#!/bin/bash
  
  # Copyright 2012  Microsoft Corporation  Johns Hopkins University (Author: Daniel Povey)
  #           2015  Guoguo Chen
  #           2016  Mitsubishi Electric Research Laboratories (Author: Shinji Watanabe)
  # Apache 2.0
  
  # This script takes data prepared in a corpus-dependent way
  # in data/local/, and converts it into the "canonical" form,
  # in various subdirectories of data/, e.g. data/lang, data/lang_test_ug,
  # data/train_si84, etc.
  
  lang_suffix=
  
  echo "$0 $@"  # Print the command line for logging
  . utils/parse_options.sh || exit 1;
  
  . ./path.sh || exit 1;
  
  echo "Preparing train and test data"
  srcdir=data/local/data
  lmdir=data/local/nist_lm
  tmpdir=data/local/lm_tmp
  lexicon=data/local/lang_tmp/lexiconp.txt
  mkdir -p $tmpdir
  
  for x in et05_orig_clean dt05_orig_clean tr05_orig_clean; do
    mkdir -p data/$x
    cp $srcdir/${x}_wav.scp data/$x/wav.scp || exit 1;
    cp $srcdir/$x.txt data/$x/text || exit 1;
    cp $srcdir/$x.spk2utt data/$x/spk2utt || exit 1;
    cp $srcdir/$x.utt2spk data/$x/utt2spk || exit 1;
    utils/filter_scp.pl data/$x/spk2utt $srcdir/spk2gender > data/$x/spk2gender || exit 1;
  done
  
  
  # Next, for each type of language model, create the corresponding FST
  # and the corresponding lang_test_* directory.
  
  echo Preparing language models for test
  
  for lm_suffix in tgpr_5k; do
    test=data/lang${lang_suffix}_test_${lm_suffix}
  
    mkdir -p $test
    cp -r data/lang${lang_suffix}/* $test || exit 1;
  
    gunzip -c $lmdir/lm_${lm_suffix}.arpa.gz | \
      arpa2fst --disambig-symbol=#0 \
               --read-symbol-table=$test/words.txt - $test/G.fst
  
    utils/validate_lang.pl --skip-determinization-check $test || exit 1;
  done
  
  echo "Succeeded in formatting data."
  rm -r $tmpdir