#!/bin/bash
  
  # Copyright Johns Hopkins University (Author: Daniel Povey, Vijayaditya Peddinti) 2016.  Apache 2.0.
  # This script generates the ctm files for dev_aspire, test_aspire and eval_aspire
  # for scoring with ASpIRE scoring server.
  # It also provides the WER for dev_aspire data.
  
  set -e
  
  # general opts
  iter=
  stage=0
  decode_num_jobs=30
  num_jobs=30
  affix=
  
  # segmentation opts
  window=10
  overlap=5
  
  # ivector opts
  max_count=75 # parameter for extract_ivectors.sh
  sub_speaker_frames=6000
  ivector_scale=0.75
  filter_ctm=true
  weights_file=
  silence_weight=0.00001
  
  # decode opts
  pass2_decode_opts="--min-active 1000"
  lattice_beam=8
  extra_left_context=0 # change for (B)LSTM
  extra_right_context=0 # change for BLSTM
  frames_per_chunk=50 # change for (B)LSTM
  acwt=0.1 # important to change this when using chain models
  post_decode_acwt=1.0 # important to change this when using chain models
  extra_left_context_initial=0
  extra_right_context_final=0
  
  score_opts="--min-lmwt 6 --max-lmwt 13"
  
  . ./cmd.sh
  [ -f ./path.sh ] && . ./path.sh
  . utils/parse_options.sh || exit 1;
  
  if [ $# -ne 4 ]; then
    echo "Usage: $0 [options] <data-dir> <lang-dir> <graph-dir> <model-dir>"
    echo " Options:"
    echo "    --stage (0|1|2)   # start scoring script from part-way through."
    echo "e.g.:"
    echo "$0 dev_aspire data/lang exp/tri5a/graph_pp exp/nnet3/tdnn"
    exit 1;
  fi
  
  data_set=$1 #select from {dev_aspire, test_aspire, eval_aspire}
  lang=$2 # data/lang
  graph=$3 #exp/tri5a/graph_pp
  dir=$4 # exp/nnet3/tdnn
  
  model_affix=`basename $dir`
  ivector_dir=exp/nnet3
  ivector_affix=${affix:+_$affix}_chain_${model_affix}${iter:+_iter$iter}
  affix=_${affix}${iter:+_iter${iter}}
  
  segmented_data_set=${data_set}_uniformsegmented
  if [ $stage -le 1 ]; then
    local/generate_uniformly_segmented_data_dir.sh  \
      --overlap $overlap --window $window $data_set $segmented_data_set
  fi
  
  if [[ "$data_set" =~ "test_aspire" ]]; then
    out_file=single_dev_test${affix}_$model_affix.ctm
    act_data_set=test_aspire
  elif [[ "$data_set" =~ "eval_aspire" ]]; then
    out_file=single_eval${affix}_$model_affix.ctm
    act_data_set=eval_aspire
  elif [[ "$data_set" =~  "dev_aspire" ]]; then
    # we will just decode the directory without oracle segments file
    # as we would like to operate in the actual evaluation condition
    out_file=single_dev${affix}_${model_affix}.ctm
    act_data_set=dev_aspire
  else
    echo "$0: Unknown data-set $data_set"
    exit 1
  fi
  
  if [ $stage -le 2 ]; then
    echo "Extracting i-vectors, stage 1"
    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 20 \
      --max-count $max_count \
      data/${segmented_data_set}_hires $ivector_dir/extractor \
      $ivector_dir/ivectors_${segmented_data_set}${ivector_affix}_stage1;
    # float comparisons are hard in bash
    if [ `bc <<< "$ivector_scale != 1"` -eq 1 ]; then
      ivector_scale_affix=_scale$ivector_scale
    else
      ivector_scale_affix=
    fi
  
    if [ ! -z "$ivector_scale_affix" ]; then
      echo "$0: Scaling iVectors, stage 1"
      srcdir=$ivector_dir/ivectors_${segmented_data_set}${ivector_affix}_stage1
      outdir=$ivector_dir/ivectors_${segmented_data_set}${ivector_affix}${ivector_scale_affix}_stage1
      mkdir -p $outdir
      copy-matrix --scale=$ivector_scale scp:$srcdir/ivector_online.scp ark:- | \
        copy-feats --compress=true ark:-  ark,scp:$outdir/ivector_online.ark,$outdir/ivector_online.scp;
      cp $srcdir/ivector_period $outdir/ivector_period
    fi
  fi
  
