#!/bin/bash
  
  # Copyright   2012  Johns Hopkins University (Author: Daniel Povey)
  #             2013  Daniel Povey
  #             2016  Brno University of Technology (Author: Karel Vesely)
  # Apache 2.0.
  
  # This script trains a diagonal UBM that we'll use in online iVector estimation,
  # where the online-estimated iVector will be used as a secondary input to a deep
  # neural net for single-pass DNN-based decoding.
  
  # This script was modified from ../../sre08/v1/sid/train_diag_ubm.sh.
  # It trains a diagonal UBM on top of input features. We use the original features,
  # assuming they are already normalized (or transformed).
  
  # This script does not use the trained model from the source directory to
  # initialize the diagonal GMM; instead, we initialize the GMM using
  # gmm-global-init-from-feats, which sets the means to random data points and
  # then does some iterations of E-M in memory.  After the in-memory
  # initialization we train for a few iterations in parallel.
  # Note that there is a slight mismatch in that the source LDA+MLLT matrix
  # (final.mat) will have been estimated using standard CMVN, and we're using
  # online CMVN.  We don't think this will have much effect.
  
  
  # Begin configuration section.
  nj=4
  cmd=run.pl
  num_iters=4
  stage=-2
  num_gselect=30 # Number of Gaussian-selection indices to use while training
                 # the model.
  num_frames=500000 # number of frames to keep in memory for initialization
  num_iters_init=20
  initial_gauss_proportion=0.5 # Start with half the target number of Gaussians
  subsample=2 # subsample all features with this periodicity, in the main E-M phase.
  cleanup=true
  min_gaussian_weight=0.0001
  remove_low_count_gaussians=true # set this to false if you need #gauss to stay fixed.
  num_threads=8
  # End configuration section.
  
  echo "$0 $@"  # Print the command line for logging
  
  [ -f ./path.sh ] && . ./path.sh; # source the path.
  . parse_options.sh || exit 1;
  
  if [ $# != 3 ]; then
    echo "Usage: $0  <data> <num-gauss> <output-dir>"
    echo " e.g.: $0 data/train 1024 exp/diag_ubm"
    echo "Options: "
    echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
    echo "  --nj <num-jobs|4>                                # number of parallel jobs to run."
    echo "  --num-iters <niter|20>                           # number of iterations of parallel "
    echo "                                                   # training (default: $num_iters)"
    echo "  --stage <stage|-2>                               # stage to do partial re-run from."
    echo "  --num-gselect <n|30>                             # Number of Gaussians per frame to"
    echo "                                                   # limit computation to, for speed"
    echo " --subsample <n|5>                                 # In main E-M phase, use every n"
    echo "                                                   # frames (a speedup)"
    echo "  --num-frames <n|500000>                          # Maximum num-frames to keep in memory"
    echo "                                                   # for model initialization"
    echo "  --num-iters-init <n|20>                          # Number of E-M iterations for model"
    echo "                                                   # initialization"
    echo " --initial-gauss-proportion <proportion|0.5>       # Proportion of Gaussians to start with"
    echo "                                                   # in initialization phase (then split)"
    echo " --num-threads <n|16>                              # number of threads to use in initialization"
    echo "                                                   # phase (must match with parallel-opts option)"
    echo " --min-gaussian-weight <weight|0.0001>             # min Gaussian weight allowed in GMM"
    echo "                                                   # initialization (this relatively high"
    echo "                                                   # value keeps counts fairly even)"
    exit 1;
  fi
  
  set -euo pipefail
  
  data=$1
  num_gauss=$2
  dir=$3
  
  ! [ $num_gauss -gt 0 ] && echo "Bad num-gauss $num_gauss" && exit 1;
  
  sdata=$data/split$nj
  mkdir -p $dir/log
  utils/split_data.sh $data $nj || exit 1;
  
  for f in $data/feats.scp; do
     [ ! -f "$f" ] && echo "$0: expecting file $f to exist" && exit 1
  done
  
  # Note: there is no point subsampling all_feats, because gmm-global-init-from-feats
  # effectively does subsampling itself (it keeps a random subset of the features).
  all_feats="ark,s,cs:copy-feats scp:$data/feats.scp ark:- |"
  feats="ark,s,cs:copy-feats scp:$sdata/JOB/feats.scp ark:- | subsample-feats --n=$subsample ark:- ark:- |"
  
  num_gauss_init=$(perl -e "print int($initial_gauss_proportion * $num_gauss); ");
  ! [ $num_gauss_init -gt 0 ] && echo "Invalid num-gauss-init $num_gauss_init" && exit 1;
  
  if [ $stage -le -2 ]; then
    echo "$0: initializing model from E-M in memory, "
    echo "$0: starting from $num_gauss_init Gaussians, reaching $num_gauss;"
    echo "$0: for $num_iters_init iterations, using at most $num_frames frames of data"
  
    $cmd --num-threads $num_threads $dir/log/gmm_init.log \
      gmm-global-init-from-feats --num-threads=$num_threads --num-frames=$num_frames \
       --min-gaussian-weight=$min_gaussian_weight \
       --num-gauss=$num_gauss --num-gauss-init=$num_gauss_init --num-iters=$num_iters_init \
      "$all_feats" $dir/0.dubm
  fi
  
  # Store Gaussian selection indices on disk-- this speeds up the training passes.
  if [ $stage -le -1 ]; then
    echo "Getting Gaussian-selection info"
    $cmd JOB=1:$nj $dir/log/gselect.JOB.log \
      gmm-gselect --n=$num_gselect $dir/0.dubm "$feats" \
        "ark:|gzip -c >$dir/gselect.JOB.gz"
  fi
  
  echo "$0: will train for $num_iters iterations, in parallel over"
  echo "$0: $nj machines, parallelized with '$cmd'"
  
  for x in $(seq 0 $[$num_iters-1]); do
    echo "$0: Training pass $x"
    if [ $stage -le $x ]; then
    # Accumulate stats.
      $cmd JOB=1:$nj $dir/log/acc.${x}.JOB.log \
        gmm-global-acc-stats "--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|" \
        $dir/$x.dubm "$feats" $dir/$x.JOB.acc
      if [ $x -lt $[$num_iters-1] ]; then # Don't remove low-count Gaussians till last iter,
        opt="--remove-low-count-gaussians=false" # or gselect info won't be valid any more.
      else
        opt="--remove-low-count-gaussians=$remove_low_count_gaussians"
      fi
      $cmd $dir/log/update.${x}.log \
        gmm-global-est $opt --min-gaussian-weight=$min_gaussian_weight $dir/${x}.dubm "gmm-global-sum-accs - $dir/${x}.*.acc|" \
        $dir/$[$x+1].dubm
      rm $dir/$x.*.acc $dir/$x.dubm
    fi
  done
  
  rm $dir/gselect.*.gz
  mv $dir/$num_iters.dubm $dir/final.dubm
  
  exit 0 # Done!