nttcslab-sp/torchain

WIP: pytorch FFI wrapper for Kaldi chain loss (a.k.a. Lattice Free MMI)

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Experimental

This project provides a critical component for developers building advanced speech recognition systems. It allows them to integrate Kaldi's Lattice-Free MMI (LF-MMI) loss function, which is highly effective for training acoustic models, directly into PyTorch-based deep learning workflows. By combining Kaldi's robust speech processing with PyTorch's flexibility, developers can create more accurate and efficient speech-to-text solutions.

No commits in the last 6 months.

Use this if you are a machine learning engineer or researcher developing custom automatic speech recognition (ASR) systems and need to leverage Kaldi's Chain loss within a PyTorch framework.

Not ideal if you are an end-user simply looking to transcribe audio or if you prefer off-the-shelf ASR solutions without deep customization.

automatic-speech-recognition acoustic-modeling deep-learning-development speech-technology machine-learning-engineering
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 8 / 25
Community 15 / 25

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Language

Python

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Last pushed

Feb 20, 2019

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