#!/bin/bash
  
  # Copyright     2013  Daniel Povey
  #          2014-2017  David Snyder
  #               2015  Johns Hopkins University (Author: Daniel Garcia-Romero)
  #               2015  Johns Hopkins University (Author: Daniel Povey)
  # Apache 2.0.
  
  # This script extracts iVectors for a set of utterances, given
  # features and a trained DNN-based iVector extractor.
  
  # Begin configuration section.
  nj=30
  cmd="run.pl"
  stage=0
  min_post=0.025 # Minimum posterior to use (posteriors below this are pruned out)
  posterior_scale=1.0 # This scale helps to control for successive features being highly
                      # correlated.  E.g. try 0.1 or 0.3.
  use_gpu=true
  chunk_size=256
  nnet_job_opt=
  # End configuration section.
  
  echo "$0 $@"  # Print the command line for logging
  
  if [ -f path.sh ]; then . ./path.sh; fi
  . parse_options.sh || exit 1;
  
  
  if [ $# != 5 ]; then
    echo "Usage: $0 <extractor-dir> <data> <ivector-dir>"
    echo " e.g.: $0 exp/extractor_2048_male data/train_male exp/ivectors_male"
    echo "main options (for others, see top of script file)"
    echo "  --config <config-file>                           # config containing options"
    echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
    echo "  --nj <n|10>                                      # Number of jobs"
    echo "  --stage <stage|0>                                # To control partial reruns"
    echo "  --num-gselect <n|20>                             # Number of Gaussians to select using"
    echo "                                                   # diagonal model."
    echo "  --min-post <min-post|0.025>                      # Pruning threshold for posteriors"
    echo "  --nnet-job-opt <option|''>                       # Options for the DNN jobs which add to or"
    echo "                                                   # replace those specified by --cmd"
    exit 1;
  fi
  
  srcdir=$1
  nnet=$2
  data=$3
  data_dnn=$4
  dir=$5
  
  gpu_opt=""
  if $use_gpu; then
    nnet_job_opt="$nnet_job_opt --gpu 1"
    gpu_opt="--use-gpu=yes"
    if ! cuda-compiled; then
      echo "$0: WARNING: you are trying to use the GPU but you have not compiled"
      echo "   for CUDA.  If you have GPUs and have nvcc installed, go to src/"
      echo "   and do ./configure; make"
      exit 1
    fi
  else
    echo "$0: without using a GPU this will be slow."
    gpu_opt="--use-gpu=no"
  fi
  
  for f in $srcdir/final.ie $srcdir/final.ubm $data/feats.scp ; do
    [ ! -f $f ] && echo "No such file $f" && exit 1;
  done
  
  # Set various variables.
  mkdir -p $dir/log
  sdata=$data/split$nj;
  utils/split_data.sh $data $nj || exit 1;
  
  sdata_dnn=$data_dnn/split$nj;
  utils/split_data.sh $data_dnn $nj || exit 1;
  
  delta_opts=`cat $srcdir/delta_opts 2>/dev/null`
  
  ## Set up features.
  feats="ark,s,cs:add-deltas $delta_opts scp:$sdata/JOB/feats.scp ark:- | apply-cmvn-sliding --norm-vars=false --center=true --cmn-window=300 ark:- ark:- | select-voiced-frames ark:- scp,s,cs:$sdata/JOB/vad.scp ark:- |"
  
  nnet_feats="ark,s,cs:apply-cmvn-sliding --center=true scp:$sdata_dnn/JOB/feats.scp ark:- |"
  
  if [ $stage -le 0 ]; then
    echo "$0: extracting iVectors"
    for g in $(seq $nj); do
      $cmd $nnet_job_opt $dir/log/extract_ivectors.$g.log \
        nnet-am-compute $gpu_opt --apply-log=true --chunk-size=${chunk_size} \
          $nnet "`echo $nnet_feats | sed s/JOB/$g/g`" ark:- \
          \| select-voiced-frames ark:- scp,s,cs:$sdata/$g/vad.scp ark:- \
          \| logprob-to-post --min-post=$min_post ark:- ark:- \| \
          scale-post ark:- $posterior_scale ark:- \| \
          ivector-extract --verbose=2 $srcdir/final.ie \
          "`echo $feats | sed s/JOB/$g/g`" ark,s,cs:- \
          ark,scp,t:$dir/ivector.$g.ark,$dir/ivector.$g.scp || exit 1 &
    done
    wait
  fi
  
  if [ $stage -le 1 ]; then
    echo "$0: combining iVectors across jobs"
    for j in $(seq $nj); do cat $dir/ivector.$j.scp; done >$dir/ivector.scp || exit 1;
  fi
  
  if [ $stage -le 2 ]; then
    # Be careful here: the speaker-level iVectors are now length-normalized,
    # even if they are otherwise the same as the utterance-level ones.
    echo "$0: computing mean of iVectors for each speaker and length-normalizing"
    $cmd $dir/log/speaker_mean.log \
      ivector-normalize-length scp:$dir/ivector.scp  ark:- \| \
      ivector-mean ark:$data/spk2utt ark:- ark:- ark,t:$dir/num_utts.ark \| \
      ivector-normalize-length ark:- ark,scp:$dir/spk_ivector.ark,$dir/spk_ivector.scp || exit 1;
  fi