Recently I spent quite a lot of time learning how to run kaldi on Android with absolutely no idea about Android development or Java. After reading a few blog posts, github repos and running into a lot of problems, here is some notes on how I got it working at last. I hope this post will be helpful for anyone who is facing the same challenge as I did.
Statistical tests are important tools for understanding the dataset, performing feature engineering and feature selection. This blog will give a brief introduction of some common statistical tests, what do they do and when to use them.
Types of tests:
A few concepts before we start:
This note is the second part of Understanding kaldi recipes with mini-librispeech example. In the previous note, we walked through data preparation, LM training, monophone and triphone training as well as decoding. And in this note we will focus on training a DNN/HMM ASR model by going through local/chain/run_tdnn.sh in mini_librispeech folder.
# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
stage=0 # set stage for i-vector extraction
decode_nj=10 # number of decoding parallel jobs
train_set=train_clean_5 # train set
test_sets=dev_clean_2 # test set
gmm=tri3b # folder to find the…
This note provides a high-level understanding of how kaldi recipe scripts work, with the hope that people with little experience in shell scripts (like me) can save some time learning kaldi.
Mini-librispeech is a small subset of LibriSpeech corpus which consists of audio book reading speech. We will go through each step in kaldi/egs/mini_librispeech/s5/run.sh.
# Change this location to somewhere where you want to put the data.
# Specifiy where you want to store audio and language model data.
data=./corpus/# Specify the url for downloading audio data.
data_url=www.openslr.org/resources/31# Specify the url for downloading vocabulary, lexicon and pre-trained language…
Machine Learning | Speech Recognition | Data Science