#!/bin/bash
  
  # Copyright 2012  Johns Hopkins University (author: Daniel Povey)  Tony Robinson
  #           2015  Guoguo Chen
  
  # This script trains LMs on the WSJ LM-training data.
  # It requires that you have already run wsj_extend_dict.sh,
  # to get the larger-size dictionary including all of CMUdict
  # plus any OOVs and possible acronyms that we could easily
  # derive pronunciations for.
  
  # This script takes no command-line arguments but takes the --cmd option.
  
  # Begin configuration section.
  rand_seed=0
  cmd=run.pl
  nwords=10000 # This is how many words we're putting in the vocab of the RNNLM.
  hidden=30
  class=200 # Num-classes... should be somewhat larger than sqrt of nwords.
  direct=1000 # Number of weights that are used for "direct" connections, in millions.
  rnnlm_ver=rnnlm-0.3e # version of RNNLM to use
  threads=1 # for RNNLM-HS
  bptt=2 # length of BPTT unfolding in RNNLM
  bptt_block=20 # length of BPTT unfolding in RNNLM
  dict_suffix=
  rnnlm_options="-direct-order 4"
  # End configuration section.
  
  [ -f ./path.sh ] && . ./path.sh
  . utils/parse_options.sh
  
  if [ $# != 1 ]; then
     echo "Usage: local/wsj_train_rnnlms.sh [options] <dest-dir>"
     echo "For options, see top of script file"
     exit 1;
  fi
  
  dir=$1
  srcdir=data/local/dict${dict_suffix}_larger
  mkdir -p $dir
  
  $KALDI_ROOT/tools/extras/check_for_rnnlm.sh "$rnnlm_ver" || exit 1
  export PATH=$KALDI_ROOT/tools/$rnnlm_ver:$PATH
  
  
  if [ ! -f $srcdir/cleaned.gz -o ! -f $srcdir/lexicon.txt ]; then
    echo "Expecting files $srcdir/cleaned.gz and $srcdir/wordlist.final to exist";
    echo "You need to run local/wsj_extend_dict.sh before running this script."
    exit 1;
  fi
  
  cat $srcdir/lexicon.txt | awk '{print $1}' | grep -v -w '!SIL' > $dir/wordlist.all
  
  # Get training data with OOV words (w.r.t. our current vocab) replaced with <UNK>.
  echo "Getting training data with OOV words replaced with <UNK> (train_nounk.gz)"
  gunzip -c $srcdir/cleaned.gz | awk -v w=$dir/wordlist.all \
    'BEGIN{while((getline<w)>0) v[$1]=1;}
    {for (i=1;i<=NF;i++) if ($i in v) printf $i" ";else printf "<UNK> ";print ""}'|sed 's/ $//g' \
    | gzip -c > $dir/all.gz
  
  echo "Splitting data into train and validation sets."
  heldout_sent=10000
  gunzip -c $dir/all.gz | head -n $heldout_sent > $dir/valid.in # validation data
  gunzip -c $dir/all.gz | tail -n +$heldout_sent | \
   perl -e ' use List::Util qw(shuffle); @A=<>; print join("", shuffle(@A)); ' \
    > $dir/train.in # training data
  
  
    # The rest will consist of a word-class represented by <RNN_UNK>, that
    # maps (with probabilities) to a whole class of words.
  
  # Get unigram counts from our training data, and use this to select word-list
  # for RNNLM training; e.g. 10k most frequent words.  Rest will go in a class
  # that we (manually, at the shell level) assign probabilities for words that
  # are in that class.  Note: this word-list doesn't need to include </s>; this
  # automatically gets added inside the rnnlm program.
  # Note: by concatenating with $dir/wordlist.all, we are doing add-one
  # smoothing of the counts.
  
