Package: exkaldi
ExKaldi: A Python-based Extension Tool of KaldiExKaldi automatic speech recognition toolkit is developed to build an interface betweenKaldi ASR toolkitand Python.
Differing from other Kaldi wrappers, ExKaldi have these features:Integrated APIs to build a ASR systems, including feature extraction, GMM-HMM acoustic model training, N-Grams language model training, decoding and scoring.ExKaldi provides tools to support train DNN acoustic model with Deep Learning frameworks, such as Tensorflow.ExKaldi supports CTC decoding.The goal of ExKaldi is to help developers build high-performance ASR systems with Python language easily.InstallationCurrent version: 1.3.5.
(We only tested our toolkit on Ubuntu >= 16., python3.6,python3.7,python3.8 with gh-action)If you have not installed Kaldi ASR toolkit, clone the Kaldi ASR toolkit repository firstly (Kaldi version 5.5 is expected.)git clone https://github.com/kaldi-asr/kaldi.git kaldi --origin upstreamAnd follow these three tutorial files to install and compile it.less kaldi/INSTALL
less kaldi/tools/INSTALL
less kaldi/src/INSTALLClone the ExKaldi source code from our github project, then install it.Install with pip$ pip install https://github.com/kpu/kenlm/archive/master.zip
$ pip install exkaldiInstall with Source$ git clone https://github.com/wangyu09/exkaldi.git
$ cd exkaldi
$ bash quick_install.shCheck if it is installed correctly.python3 -c "import exkaldi"TutorialInexkaldi/tutorialsdirectory, we prepared a simple tutorial to show how to use ExKaldi APIs to build a ASR system from the scratch.
The data is from librispeechtrain_100_cleandataset. This tutorial includes:Extract and process MFCC feature.Train and querying a N-grams language model.Train monophone GMM-HMM, build decision tree, and train triphone GMM-HMM.Train a DNN acoustic model with Tensorflow.Compile WFST decoding graph.Decode based on GMM-HMM and DNN-HMM.Process lattice and compute WER score.This ASR symtem built here is just a dummy model, and we have done some formal experiments inexkaldi/examples. Check the source code ordocumentsto look more information about APIs.ExperimentsWe have done some experiments to test ExKaldi toolkit, and they achieved a good performance.TIMIT1, The perplexity of various language models. All these systems are trained with TIMITtraindataset and tested with TIMITtestdata. The score showed in the table is PPL score.2-grams3-grams4-grams5-grams6-gramsKaldi baseline irstlm14.41------------ExKaldi srilm14.4213.0513.6714.3014.53ExKaldi kenlm14.3912.7512.7512.7012.252, The phone error rate (PER) of various GMM-HMM-based systems. All these systems are trained with TIMITtraindataset and tested with TIMITtestdataset. The Language model backend used in ExKaldi is KenLM. From the results, we can know than KenLm is avaliable to optimize the language model. And what's more, with ExKaldi, we cherry-picked the N-grams model by testing the perplexity and it improved the performance of ASR system.monotri1tri2tri3Kaldi baseline 2grams32.5426.1723.6321.54ExKaldi 2grams32.5325.8923.6321.43ExKaldi 6grams29.8324.0722.4020.013, The phone error rate (PER) of two DNN-HMM-based systems. We trained our models with Tensorflow 2.3. The version of PyTorch-Kaldi toolkit is 1.0 in our experiment.DNNLSTMKaldi baseline18.67---PyTorch-Kaldi17.9917.01ExKaldi15.1315.01