#!/bin/bash
  # Copyright   2017   Johns Hopkins University (Author: Daniel Garcia-Romero)
  #             2017   Johns Hopkins University (Author: Daniel Povey)
  #        2017-2018   David Snyder
  #             2018   Ewald Enzinger
  #             2018   Zili Huang
  # Apache 2.0.
  #
  # See ../README.txt for more info on data required.
  # Results (diarization error rate) are inline in comments below.
  
  . ./cmd.sh
  . ./path.sh
  set -e
  mfccdir=`pwd`/mfcc
  vaddir=`pwd`/mfcc
  
  voxceleb1_root=/export/corpora/VoxCeleb1
  voxceleb2_root=/export/corpora/VoxCeleb2
  dihard_2018_dev=/export/corpora/LDC/LDC2018E31
  dihard_2018_eval=/export/corpora/LDC/LDC2018E32v1.1
  num_components=2048
  ivector_dim=400
  ivec_dir=exp/extractor_c${num_components}_i${ivector_dim}
  
  stage=0
  
  if [ $stage -le 0 ]; then
    local/make_voxceleb2.pl $voxceleb2_root dev data/voxceleb2_train
    local/make_voxceleb2.pl $voxceleb2_root test data/voxceleb2_test
  
    # Now prepare the VoxCeleb1 train and test data.  If you downloaded the corpus soon
    # after it was first released, you may need to use an older version of the script, which
    # can be invoked as follows:
    # local/make_voxceleb1.pl $voxceleb1_root data
    local/make_voxceleb1_v2.pl $voxceleb1_root dev data/voxceleb1_train
    local/make_voxceleb1_v2.pl $voxceleb1_root test data/voxceleb1_test
  
    # We'll train on all of VoxCeleb2, plus the training portion of VoxCeleb1.
    # This should give 7,351 speakers and 1,277,503 utterances.
    utils/combine_data.sh data/train data/voxceleb2_train data/voxceleb2_test data/voxceleb1_train
  
    # Prepare the development and evaluation set for DIHARD 2018.
    local/make_dihard_2018_dev.sh $dihard_2018_dev data/dihard_2018_dev
    local/make_dihard_2018_eval.sh $dihard_2018_eval data/dihard_2018_eval
  fi
  
  if [ $stage -le 1 ]; then
    # Make MFCCs for each dataset
    for name in train dihard_2018_dev dihard_2018_eval; do
      steps/make_mfcc.sh --write-utt2num-frames true \
        --mfcc-config conf/mfcc.conf --nj 40 --cmd "$train_cmd --max-jobs-run 20" \
        data/${name} exp/make_mfcc $mfccdir
      utils/fix_data_dir.sh data/${name}
    done
  
    # Compute the energy-based VAD for train
    sid/compute_vad_decision.sh --nj 40 --cmd "$train_cmd" \
      data/train exp/make_vad $vaddir
    utils/fix_data_dir.sh data/train
  
    # This writes features to disk after adding deltas and applying the sliding window CMN.
    # Although this is somewhat wasteful in terms of disk space, for diarization
    # it ends up being preferable to performing the CMN in memory.  If the CMN
    # were performed in memory it would need to be performed after the subsegmentation,
    # which leads to poorer results.
    for name in train dihard_2018_dev dihard_2018_eval; do
      local/prepare_feats.sh --nj 40 --cmd "$train_cmd" \
        data/$name data/${name}_cmn exp/${name}_cmn
      if [ -f data/$name/vad.scp ]; then
        cp data/$name/vad.scp data/${name}_cmn/
      fi
      if [ -f data/$name/segments ]; then
        cp data/$name/segments data/${name}_cmn/
      fi
      utils/fix_data_dir.sh data/${name}_cmn
    done
  
    echo "0.01" > data/train_cmn/frame_shift
    # Create segments to extract i-vectors from for PLDA training data.
    # The segments are created using an energy-based speech activity
    # detection (SAD) system, but this is not necessary.  You can replace
    # this with segments computed from your favorite SAD.
    diarization/vad_to_segments.sh --nj 40 --cmd "$train_cmd" \
        data/train_cmn data/train_cmn_segmented
  fi
  
  if [ $stage -le 2 ]; then
    # Train the UBM on VoxCeleb 1 and 2.
    sid/train_diag_ubm.sh --cmd "$train_cmd --mem 4G" \
      --nj 40 --num-threads 8 \
      data/train $num_components \
      exp/diag_ubm
  
