#!/bin/bash
  
  # Copyright 2012  Microsoft Corporation  Johns Hopkins University (Author: Daniel Povey)
  # 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_si284, data/train_si84, etc.
  
  # Don't bother doing train_si84 separately (although we have the file lists
  # in data/local/) because it's just the first 7138 utterances in train_si284.
  # We'll create train_si84 after doing the feature extraction.
  
  . ./path.sh || exit 1;
  
  echo "Preparing train and test data"
  srcdir=data/local/data
  lmdir=data/local/nist_lm
  lexicon=data/local/lang_tmp/lexiconp.txt
  
  for x in test_eval92_clean test_eval92_noisy test_eval92_5k_clean test_eval92_5k_noisy dev_dt_05_clean dev_dt_05_reverb dev_dt_05_noisy dev_dt_20_clean dev_dt_20_reverb dev_dt_20_noisy train_si84_clean train_si84_reverb train_si84_noisy; 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 bg tgpr tg bg_5k tgpr_5k tg_5k; do
    test=data/lang_test_${lm_suffix}
    mkdir -p $test
    cp -r data/lang/* $test
  
    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 $test || exit 1;
  done
  
  echo "Succeeded in formatting data."