  export TMPDIR=$dir # to avoid filling up /tmp/
  
  cat $dir/train.in $dir/wordlist.all | grep -v '</s>' | grep -v '<s>' | \
    awk '{ for(x=1;x<=NF;x++) count[$x]++; } END{for(w in count){print count[w], w;}}' | \
    sort -nr > $dir/unigram.counts
  
  head -$nwords $dir/unigram.counts | awk '{print $2}' > $dir/wordlist.rnn
  
  tail -n +$nwords $dir/unigram.counts > $dir/unk_class.counts
  
  tot=`awk '{x=x+$1} END{print x}' $dir/unk_class.counts`
  awk -v tot=$tot '{print $2, ($1*1.0/tot);}' <$dir/unk_class.counts  >$dir/unk.probs
  
  
  for type in train valid; do
    cat $dir/$type.in | awk -v w=$dir/wordlist.rnn \
      'BEGIN{while((getline<w)>0) v[$1]=1;}
      {for (i=1;i<=NF;i++) if ($i in v) printf $i" ";else printf "<RNN_UNK> ";print ""}'|sed 's/ $//g' \
      > $dir/$type
  done
  rm $dir/train.in # no longer needed-- and big.
  
  # Now randomize the order of the training data.
  cat $dir/train | awk -v rand_seed=$rand_seed 'BEGIN{srand(rand_seed);} {printf("%f\t%s
  ", rand(), $0);}' | \
   sort | cut -f 2 > $dir/foo
  mv $dir/foo $dir/train
  
  # OK we'll train the RNNLM on this data.
  
  # todo: change 100 to 320.
  # using 100 classes as square root of 10k.
  echo "Training RNNLM (note: this uses a lot of memory! Run it on a big machine.)"
  #time rnnlm -train $dir/train -valid $dir/valid -rnnlm $dir/100.rnnlm \
  #  -hidden 100 -rand-seed 1 -debug 2 -class 100 -bptt 2 -bptt-block 20 \
  #  -direct-order 4 -direct 1000 -binary >& $dir/rnnlm1.log &
  
  # since the mikolov rnnlm and faster-rnnlm have slightly different interfaces...
  if [ "$rnnlm_ver" == "faster-rnnlm" ]; then
    $cmd $dir/rnnlm.log \
       $KALDI_ROOT/tools/$rnnlm_ver/rnnlm -threads $threads -train $dir/train -valid $dir/valid \
       -rnnlm $dir/rnnlm -hidden $hidden -seed 1 -bptt $bptt -bptt-block $bptt_block \
       $rnnlm_options -direct $direct || exit 1;
  else
    $cmd $dir/rnnlm.log \
       $KALDI_ROOT/tools/$rnnlm_ver/rnnlm -independent -train $dir/train -valid $dir/valid \
       -rnnlm $dir/rnnlm -hidden $hidden -rand-seed 1 -debug 2 -class $class -bptt $bptt -bptt-block $bptt_block \
       $rnnlm_options -direct $direct -binary || exit 1;
  fi
  
  # make it like a Kaldi table format, with fake utterance-ids.
  cat $dir/valid.in | awk '{ printf("uttid-%d ", NR); print; }' > $dir/valid.with_ids
  
  utils/rnnlm_compute_scores.sh --ensure_normalized_probs true --rnnlm_ver $rnnlm_ver $dir $dir/tmp.valid $dir/valid.with_ids \
    $dir/valid.scores
  
  nw=`cat $dir/valid.with_ids | awk '{a+=NF}END{print a}'` # Note: valid.with_ids includes utterance-ids which
    # is one per word, to account for the </s> at the end of each sentence; this is the
    # correct number to normalize buy.
    # we have noticed that "wc -w" might give wrong results for certain languages
  
  p=`awk -v nw=$nw '{x=x+$2} END{print exp(x/nw);}' <$dir/valid.scores`
  echo Perplexity is $p | tee $dir/perplexity.log
  
  rm $dir/train $dir/all.gz
  
  # This is a better setup, but takes a long time to train:
  #echo "Training RNNLM (note: this uses a lot of memory! Run it on a big machine.)"
  #time rnnlm -train $dir/train -valid $dir/valid -rnnlm $dir/320.rnnlm \
  #  -hidden 320 -rand-seed 1 -debug 2 -class 300 -bptt 2 -bptt-block 20 \
  #  -direct-order 4 -direct 2000 -binary