#!/bin/bash
  
  set -e -o pipefail
  
  
  # This script is called from local/nnet3/run_tdnn.sh and local/chain/run_tdnn.sh (and may eventually
  # be called by more scripts).  It contains the common feature preparation and iVector-related parts
  # of the script.  See those scripts for examples of usage.
  
  
  stage=0
  train_set=train_960_cleaned    # you might set this to e.g. train_960
  gmm=tri6b_cleaned         # This specifies a GMM-dir from the features of the type you're training the system on;
                           # it should contain alignments for 'train_set'.
  num_threads_ubm=16
  num_processes=4
  nnet3_affix=_cleaned     # affix for exp/nnet3 directory to put iVector stuff in, so it
                           # becomes exp/nnet3_cleaned or whatever.
  
  . ./cmd.sh
  . ./path.sh
  . ./utils/parse_options.sh
  
  gmm_dir=exp/${gmm}
  ali_dir=exp/${gmm}_ali_${train_set}_sp
  
  for f in data/${train_set}/feats.scp ${gmm_dir}/final.mdl; do
    if [ ! -f $f ]; then
      echo "$0: expected file $f to exist"
      exit 1
    fi
  done
  
  if [ $stage -le 1 ]; then
    #Although the nnet will be trained by high resolution data, we still have to
    # perturb the normal data to get the alignment.  _sp stands for speed-perturbed
    echo "$0: preparing directory for low-resolution speed-perturbed data (for alignment)"
    utils/data/perturb_data_dir_speed_3way.sh data/${train_set} data/${train_set}_sp
    echo "$0: making MFCC features for low-resolution speed-perturbed data"
    steps/make_mfcc.sh --cmd "$train_cmd" --nj 50 data/${train_set}_sp || exit 1;
    steps/compute_cmvn_stats.sh data/${train_set}_sp || exit 1;
    echo "$0: fixing input data-dir to remove nonexistent features, in case some "
    echo ".. speed-perturbed segments were too short."
    utils/fix_data_dir.sh data/${train_set}_sp
  fi
  
  if [ $stage -le 2 ]; then
    if [ -f $ali_dir/ali.1.gz ]; then
      echo "$0: alignments in $ali_dir appear to already exist.  Please either remove them "
      echo " ... or use a later --stage option."
      exit 1
    fi
    echo "$0: aligning with the perturbed low-resolution data"
    steps/align_fmllr.sh --nj 100 --cmd "$train_cmd" \
      data/${train_set}_sp data/lang $gmm_dir $ali_dir || exit 1
  fi
  
  if [ $stage -le 3 ]; then
    # Create high-resolution MFCC features (with 40 cepstra instead of 13).
    # this shows how you can split across multiple file-systems.  we'll split the
    # MFCC dir across multiple locations.  You might want to be careful here, if you
    # have multiple copies of Kaldi checked out and run the same recipe, not to let
    # them overwrite each other.
    echo "$0: creating high-resolution MFCC features"
    mfccdir=data/${train_set}_sp_hires/data
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then
      utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/mfcc/librispeech-$(date +'%m_%d_%H_%M')/s5/$mfccdir/storage $mfccdir/storage
    fi
  
    for datadir in ${train_set}_sp test_clean test_other dev_clean dev_other; do
      utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
    done
  
    # do volume-perturbation on the training data prior to extracting hires
    # features; this helps make trained nnets more invariant to test data volume.
    utils/data/perturb_data_dir_volume.sh data/${train_set}_sp_hires
  
    for datadir in ${train_set}_sp test_clean test_other dev_clean dev_other; do
      steps/make_mfcc.sh --nj 70 --mfcc-config conf/mfcc_hires.conf \
        --cmd "$train_cmd" data/${datadir}_hires || exit 1;
      steps/compute_cmvn_stats.sh data/${datadir}_hires || exit 1;
      utils/fix_data_dir.sh data/${datadir}_hires
    done
  
    # now create a data subset.  60k is 1/5th of the training dataset (around 200 hours).
    utils/subset_data_dir.sh data/${train_set}_sp_hires 60000 data/${train_set}_sp_hires_60k
  fi
  
  
  if [ $stage -le 4 ]; then
    echo "$0: making a subset of data to train the diagonal UBM and the PCA transform."
    # We'll one hundredth of the data, since Librispeech is very large.
    mkdir -p exp/nnet3${nnet3_affix}/diag_ubm
    temp_data_root=exp/nnet3${nnet3_affix}/diag_ubm
  
    num_utts_total=$(wc -l <data/${train_set}_sp_hires/utt2spk)
    num_utts=$[$num_utts_total/100]
    utils/data/subset_data_dir.sh data/${train_set}_sp_hires \
       $num_utts ${temp_data_root}/${train_set}_sp_hires_subset
  
    echo "$0: computing a PCA transform from the hires data."
    steps/online/nnet2/get_pca_transform.sh --cmd "$train_cmd" \
        --splice-opts "--left-context=3 --right-context=3" \
        --max-utts 10000 --subsample 2 \
         ${temp_data_root}/${train_set}_sp_hires_subset \
         exp/nnet3${nnet3_affix}/pca_transform
  
    echo "$0: training the diagonal UBM."
    # Use 512 Gaussians in the UBM.
    steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 30 \
      --num-frames 700000 \
      --num-threads $num_threads_ubm \
      ${temp_data_root}/${train_set}_sp_hires_subset 512 \
      exp/nnet3${nnet3_affix}/pca_transform exp/nnet3${nnet3_affix}/diag_ubm
  fi
  
  
  if [ $stage -le 5 ]; then
    # iVector extractors can in general be sensitive to the amount of data, but
    # this one has a fairly small dim (defaults to 100) so we don't use all of it,
    # we use just the 60k subset (about one fifth of the data, or 200 hours).
    echo "$0: training the iVector extractor"
    steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 --num-processes $num_processes \
      data/${train_set}_sp_hires_60k exp/nnet3${nnet3_affix}/diag_ubm exp/nnet3${nnet3_affix}/extractor || exit 1;
  fi
  
  if [ $stage -le 6 ]; then
    echo "$0: extracting iVectors for training data"
    ivectordir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $ivectordir/storage ]; then
      utils/create_split_dir.pl /export/b{09,10,11,12}/$USER/kaldi-data/ivectors/librispeech-$(date +'%m_%d_%H_%M')/s5/$ivectordir/storage $ivectordir/storage
    fi
    # We extract iVectors on the speed-perturbed training data after combining
    # short segments, which will be what we train the system on.  With
    # --utts-per-spk-max 2, the script pairs the utterances into twos, and treats
    # each of these pairs as one speaker. this gives more diversity in iVectors..
    # Note that these are extracted 'online'.
  
    # having a larger number of speakers is helpful for generalization, and to
    # handle per-utterance decoding well (iVector starts at zero).
    utils/data/modify_speaker_info.sh --utts-per-spk-max 2 \
      data/${train_set}_sp_hires ${ivectordir}/${train_set}_sp_hires_max2
  
    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 60 \
      ${ivectordir}/${train_set}_sp_hires_max2 exp/nnet3${nnet3_affix}/extractor \
      $ivectordir || exit 1;
  fi
  
  if [ $stage -le 7 ]; then
    echo "$0: extracting iVectors for dev and test data"
    for data in test_clean test_other dev_clean dev_other; do
      steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 20 \
        data/${data}_hires exp/nnet3${nnet3_affix}/extractor \
        exp/nnet3${nnet3_affix}/ivectors_${data}_hires || exit 1;
    done
  fi
  
  exit 0;