  decode_dir=$dir/decode_${segmented_data_set}${affix}_pp
  # generate the lattices
  if [ $stage -le 3 ]; then
    echo "Generating lattices, stage 1"
    steps/nnet3/decode.sh --nj $decode_num_jobs --cmd "$decode_cmd" --config conf/decode.config \
      --acwt $acwt --post-decode-acwt $post_decode_acwt \
      --extra-left-context $extra_left_context  \
      --extra-right-context $extra_right_context  \
      --extra-left-context-initial $extra_left_context_initial \
      --extra-right-context-final $extra_right_context_final \
      --frames-per-chunk "$frames_per_chunk" \
      --online-ivector-dir $ivector_dir/ivectors_${segmented_data_set}${ivector_affix}${ivector_scale_affix}_stage1 \
      --skip-scoring true ${iter:+--iter $iter} \
      $graph data/${segmented_data_set}_hires ${decode_dir}_stage1;
  fi
  
  if [ $stage -le 4 ]; then
    if $filter_ctm; then
      if [ ! -z $weights_file ]; then
        echo "$0: Using provided vad weights file $weights_file"
        ivector_extractor_input=$weights_file
      else
        echo "$0 : Generating vad weights file"
        ivector_extractor_input=${decode_dir}_stage1/weights${affix}.gz
        local/extract_vad_weights.sh --cmd "$decode_cmd" ${iter:+--iter $iter} \
          data/${segmented_data_set}_hires $lang \
          ${decode_dir}_stage1 $ivector_extractor_input
      fi
    else
      # just use all the frames
      ivector_extractor_input=${decode_dir}_stage1
    fi
  fi
  
  if [ $stage -le 5 ]; then
    echo "Extracting i-vectors, stage 2 with input $ivector_extractor_input"
    # this does offline decoding, except we estimate the iVectors per
    # speaker, excluding silence (based on alignments from a DNN decoding), with a
    # different script.  This is just to demonstrate that script.
    # the --sub-speaker-frames is optional; if provided, it will divide each speaker
    # up into "sub-speakers" of at least that many frames... can be useful if
    # acoustic conditions drift over time within the speaker's data.
    steps/online/nnet2/extract_ivectors.sh --cmd "$train_cmd" --nj 20 \
      --silence-weight $silence_weight \
      --sub-speaker-frames $sub_speaker_frames --max-count $max_count \
      data/${segmented_data_set}_hires $lang $ivector_dir/extractor \
      $ivector_extractor_input $ivector_dir/ivectors_${segmented_data_set}${ivector_affix};
  fi
  
  if [ $stage -le 6 ]; then
    echo "Generating lattices, stage 2 with --acwt $acwt"
    rm -f ${decode_dir}_tg/.error
    steps/nnet3/decode.sh --nj $decode_num_jobs --cmd "$decode_cmd" --config conf/decode.config $pass2_decode_opts \
        --acwt $acwt --post-decode-acwt $post_decode_acwt \
        --extra-left-context $extra_left_context  \
        --extra-right-context $extra_right_context  \
        --extra-left-context-initial $extra_left_context_initial \
        --extra-right-context-final $extra_right_context_final \
        --frames-per-chunk "$frames_per_chunk" \
        --skip-scoring true ${iter:+--iter $iter} --lattice-beam $lattice_beam \
        --online-ivector-dir $ivector_dir/ivectors_${segmented_data_set}${ivector_affix} \
       $graph data/${segmented_data_set}_hires ${decode_dir}_tg || touch ${decode_dir}_tg/.error
    [ -f ${decode_dir}_tg/.error ] && echo "$0: Error decoding" && exit 1;
  fi
  
  if [ $stage -le 7 ]; then
    echo "Rescoring lattices"
    steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
      --skip-scoring true \
      ${lang}_pp_test{,_fg} data/${segmented_data_set}_hires \
      ${decode_dir}_{tg,fg};
  fi
  
  decode_dir=${decode_dir}_fg
  if [ $stage -le 8 ]; then
    local/score_aspire.sh --cmd "$decode_cmd" \
      $score_opts \
      --word-ins-penalties "0.0,0.25,0.5,0.75,1.0" \
      --ctm-beam 6 \
      ${iter:+--iter $iter} \
      --decode-mbr true \
      --tune-hyper true \
      $lang $decode_dir $act_data_set $segmented_data_set $out_file
  fi