#!/bin/bash
  
  # Copyright 2012 Johns Hopkins University (Author: Daniel Povey).  Apache 2.0.
  # This script, which will generally be called from other neural-net training
  # scripts, extracts the training examples used to train the neural net (and also
  # the validation examples used for diagnostics), and puts them in separate archives.
  
  # Begin configuration section.
  cmd=run.pl
  
  feat_type=
  stage=0
  splice_width=4 # meaning +- 4 frames on each side for second LDA
  rand_prune=4.0 # Relates to a speedup we do for LDA.
  within_class_factor=0.0001 # This affects the scaling of the transform rows...
                             # sorry for no explanation, you'll have to see the code.
  
  echo "$0 $@"  # Print the command line for logging
  
  if [ -f path.sh ]; then . ./path.sh; fi
  . parse_options.sh || exit 1;
  
  
  if [ $# != 4 ]; then
    echo "Usage: steps/nnet2/get_lda.sh [opts] <data> <lang> <ali-dir> <exp-dir>"
    echo " e.g.: steps/nnet2/get_lda.sh data/train data/lang exp/tri3_ali exp/tri4_nnet"
    echo " As well as extracting the examples, this script will also do the LDA computation,"
    echo " if --est-lda=true (default:true)"
    echo ""
    echo "Main options (for others, see top of script file)"
    echo "  --config <config-file>                           # config file containing options"
    echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
    echo "  --splice-width <width|4>                         # Number of frames on each side to append for feature input"
    echo "                                                   # (note: we splice processed, typically 40-dimensional frames"
    echo "  --stage <stage|0>                                # Used to run a partially-completed training process from somewhere in"
    echo "                                                   # the middle."
    
    exit 1;
  fi
  
  data=$1
  lang=$2
  alidir=$3
  dir=$4
  
  # Check some files.
  for f in $data/feats.scp $lang/L.fst $alidir/ali.1.gz $alidir/final.mdl $alidir/tree; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  
  # Set some variables.
  oov=`cat $lang/oov.int`
  num_leaves=`gmm-info $alidir/final.mdl 2>/dev/null | awk '/number of pdfs/{print $NF}'` || exit 1;
  silphonelist=`cat $lang/phones/silence.csl` || exit 1;
  
  nj=`cat $alidir/num_jobs` || exit 1;  # number of jobs in alignment dir...
  # in this dir we'll have just one job.
  sdata=$data/split$nj
  utils/split_data.sh $data $nj
  
  mkdir -p $dir/log
  echo $nj > $dir/num_jobs
  cp $alidir/tree $dir
  
  ## Set up features.  Note: these are different from the normal features
  ## because we have one rspecifier that has the features for the entire
  ## training set, not separate ones for each batch.
  if [ -z $feat_type ]; then
    if [ -f $alidir/final.mat ] && ! [ -f $alidir/raw_trans.1 ]; then feat_type=lda; else feat_type=raw; fi
  fi
  echo "$0: feature type is $feat_type"
  
  case $feat_type in
    raw) feats="ark,s,cs:utils/filter_scp.pl --exclude $dir/valid_uttlist $sdata/JOB/feats.scp | apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:- ark:- |"
      train_subset_feats="ark,s,cs:utils/filter_scp.pl $dir/train_subset_uttlist $data/feats.scp | apply-cmvn --norm-vars=false --utt2spk=ark:$data/utt2spk scp:$data/cmvn.scp scp:- ark:- |"
     ;;
    lda) 
      splice_opts=`cat $alidir/splice_opts 2>/dev/null`
      cp $alidir/splice_opts $dir 2>/dev/null
      cp $alidir/final.mat $dir    
        feats="ark,s,cs:utils/filter_scp.pl --exclude $dir/valid_uttlist $sdata/JOB/feats.scp | apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:- ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $dir/final.mat ark:- ark:- |"
        train_subset_feats="ark,s,cs:utils/filter_scp.pl $dir/train_subset_uttlist $data/feats.scp | apply-cmvn --norm-vars=false --utt2spk=ark:$data/utt2spk scp:$data/cmvn.scp scp:- ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $dir/final.mat ark:- ark:- |"
      ;;
    *) echo "$0: invalid feature type $feat_type" && exit 1;
  esac
  if [ -f $alidir/trans.1 ] && [ $feat_type != "raw" ]; then
    echo "$0: using transforms from $alidir"
    feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$alidir/trans.JOB ark:- ark:- |"
    train_subset_feats="$train_subset_feats transform-feats --utt2spk=ark:$data/utt2spk 'ark:cat $alidir/trans.*|' ark:- ark:- |"
  fi
  if [ -f $alidir/raw_trans.1 ] && [ $feat_type == "raw" ]; then
    echo "$0: using raw-fMLLR transforms from $alidir"
    feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$alidir/raw_trans.JOB ark:- ark:- |"
    train_subset_feats="$train_subset_feats transform-feats --utt2spk=ark:$data/utt2spk 'ark:cat $alidir/raw_trans.*|' ark:- ark:- |"
  fi
  
  
  feat_dim=`feat-to-dim "$train_subset_feats" -` || exit 1;
  lda_dim=$[$feat_dim*(1+2*($splice_width))]; # No dim reduction.
  
  if [ $stage -le 0 ]; then
    echo "$0: Accumulating LDA statistics."
    $cmd JOB=1:$nj $dir/log/lda_acc.JOB.log \
      ali-to-post "ark:gunzip -c $alidir/ali.JOB.gz|" ark:- \| \
        weight-silence-post 0.0 $silphonelist $alidir/final.mdl ark:- ark:- \| \
        acc-lda --rand-prune=$rand_prune $alidir/final.mdl "$feats splice-feats --left-context=$splice_width --right-context=$splice_width ark:- ark:- |" ark,s,cs:- \
         $dir/lda.JOB.acc || exit 1;
  fi
  
  echo $feat_dim > $dir/feat_dim
  echo $lda_dim > $dir/lda_dim
  
  if [ $stage -le 1 ]; then
    nnet-get-feature-transform --within-class-factor=$within_class_factor --dim=$lda_dim $dir/lda.mat $dir/lda.*.acc \
        2>$dir/log/lda_est.log || exit 1;
    rm $dir/lda.*.acc
  fi
  
  echo "$0: Finished estimating LDA"