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We present PyChain, a fully parallelized PyTorch implementation of end-to-endlattice-free maximum mutual information (LF-MMI) training for the so-called\emph{chain models} in the Kaldi automatic speech recognition (ASR) toolkit.Unlike other PyTorch and Kaldi based ASR toolkits, PyChain is designed to be asflexible and light-weight as possible so that it can be easily plugged into newASR projects, or other existing PyTorch-based ASR tools, as exemplifiedrespectively by a new project PyChain-example, and Espresso, an existingend-to-end ASR toolkit. PyChain's efficiency and flexibility is demonstratedthrough such novel features as full GPU training on numerator/denominatorgraphs, and support for unequal length sequences. Experiments on the WSJdataset show that with simple neural networks and commonly used machinelearning techniques, PyChain can achieve competitive results that arecomparable to Kaldi and better than other end-to-end ASR systems.

PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASR