#!/bin/bash
  
  # To be run from one level above this directory
  # Generate the text for the LM training
  tmp_dir=data/local/tmp
  train_all=data/local/data/train_all
  
  if [ $# -lt 1 ]; then
    echo "Specify the location of the split files"
    exit 1;
  fi
  
  splitFile=$1
  split=train
  # Train only
  if [ -d $tmp_dir/$split ]; then
    rm -r $tmp_dir/$split
  fi
  cp -r $train_all $tmp_dir/$split
  
  awk 'BEGIN {FS=" "}; FNR==NR { a[$1]; next } ((substr($2,0,length($2)-2) ".sph") in a)' \
  $splitFile/$split $train_all/segments > $tmp_dir/$split/segments
  
  n=`awk 'BEGIN {FS = " "}; {print substr($2,0,length($2)-2)}' $tmp_dir/$split/segments | sort | uniq | wc -l`
  
  echo "$n conversations left in split $split"
  
  utils/fix_data_dir.sh $tmp_dir/$split
  utils/fix_data_dir.sh $tmp_dir/$split
  # There is no feature file yet, use --no-feats switch
  utils/validate_data_dir.sh --no-feats $tmp_dir/$split
  rm $tmp_dir/$split/*.tmp
  
  # Now use this training text
  
  text=$tmp_dir/train/text
  lexicon=data/local/dict/lexicon.txt 
  
  for f in "$text" "$lexicon"; do
    [ ! -f $x ] && echo "$0: No such file $f" && exit 1;
  done
  
  # This script takes no arguments.  It assumes you have already run
  # fisher_data_prep.sh and fisher_prepare_dict.sh
  # It takes as input the files
  #data/train_all/text
  #data/local/dict/lexicon.txt
  
  dir=`pwd`/data/local/lm
  mkdir -p $dir
  export LC_ALL=C # You'll get errors about things being not sorted, if you
  # have a different locale.
  export PATH=$PATH:`pwd`/../../../tools/kaldi_lm
  ( # First make sure the kaldi_lm toolkit is installed.
   cd ../../../tools || exit 1;
   if [ -d kaldi_lm ]; then
     echo Not installing the kaldi_lm toolkit since it is already there.
   else
     echo Downloading and installing the kaldi_lm tools
     if [ ! -f kaldi_lm.tar.gz ]; then
       wget http://www.danielpovey.com/files/kaldi/kaldi_lm.tar.gz || exit 1;
     fi
     tar -xvzf kaldi_lm.tar.gz || exit 1;
     cd kaldi_lm
     make || exit 1;
     echo Done making the kaldi_lm tools
   fi
  ) || exit 1;
  
  mkdir -p $dir
  
  
  cleantext=$dir/text.no_oov
  
  cat $text | awk -v lex=$lexicon 'BEGIN{while((getline<lex) >0){ seen[$1]=1; } } 
    {for(n=1; n<=NF;n++) {  if (seen[$n]) { printf("%s ", $n); } else {printf("<unk> ");} } printf("
  ");}' \
    > $cleantext || exit 1;
  
  
  cat $cleantext | awk '{for(n=2;n<=NF;n++) print $n; }' | sort | uniq -c | \
     sort -nr > $dir/word.counts || exit 1;
  
  
  # Get counts from acoustic training transcripts, and add  one-count
  # for each word in the lexicon (but not silence, we don't want it
  # in the LM-- we'll add it optionally later).
  cat $cleantext | awk '{for(n=2;n<=NF;n++) print $n; }' | \
    cat - <(grep -w -v '!SIL' $lexicon | awk '{print $1}') | \
     sort | uniq -c | sort -nr > $dir/unigram.counts || exit 1;
  
  # note: we probably won't really make use of <unk> as there aren't any OOVs
  cat $dir/unigram.counts  | awk '{print $2}' | get_word_map.pl "<s>" "</s>" "<unk>" > $dir/word_map \
     || exit 1;
  
  # note: ignore 1st field of train.txt, it's the utterance-id.
  cat $cleantext | awk -v wmap=$dir/word_map 'BEGIN{while((getline<wmap)>0)map[$1]=$2;}
    { for(n=2;n<=NF;n++) { printf map[$n]; if(n<NF){ printf " "; } else { print ""; }}}' | gzip -c >$dir/train.gz \
     || exit 1;
  
  train_lm.sh --arpa --lmtype 3gram-mincount $dir || exit 1;
  
  # Perplexity over 88307.000000 words (excluding 691.000000 OOVs) is 71.241332
  
  # note: output is
  # data/local/lm/3gram-mincount/lm_unpruned.gz 
  
  
  exit 0
  
  echo "Baseline"
  
  # From here is some commands to do a baseline with SRILM (assuming
  # you have it installed).
  heldout_sent=158126 # Don't change this if you want result to be comparable with
      # kaldi_lm results
  sdir=$dir/srilm # in case we want to use SRILM to double-check perplexities.
  mkdir -p $sdir
  cat $cleantext | awk '{for(n=2;n<=NF;n++){ printf $n; if(n<NF) printf " "; else print ""; }}' | \
    head -$heldout_sent > $sdir/heldout
  cat $cleantext | awk '{for(n=2;n<=NF;n++){ printf $n; if(n<NF) printf " "; else print ""; }}' | \
    tail -n +$heldout_sent > $sdir/train
  
  cat $dir/word_map | awk '{print $1}' | cat - <(echo "<s>"; echo "</s>" ) > $sdir/wordlist
  
  
  ngram-count -text $sdir/train -order 3 -limit-vocab -vocab $sdir/wordlist -unk \
    -map-unk "<unk>" -kndiscount -interpolate -lm $sdir/srilm.o3g.kn.gz
  ngram -lm $sdir/srilm.o3g.kn.gz -ppl $sdir/heldout 
  
  # data/local/lm/srilm/srilm.o3g.kn.gz: line 71: warning: non-zero probability for <unk> in closed-vocabulary LM
  # file data/local/lm/srilm/heldout: 10000 sentences, 78998 words, 0 OOVs
  # 0 zeroprobs, logprob= -165170 ppl= 71.7609 ppl1= 123.258
  
  
  # Note: perplexity SRILM gives to Kaldi-LM model is similar to what kaldi-lm reports above.
  # Difference in WSJ must have been due to different treatment of <unk>.
  ngram -lm $dir/3gram-mincount/lm_unpruned.gz  -ppl $sdir/heldout 
  
  # data/local/lm/srilm/srilm.o3g.kn.gz: line 71: warning: non-zero probability for <unk> in closed-vocabulary LM
  # file data/local/lm/srilm/heldout: 10000 sentences, 78998 words, 0 OOVs
  # 0 zeroprobs, logprob= -164990 ppl= 71.4278 ppl1= 122.614