>
Standards-Version: 4.7.0
Homepage: https://github.com/pytorch/torchcodec
Vcs-Browser: https://salsa.debian.org/deeplearning-team/torchcodec
Vcs-Git: https://salsa.debian.org/deeplearning-team/torchcodec.git

Package: python3-torchcodec
Architecture: any
Depends: ${misc:Depends}, ${python3:Depends}, ${shlibs:Depends}
Description: PyTorch media decoding and encoding
 TorchCodec is a Python library for decoding video and audio data into PyTorch
 tensors, on CPU and CUDA GPU. It also supports video and audio encoding on
 CPU!  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 and audio,
 or run inference, TorchCodec is how you turn these into data.  We achieve
 these capabilities through:
 .
   * Pythonic APIs that mirror Python and PyTorch conventions.
   * Relying on FFmpeg to do the decoding and encoding.
   * TorchCodec uses the version of FFmpeg you already have installed. FFmpeg
     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.