#!/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=
num_utts_subset=300    # number of utterances in validation and training
                       # subsets used for shrinkage and diagnostics
hidden_layer_dim=300
within_class_factor=0.0001
num_valid_frames_combine=0 # #valid frames for combination weights at the very end.
num_train_frames_combine=10000 # # train frames for the above.
num_frames_diagnostic=4000 # number of frames for "compute_prob" jobs
samples_per_iter=400000 # each iteration of training, see this many samples
                        # per job.  This is just a guideline; it will pick a number
                        # that divides the number of samples in the entire data.
transform_dir=     # If supplied, overrides alidir
num_jobs_nnet=16    # Number of neural net jobs to run in parallel
stage=0
io_opts="-tc 5" # for jobs with a lot of I/O, limits the number running at one time. 
splice_width=4 # meaning +- 4 frames on each side for second LDA
spk_vecs_dir=
random_copy=false

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_egs.sh [opts] <data> <lang> <ali-dir> <exp-dir>"
  echo " e.g.: steps/nnet2/get_egs.sh data/train data/lang exp/tri3_ali exp/tri4_nnet"
  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 "  --num-jobs-nnet <num-jobs|16>                    # Number of parallel jobs to use for main neural net"
  echo "                                                   # training (will affect results as well as speed; try 8, 16)"
  echo "                                                   # Note: if you increase this, you may want to also increase"
  echo "                                                   # the learning rate."
  echo "  --samples-per-iter <#samples|400000>             # Number of samples of data to process per iteration, per"
  echo "                                                   # process."
  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 "  --num-frames-diagnostic <#frames|4000>           # Number of frames used in computing (train,valid) diagnostics"
  echo "  --num-valid-frames-combine <#frames|10000>       # Number of frames used in getting combination weights at the"
  echo "                                                   # very end."
  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


# Get list of validation utterances. 
awk '{print $1}' $data/utt2spk | utils/shuffle_list.pl | head -$num_utts_subset \
    > $dir/valid_uttlist || exit 1;

if [ -f $data/utt2uniq ]; then
  echo "File $data/utt2uniq exists, so augmenting valid_uttlist to"
  echo "include all perturbed versions of the same 'real' utterances."
  mv $dir/valid_uttlist $dir/valid_uttlist.tmp
  utils/utt2spk_to_spk2utt.pl $data/utt2uniq > $dir/uniq2utt
  cat $dir/valid_uttlist.tmp | utils/apply_map.pl $data/utt2uniq | \
    sort | uniq | utils/apply_map.pl $dir/uniq2utt | \
    awk '{for(n=1;n<=NF;n++) print $n;}' | sort  > $dir/valid_uttlist
  rm $dir/uniq2utt $dir/valid_uttlist.tmp
fi

awk '{print $1}' $data/utt2spk | utils/filter_scp.pl --exclude $dir/valid_uttlist | \
     head -$num_utts_subset > $dir/train_subset_uttlist || exit 1;

[ -z "$transform_dir" ] && transform_dir=$alidir

## 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 $transform_dir/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:- |"
    valid_feats="ark,s,cs:utils/filter_scp.pl $dir/valid_uttlist $data/feats.scp | apply-cmvn --norm-vars=false --utt2spk=ark:$data/utt2spk scp:$data/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:- |"
      valid_feats="ark,s,cs:utils/filter_scp.pl $dir/valid_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:- |"
      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 $transform_dir/trans.1 ] && [ $feat_type != "raw" ]; then
  echo "$0: using transforms from $transform_dir"
  feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
  valid_feats="$valid_feats transform-feats --utt2spk=ark:$data/utt2spk 'ark:cat $transform_dir/trans.*|' ark:- ark:- |"
  train_subset_feats="$train_subset_feats transform-feats --utt2spk=ark:$data/utt2spk 'ark:cat $transform_dir/trans.*|' ark:- ark:- |"
fi
if [ -f $transform_dir/raw_trans.1 ] && [ $feat_type == "raw" ]; then
  echo "$0: using raw-fMLLR transforms from $transform_dir"
  feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/raw_trans.JOB ark:- ark:- |"
  valid_feats="$valid_feats transform-feats --utt2spk=ark:$data/utt2spk 'ark:cat $transform_dir/raw_trans.*|' ark:- ark:- |"
  train_subset_feats="$train_subset_feats transform-feats --utt2spk=ark:$data/utt2spk 'ark:cat $transform_dir/raw_trans.*|' ark:- ark:- |"
fi

