Powering HMM by Generative Models

Code for our work: Powering Hidden Markov Model by Neural Network based Generative Models

Virtual environment

Please rename the default directory "genhmm" into "gm_hmm" (current module importing depents on this directory name), e.g.

$ mv genhmm gm_hmm

and create the environment as follows.

Create a virtual environment with a python3 interpreter, in the newly created gm_hmm/ directory.

$ cd gm_hmm
$ virtualenv -p python3.6 pyenv
$ cd ..

Add the parent directory of gm_hmm/ to the path:

$ echo $PWD > gm_hmm/pyenv/lib/python3.6/site-packages/gm_hmm.pth

Install the dependencies:

$ cd gm_hmm
$ source pyenv/bin/activate
$ pip install -r requirements.txt

Additional tools

You must install GNU make, on Ubuntu:

$ sudo apt install build-essential
$ make -v
GNU Make 4.1
Built for x86_64-pc-linux-gnu
...

Getting Started

Dataset preparation

See README.md in src/timit-preprocessor

Training

Start by creating the necessary experimental folders for using model "GenHMM" and data feature length of 39, with:

$ make init model=gen nfeats=39 exp_name=genHMM

Change directory to the created experiment directory:

$ cd exp/gen/39feats/genHMM

To run the training of genHMM on 2 classes and during 10 epochs, with two distributed jobs, run:

$ make j=2 nclasses=2 nepochs=10 

Modify the j option to change the number of jobs for this experiment.

The logs appear in log/class.... you can follow the training with:

$ make watch

• Note 1: number of epochs is here number of checkpoints. One checkpoint consist of multiple expectation maximization steps, which you can configure at default.json.