pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
I was hoping to compare performance of Transformer models and non-Transformer models. I'm essentially trying to reproduce BERT which is possible according to the PyTorch documentation. Or am I better off just adding the model class to the neural_nets.py library?
Hi, sorry for the late reply, we are very very busy with SpeechBrain and PyTorch-Kaldi won't be maintained anymore very soon. You can easily wrap the pytorch transformers to a model in neural_nets.py.
I was hoping to compare performance of Transformer models and non-Transformer models. I'm essentially trying to reproduce BERT which is possible according to the PyTorch documentation. Or am I better off just adding the model class to the neural_nets.py library?