EC2 Installation Walkthrough

Introduction

In this guide I will explain how to setup OpenDcd with Kaldi on EC and decode open source models based on Librispeech corpus. For this walkthrough I used a large instance with four cores and 15GB of memory. OpenDcd is very memory efficient for both decoding and graph construction and this is easily enough to decode the large 4-gram model.

Machine Configure and Setup

   sudo add-apt-repository ppa:ubuntu-toolchain-r/test
   sudo apt-get update
   sudo apt-get install -y gcc-4.9 g++-4.9 cpp-4.9 subversion make zlib1g-dev automake libtool autoconf libatlas3-base

Due a bug in gcc 4.8 we installed gcc 4.9 and set hard links as the system default

   sudo ln -s /usr/bin/g++-4.9 /usr/bin/g++
   sudo ln -s /usr/bin/gcc-4.9 /usr/bin/gcc
   sudo ln -s -f bash /bin/sh

Install Kaldi

'''bash svn co https://svn.code.sf.net/p/kaldi/code/trunk kaldi cd kaldi/tools make cd ../src ./configure '''

For descent runtime performance it is essential to edit the kaldi.mk file and add the -O2 switch. Now just type make to build the Kaldi and optionally specify the number of cores.

  make -j4

Install OpenDcd

   git clone https://github.com/edobashira/opendcd.git
   cd opendcd/3rdparty
   make
   cd ../src/bin
   make -j4

Graph Construction

There are two graph construction methods, in the first we take a set of Kaldi component transducers as the input to the build process. In the second method we take raw language model and lexicon and build everything from scratch. In this recipe we will use the pre-built models from kaldi-asr.org and use the first method.

Download Models

Build Cascade

Decode