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

arXiv: https://arxiv.org/abs/2010.03466 · HTML: https://mlsanity.com/paper/2010.03466/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Are disentangled representations all you need to build speaker anonymization systems?](https://mlsanity.com/paper/2208.10497/) (2022)
- [Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model](https://mlsanity.com/paper/2104.02558/) (2021)
- [A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers](https://mlsanity.com/paper/2304.07842/) (2023)
- [Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasks](https://mlsanity.com/paper/2212.08489/) (2022)
- [Privacy-Preserving Speech Representation Learning using Vector Quantization](https://mlsanity.com/paper/2203.09518/) (2022)

## Abstract (arXiv)

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.