if [ $stage -le 0 ]; then
  echo "$0: working out number of frames of training data"
  num_frames=`feat-to-len scp:$data/feats.scp ark,t:- | awk '{x += $2;} END{print x;}'` || exit 1;
  echo $num_frames > $dir/num_frames
else
  num_frames=`cat $dir/num_frames` || exit 1;
fi

# Working out number of iterations per epoch.
iters_per_epoch=`perl -e "print int($num_frames/($samples_per_iter * $num_jobs_nnet) + 0.5);"` || exit 1;
[ $iters_per_epoch -eq 0 ] && iters_per_epoch=1
samples_per_iter_real=$[$num_frames/($num_jobs_nnet*$iters_per_epoch)]
echo "$0: Every epoch, splitting the data up into $iters_per_epoch iterations,"
echo "$0: giving samples-per-iteration of $samples_per_iter_real (you requested $samples_per_iter)."


## If --est-lda=true, o LDA on top of whatever features we already have; store
## the matrix which we'll put into the neural network as a constant.

feat_dim=`feat-to-dim "$train_subset_feats" -` || exit 1;
lda_dim=$[$feat_dim*(1+2*($splice_width))]; # No dim reduction.

nnet_context_opts="--left-context=$splice_width --right-context=$splice_width"
mkdir -p $dir/egs

if [ ! -z $spk_vecs_dir ]; then
  [ ! -f $spk_vecs_dir/vecs.1 ] && echo "No such file $spk_vecs_dir/vecs.1" && exit 1;
  spk_vecs_opt=("--spk-vecs=ark:cat $spk_vecs_dir/vecs.*|" "--utt2spk=ark:$data/utt2spk")
else
  spk_vecs_opt=()
fi

if [ $stage -le 2 ]; then
  echo "Getting validation and training subset examples."
  rm $dir/.error 2>/dev/null
  $cmd $dir/log/create_valid_subset.log \
    nnet-get-egs $nnet_context_opts "${spk_vecs_opt[@]}" "$valid_feats" \
     "ark,cs:gunzip -c $alidir/ali.*.gz | ali-to-pdf $alidir/final.mdl ark:- ark:- | ali-to-post ark:- ark:- |" \
     "ark:$dir/egs/valid_all.egs" || touch $dir/.error &
  $cmd $dir/log/create_train_subset.log \
    nnet-get-egs $nnet_context_opts "${spk_vecs_opt[@]}" "$train_subset_feats" \
     "ark,cs:gunzip -c $alidir/ali.*.gz | ali-to-pdf $alidir/final.mdl ark:- ark:- | ali-to-post ark:- ark:- |" \
     "ark:$dir/egs/train_subset_all.egs" || touch $dir/.error &
  wait;
  [ -f $dir/.error ] && exit 1;
  echo "Getting subsets of validation examples for diagnostics and combination."
  $cmd $dir/log/create_valid_subset_combine.log \
    nnet-subset-egs --n=$num_valid_frames_combine ark:$dir/egs/valid_all.egs \
        ark:$dir/egs/valid_combine.egs || touch $dir/.error &
  $cmd $dir/log/create_valid_subset_diagnostic.log \
    nnet-subset-egs --n=$num_frames_diagnostic ark:$dir/egs/valid_all.egs \
    ark:$dir/egs/valid_diagnostic.egs || touch $dir/.error &

