Pkwrap: a PyTorch Package for LF-MMI Training of Acoustic Models
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1.
5 papers.
- 2022Are disentangled representations all you need to build speaker anonymization systems?cited by 10
- 2021Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic modelcited by 5
- 2023A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllerscited by 3
- 2022Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding taskscited by 2
- 2022Privacy-Preserving Speech Representation Learning using Vector Quantizationcited by 0
- 2023A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers16 Apr 2023
- 2022Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasks16 Dec 2022
- 2022Are disentangled representations all you need to build speaker anonymization systems?22 Aug 2022
- 2022Privacy-Preserving Speech Representation Learning using Vector Quantization15 Mar 2022
- 2021Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model6 Apr 2021