#!/bin/bash
  # Copyright 2015-2016  Sarah Flora Juan
  # Copyright 2016  Johns Hopkins University (Author: Yenda Trmal)
  # Apache 2.0
  
  set -e -o pipefail
  
  # To create G.fst from ARPA language model
  . ./path.sh || die "path.sh expected";
  
  local/train_lms_srilm.sh --train-text data/train/text data/ data/srilm
  
  nl -nrz -w10  corpus/LM/iban-bp-2012.txt | utils/shuffle_list.pl > data/local/external_text
  local/train_lms_srilm.sh --train-text data/local/external_text data/ data/srilm_external
  
  # let's do ngram interpolation of the previous two LMs
  # the lm.gz is always symlink to the model with the best perplexity, so we use that
  
  mkdir -p data/srilm_interp
  for w in 0.9 0.8 0.7 0.6 0.5; do
      ngram -lm data/srilm/lm.gz  -mix-lm data/srilm_external/lm.gz \
            -lambda $w -write-lm data/srilm_interp/lm.${w}.gz
      echo -n "data/srilm_interp/lm.${w}.gz "
      ngram -lm data/srilm_interp/lm.${w}.gz -ppl data/srilm/dev.txt | paste -s -
  done | sort  -k15,15g  > data/srilm_interp/perplexities.txt
  
  # for basic decoding, let's use only a trigram LM
  [ -d data/lang_test/ ] && rm -rf data/lang_test
  cp -R data/lang data/lang_test
  lm=$(cat data/srilm/perplexities.txt | grep 3gram | head -n1 | awk '{print $1}')
  local/arpa2G.sh $lm data/lang_test data/lang_test
  
  # for decoding using bigger LM let's find which interpolated gave the most improvement
  [ -d data/lang_big ] && rm -rf data/lang_big
  cp -R data/lang data/lang_big
  lm=$(cat data/srilm_interp/perplexities.txt | head -n1 | awk '{print $1}')
  local/arpa2G.sh $lm data/lang_big data/lang_big
  
  # for really big lm, we should only decode using small LM
  # and resocre using the big lm
  utils/build_const_arpa_lm.sh $lm data/lang_big data/lang_big
  exit 0;