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We introduce PyKaldi2 speech recognition toolkit implemented based on Kaldiand PyTorch. While similar toolkits are available built on top of the two, akey feature of PyKaldi2 is sequence training with criteria such as MMI, sMBRand MPE. In particular, we implemented the sequence training module withon-the-fly lattice generation during model training in order to simplify thetraining pipeline. To address the challenging acoustic environments in realapplications, PyKaldi2 also supports on-the-fly noise and reverberationsimulation to improve the model robustness. With this feature, it is possibleto backpropogate the gradients from the sequence-level loss to the front-endfeature extraction module, which, hopefully, can foster more research in thedirection of joint front-end and backend learning. We performed benchmarkexperiments on Librispeech, and show that PyKaldi2 can achieve reasonablerecognition accuracy. The toolkit is released under the MIT license.

PyKaldi2: Yet another speech toolkit based on Kaldi and PyTorch