#!/bin/bash
  
  # Copyright       2014  Daniel Povey
  # Apache 2.0
  
  #
  # This script takes a data directory and a directory computed by
  # ./train_lvtln_model.sh, and it computes per-utterance warp-factors utt2warp.  It
  # expects vad.scp to exist in the data directory.  Note: like
  # train_lvtln_model.sh, it uses features of the speaker-id type, i.e. double
  # delta features with sliding window cepstral mean normalization.
  
  # Begin configuration.
  stage=-1
  config=
  cmd=run.pl
  logdet_scale=0.0
  subsample=5 # We use every 5th frame by default; this is more
              # CPU-efficient.
  nj=4
  cleanup=true
  num_gselect=25
  num_iters=5 # number of iters of transform estimation
  # End configuration.
  
  echo "$0 $@"  # Print the command line for logging
  
  [ -f path.sh ] && . ./path.sh;
  . parse_options.sh || exit 1;
  
  if [ $# != 3 ]; then
     echo "Usage: $0 <data-dir> <vtln-dir> <exp-dir>"
     echo "e.g.: $0 data/train_novtln exp/vtln exp/train_warps"
     echo "where <vtln-dir> is produced by train_lvtln_model.sh"
     echo "Output is <exp-dir>/utt2warp"
     echo "main options (for others, see top of script file)"
     echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
     echo "  --nj <num-jobs>                                  # number of jobs to use (default 4)"
     echo "  --config <config-file>                           # config containing options"
     echo "  --stage <stage>                                  # stage to do partial re-run from."
     exit 1;
  fi
  
  data=$1
  vtlndir=$2
  dir=$3
  
  for f in $data/feats.scp $data/spk2utt $vtlndir/final.lvtln $vtlndir/final.dubm $vtlndir/final.ali_dubm; do
    [ ! -f $f ] && echo "train_deltas.sh: no such file $f" && exit 1;
  done
  
  if [ -f $data/utt2warp ]; then
    echo "$0: source data directory $data appears to already have VTLN.";
    exit 1;
  fi
  
  mkdir -p $dir/log
  echo $nj > $dir/num_jobs
  
  sdata=$data/split$nj;
  split_data.sh $data $nj || exit 1;
  
  cmvn_sliding_opts="--norm-vars=false --center=true --cmn-window=300"
  # don't change $cmvn_sliding_opts, it should probably match the
  # options used in ../sid/train_diag_ubm.sh and ./train_lvtln_model.sh
  
  sifeats="ark,s,cs:add-deltas scp:$sdata/JOB/feats.scp ark:- | apply-cmvn-sliding $cmvn_sliding_opts ark:- ark:- | select-voiced-frames ark:- scp,s,cs:$sdata/JOB/vad.scp ark:- | subsample-feats --n=$subsample ark:- ark:- |"
  
  
  if [ $stage -le -1 ]; then
    echo "$0: computing Gaussian selection info."
  
    $cmd JOB=1:$nj $dir/log/gselect.JOB.log \
      gmm-gselect --n=$num_gselect $vtlndir/final.ali_dubm "$sifeats" \
        "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
  fi
  
  feats="$sifeats"
  
  x=0
  while [ $x -lt $num_iters ]; do
    if [ $stage -le $x ]; then
      echo "$0: pass $x of computing LVTLN transforms"
      
      if [ $x -eq 0 ]; then ubm=$vtlndir/final.ali_dubm; else ubm=$vtlndir/final.dubm; fi
  
      $cmd JOB=1:$nj $dir/log/lvtln.$x.JOB.log \
        gmm-global-gselect-to-post "$ubm" "$feats" \
          "ark,s,cs:gunzip -c $dir/gselect.JOB.gz|" ark:- \| \
        gmm-global-est-lvtln-trans \
          --logdet-scale=$logdet_scale --verbose=1 \
          $vtlndir/final.dubm $vtlndir/final.lvtln "$sifeats" ark,s,cs:- \
          ark:$dir/trans.$x.JOB ark,t:$dir/warp.$x.JOB || exit 1
    
      # consolidate the warps into one file.
      for j in $(seq $nj); do cat $dir/warp.$x.$j; done > $dir/warp.$x
      rm $dir/warp.$x.*
    fi
    feats="$sifeats transform-feats ark:$dir/trans.$x.JOB ark:- ark:- |"
    x=$[$x+1]
  done
  
  ln -sf warp.$[$x-1] $dir/utt2warp
  
  $cleanup && rm $dir/gselect.*.gz $dir/trans.*
  echo "$0: Distribution of classes for one job is below."
  grep 'Distribution of classes' $dir/log/lvtln.$[$x-1].1.log
  
  
  echo "$0: created warp factors in $dir/utt2warp"
  
  
  # Summarize warning messages...
  utils/summarize_warnings.pl  $dir/log
  
  echo "$0: Done getting VTLN warps in $dir"