  We present a simple wrapper that is useful to train acoustic models in
PyTorch using Kaldi's LF-MMI training framework. The wrapper, called pkwrap
(short form of PyTorch kaldi wrapper), enables the user to utilize the
flexibility provided by PyTorch in designing model architectures. It exposes
the LF-MMI cost function as an autograd function. Other capabilities of Kaldi
have also been ported to PyTorch. This includes the parallel training ability
when multi-GPU environments are unavailable and decode with graphs created in
Kaldi. The package is available on Github at https://github.com/idiap/pkwrap.