    sid/train_full_ubm.sh --cmd "$train_cmd --mem 25G" \
      --nj 40 --remove-low-count-gaussians false \
      data/train \
      exp/diag_ubm exp/full_ubm
  fi
  
  if [ $stage -le 3 ]; then
    # In this stage, we train the i-vector extractor on a subset of VoxCeleb 1
    # and 2.
    #
    # Note that there are well over 1 million utterances in our training set,
    # and it takes an extremely long time to train the extractor on all of this.
    # Also, most of those utterances are very short.  Short utterances are
    # harmful for training the i-vector extractor.  Therefore, to reduce the
    # training time and improve performance, we will only train on the 100k
    # longest utterances.
    utils/subset_data_dir.sh \
      --utt-list <(sort -n -k 2 data/train/utt2num_frames | tail -n 100000) \
      data/train data/train_100k
  
    # Train the i-vector extractor.
    sid/train_ivector_extractor.sh --cmd "$train_cmd --mem 16G" \
      --ivector-dim $ivector_dim --num-iters 5 \
      exp/full_ubm/final.ubm data/train_100k \
      $ivec_dir
  fi
  
  if [ $stage -le 4 ]; then
    # Extract i-vectors for DIHARD 2018 development and evaluation set. 
    # We set apply-cmn false and apply-deltas false because we already add
    # deltas and apply cmn in stage 1.
    diarization/extract_ivectors.sh --cmd "$train_cmd --mem 20G" \
      --nj 40 --window 1.5 --period 0.75 --apply-cmn false --apply-deltas false \
      --min-segment 0.5 $ivec_dir \
      data/dihard_2018_dev_cmn $ivec_dir/ivectors_dihard_2018_dev
  
    diarization/extract_ivectors.sh --cmd "$train_cmd --mem 20G" \
      --nj 40 --window 1.5 --period 0.75 --apply-cmn false --apply-deltas false \
      --min-segment 0.5 $ivec_dir \
      data/dihard_2018_eval_cmn $ivec_dir/ivectors_dihard_2018_eval
  
    # Reduce the amount of training data for the PLDA training.
    utils/subset_data_dir.sh data/train_cmn_segmented 128000 data/train_cmn_segmented_128k
    # Extract i-vectors for the VoxCeleb, which is our PLDA training
    # data.  A long period is used here so that we don't compute too
    # many i-vectors for each recording.
    diarization/extract_ivectors.sh --cmd "$train_cmd --mem 25G" \
      --nj 40 --window 3.0 --period 10.0 --min-segment 1.5 --apply-cmn false --apply-deltas false \
      --hard-min true $ivec_dir \
      data/train_cmn_segmented_128k $ivec_dir/ivectors_train_segmented_128k
  fi
  
  if [ $stage -le 5 ]; then
    # Train a PLDA model on VoxCeleb, using DIHARD 2018 development set to whiten.
    "$train_cmd" $ivec_dir/ivectors_dihard_2018_dev/log/plda.log \
      ivector-compute-plda ark:$ivec_dir/ivectors_train_segmented_128k/spk2utt \
        "ark:ivector-subtract-global-mean \
        scp:$ivec_dir/ivectors_train_segmented_128k/ivector.scp ark:- \
        | transform-vec $ivec_dir/ivectors_dihard_2018_dev/transform.mat ark:- ark:- \
        | ivector-normalize-length ark:- ark:- |" \
      $ivec_dir/ivectors_dihard_2018_dev/plda || exit 1;
  fi
  
  # Perform PLDA scoring
  if [ $stage -le 6 ]; then
    # Perform PLDA scoring on all pairs of segments for each recording.
    diarization/score_plda.sh --cmd "$train_cmd --mem 4G" \
      --nj 20 $ivec_dir/ivectors_dihard_2018_dev $ivec_dir/ivectors_dihard_2018_dev \
      $ivec_dir/ivectors_dihard_2018_dev/plda_scores
  
    diarization/score_plda.sh --cmd "$train_cmd --mem 4G" \
      --nj 20 $ivec_dir/ivectors_dihard_2018_dev $ivec_dir/ivectors_dihard_2018_eval \
      $ivec_dir/ivectors_dihard_2018_eval/plda_scores
  fi
  
  # Cluster the PLDA scores using a stopping threshold.
  if [ $stage -le 7 ]; then
    # First, we find the threshold that minimizes the DER on DIHARD 2018 development set.
    mkdir -p $ivec_dir/tuning
    echo "Tuning clustering threshold for DIHARD 2018 development set"
    best_der=100
    best_threshold=0
  
    # The threshold is in terms of the log likelihood ratio provided by the
    # PLDA scores.  In a perfectly calibrated system, the threshold is 0.
    # In the following loop, we evaluate DER performance on DIHARD 2018 development 
    # set using some reasonable thresholds for a well-calibrated system.
    for threshold in -0.5 -0.4 -0.3 -0.2 -0.1 -0.05 0 0.05 0.1 0.2 0.3 0.4 0.5; do
      diarization/cluster.sh --cmd "$train_cmd --mem 4G" --nj 20 \
        --threshold $threshold --rttm-channel 1 $ivec_dir/ivectors_dihard_2018_dev/plda_scores \
        $ivec_dir/ivectors_dihard_2018_dev/plda_scores_t$threshold
  
      md-eval.pl -r data/dihard_2018_dev/rttm \
       -s $ivec_dir/ivectors_dihard_2018_dev/plda_scores_t$threshold/rttm \
       2> $ivec_dir/tuning/dihard_2018_dev_t${threshold}.log \
       > $ivec_dir/tuning/dihard_2018_dev_t${threshold}
  
      der=$(grep -oP 'DIARIZATION\ ERROR\ =\ \K[0-9]+([.][0-9]+)?' \
        $ivec_dir/tuning/dihard_2018_dev_t${threshold})
      if [ $(perl -e "print ($der < $best_der ? 1 : 0);") -eq 1 ]; then
        best_der=$der
        best_threshold=$threshold
      fi
    done
    echo "$best_threshold" > $ivec_dir/tuning/dihard_2018_dev_best
  
    diarization/cluster.sh --cmd "$train_cmd --mem 4G" --nj 20 \
      --threshold $(cat $ivec_dir/tuning/dihard_2018_dev_best) --rttm-channel 1 \
      $ivec_dir/ivectors_dihard_2018_dev/plda_scores $ivec_dir/ivectors_dihard_2018_dev/plda_scores
  
    # Cluster DIHARD 2018 evaluation set using the best threshold found for the DIHARD 
    # 2018 development set. The DIHARD 2018 development set is used as the validation 
    # set to tune the parameters. 
    diarization/cluster.sh --cmd "$train_cmd --mem 4G" --nj 20 \
      --threshold $(cat $ivec_dir/tuning/dihard_2018_dev_best) --rttm-channel 1 \
      $ivec_dir/ivectors_dihard_2018_eval/plda_scores $ivec_dir/ivectors_dihard_2018_eval/plda_scores
  
    mkdir -p $ivec_dir/results
    # Compute the DER on the DIHARD 2018 evaluation set. We use the official metrics of   
    # the DIHARD challenge. The DER is calculated with no unscored collars and including  
    # overlapping speech.
    md-eval.pl -r data/dihard_2018_eval/rttm \
      -s $ivec_dir/ivectors_dihard_2018_eval/plda_scores/rttm 2> $ivec_dir/results/threshold.log \
      > $ivec_dir/results/DER_threshold.txt
    der=$(grep -oP 'DIARIZATION\ ERROR\ =\ \K[0-9]+([.][0-9]+)?' \
      $ivec_dir/results/DER_threshold.txt)
    # Using supervised calibration, DER: 28.51%
    echo "Using supervised calibration, DER: $der%"
  fi
  
  # Cluster the PLDA scores using the oracle number of speakers
  if [ $stage -le 8 ]; then
    # In this section, we show how to do the clustering if the number of speakers
    # (and therefore, the number of clusters) per recording is known in advance.
    diarization/cluster.sh --cmd "$train_cmd --mem 4G" --nj 20 \
      --reco2num-spk data/dihard_2018_eval/reco2num_spk --rttm-channel 1 \
      $ivec_dir/ivectors_dihard_2018_eval/plda_scores $ivec_dir/ivectors_dihard_2018_eval/plda_scores_num_spk
  
    md-eval.pl -r data/dihard_2018_eval/rttm \
      -s $ivec_dir/ivectors_dihard_2018_eval/plda_scores_num_spk/rttm 2> $ivec_dir/results/num_spk.log \
      > $ivec_dir/results/DER_num_spk.txt
    der=$(grep -oP 'DIARIZATION\ ERROR\ =\ \K[0-9]+([.][0-9]+)?' \
      $ivec_dir/results/DER_num_spk.txt)
    # Using the oracle number of speakers, DER: 24.42%
    echo "Using the oracle number of speakers, DER: $der%"
  fi