  $cmd $dir/log/create_train_subset_combine.log \
    nnet-subset-egs --n=$num_train_frames_combine ark:$dir/egs/train_subset_all.egs \
    ark:$dir/egs/train_combine.egs || touch $dir/.error &
  $cmd $dir/log/create_train_subset_diagnostic.log \
    nnet-subset-egs --n=$num_frames_diagnostic ark:$dir/egs/train_subset_all.egs \
    ark:$dir/egs/train_diagnostic.egs || touch $dir/.error &
  wait
  cat $dir/egs/valid_combine.egs $dir/egs/train_combine.egs > $dir/egs/combine.egs

  for f in $dir/egs/{combine,train_diagnostic,valid_diagnostic}.egs; do
    [ ! -s $f ] && echo "No examples in file $f" && exit 1;
  done
  rm $dir/egs/valid_all.egs $dir/egs/train_subset_all.egs $dir/egs/{train,valid}_combine.egs
fi

if [ $stage -le 3 ]; then
  mkdir -p $dir/temp

  # Other scripts might need to know the following info:
  echo $num_jobs_nnet >$dir/egs/num_jobs_nnet
  echo $iters_per_epoch >$dir/egs/iters_per_epoch
  echo $samples_per_iter_real >$dir/egs/samples_per_iter

  echo "Creating training examples";
  # in $dir/egs, create $num_jobs_nnet separate files with training examples.
  # The order is not randomized at this point.

  egs_list=
  for n in `seq 1 $num_jobs_nnet`; do
    egs_list="$egs_list ark:$dir/egs/egs_orig.$n.JOB.ark"
  done
  echo "Generating training examples on disk"
  # The examples will go round-robin to egs_list.
  $cmd $io_opts JOB=1:$nj $dir/log/get_egs.JOB.log \
    nnet-get-egs $nnet_context_opts "${spk_vecs_opt[@]}" "$feats" \
    "ark,cs:gunzip -c $alidir/ali.JOB.gz | ali-to-pdf $alidir/final.mdl ark:- ark:- | ali-to-post ark:- ark:- |" ark:- \| \
    nnet-copy-egs ark:- $egs_list || exit 1;
fi

if [ $stage -le 4 ]; then
  # combine all the "egs_orig.JOB.*.scp" (over the $nj splits of the data) and
  # then split into multiple parts egs.JOB.*.scp for different parts of the
  # data, 0 .. $iters_per_epoch-1.

  if [ $iters_per_epoch -eq 1 ]; then
    echo "Since iters-per-epoch == 1, just concatenating the data."
    for n in `seq 1 $num_jobs_nnet`; do
      cat $dir/egs/egs_orig.$n.*.ark > $dir/egs/egs_tmp.$n.0.ark || exit 1;
      rm $dir/egs/egs_orig.$n.*.ark  # don't "|| exit 1", due to NFS bugs...
    done
  else # We'll have to split it up using nnet-copy-egs.
    egs_list=
    for n in `seq 0 $[$iters_per_epoch-1]`; do
      egs_list="$egs_list ark:$dir/egs/egs_tmp.JOB.$n.ark"
    done
    # note, the "|| true" below is a workaround for NFS bugs
    # we encountered running this script with Debian-7, NFS-v4.
    $cmd $io_opts JOB=1:$num_jobs_nnet $dir/log/split_egs.JOB.log \
      nnet-copy-egs --random=$random_copy --srand=JOB \
        "ark:cat $dir/egs/egs_orig.JOB.*.ark|" $egs_list '&&' \
        '(' rm $dir/egs/egs_orig.JOB.*.ark '||' true ')' || exit 1;
  fi
fi

if [ $stage -le 5 ]; then
  # Next, shuffle the order of the examples in each of those files.
  # Each one should not be too large, so we can do this in memory.
  echo "Shuffling the order of training examples"
  echo "(in order to avoid stressing the disk, these won't all run at once)."


  # note, the "|| true" below is a workaround for NFS bugs
  # we encountered running this script with Debian-7, NFS-v4.
  for n in `seq 0 $[$iters_per_epoch-1]`; do
    $cmd $io_opts JOB=1:$num_jobs_nnet $dir/log/shuffle.$n.JOB.log \
      nnet-shuffle-egs "--srand=\$[JOB+($num_jobs_nnet*$n)]" \
      ark:$dir/egs/egs_tmp.JOB.$n.ark ark:$dir/egs/egs.JOB.$n.ark '&&' \
      '(' rm $dir/egs/egs_tmp.JOB.$n.ark '||' true ')' || exit 1;
  done
fi

echo "$0: Finished preparing training examples"