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

Pkwrap: a PyTorch Package for LF-MMI Training of Acoustic Models