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- Download and install TensorFlow.
- Download and install Kaldi
- Modify the config/config_*.cfg for your setup, specifically the directories
main.py: Goes through the neural net training procedure, look at the config files in the config directory to modify the settings
- Compute the features of training and testing set for GMM and DNN
- Train the monophone GMM with kaldi and get alignments
- Train the triphone GMM with kaldi and get alignments
- train the LDA+MLLT GMM with kaldi and get alignments
- Train the neural net with TensorFlow with the alignments as targets
- Get the state pseudo-likelihoods of the testing set using the neural net
- Decode the testing set with Kaldi using the state pseudo-likelihoods and report the results
feat.py: Does feature computation currently supports:
- mfcc
- fbank
prepare_data.py: data prep functionality
- compute the features for all the utterances
- compute mean and variance statistics for normalisation
- shuffle the examples for mini-batch training
kaldi_io.py: functionality to interface with kaldi
- read alignments
- read scp files
- create dummy neural net for decoding
- read and write kaldi ark format
nnet.py: neural network class
- train: train the neural net
- decode: compute pseudo-likelihood