#!/bin/bash
  
  # Copyright 2012-2015  Brno University of Technology (Author: Karel Vesely)
  # Apache 2.0
  
  # This example shows how to build CNN with 2D convolution along both frequency 
  # and time axis. First we train CNN, then build RBMs on top, then do train 
  # per-frame training and sequence-discriminative training.
  
  # Note: With DNNs in RM, the optimal LMWT is 2-6. Don't be tempted to try acwt's like 0.2, 
  # the value 0.1 is better both for decoding and sMBR.
  
  . ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
             ## This relates to the queue.
  
  . ./path.sh ## Source the tools/utils (import the queue.pl)
  
  # Note: With DNNs in RM, the optimal LMWT is 2-6. Don't be tempted to try acwt's like 0.2, 
  # the value 0.1 is better both for decoding and sMBR.
  
  dev=data-fbank/test
  train=data-fbank/train
  
  dev_original=data/test
  train_original=data/train
  
  gmm=exp/tri3b
  
  stage=0
  . utils/parse_options.sh
  
  set -eu
  
  # Make the FBANK features,
  [ ! -e $dev ] && if [ $stage -le 0 ]; then
    # Dev set
    utils/copy_data_dir.sh $dev_original $dev || exit 1; rm $dev/{cmvn,feats}.scp
    steps/make_fbank_pitch.sh --nj 10 --cmd "$train_cmd" \
       $dev $dev/log $dev/data || exit 1;
    steps/compute_cmvn_stats.sh $dev $dev/log $dev/data || exit 1;
    # Training set
    utils/copy_data_dir.sh $train_original $train || exit 1; rm $train/{cmvn,feats}.scp
    steps/make_fbank_pitch.sh --nj 10 --cmd "$train_cmd" \
       $train $train/log $train/data || exit 1;
    steps/compute_cmvn_stats.sh $train $train/log $train/data || exit 1;
    # Split the training set
    utils/subset_data_dir_tr_cv.sh --cv-spk-percent 10 $train ${train}_tr90 ${train}_cv10
  fi
  
  # Run the CNN pre-training,
  if [ $stage -le 1 ]; then
    dir=exp/cnn4g-2D
    ali=${gmm}_ali
    # Train
    $cuda_cmd $dir/log/train_nnet.log \
      steps/nnet/train.sh \
        --cmvn-opts "--norm-means=true --norm-vars=true" \
        --delta-opts "--delta-order=2" --splice 5 \
        --network-type cnn2d --cnn-proto-opts "--pool1-y-len=4 --pool1-y-step=4 --pitch-dim=3" \
        --hid-layers 2 --learn-rate 0.008 \
        ${train}_tr90 ${train}_cv10 data/lang $ali $ali $dir || exit 1;
    # Decode
    steps/nnet/decode.sh --nj 20 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt 0.1 \
      $gmm/graph $dev $dir/decode || exit 1;
  fi
  
  # Pre-train stack of RBMs on top of the convolutional layers (4 layers, 1024 units),
  if [ $stage -le 2 ]; then
    dir=exp/cnn4g-2D_pretrain-dbn
    transf_cnn=exp/cnn4g-2D/final.feature_transform_cnn # transform with convolutional layers
    # Train
    $cuda_cmd $dir/log/pretrain_dbn.log \
      steps/nnet/pretrain_dbn.sh --nn-depth 4 --hid-dim 1024 --rbm-iter 20 \
      --feature-transform $transf_cnn --input-vis-type bern \
      --param-stddev-first 0.05 --param-stddev 0.05 \
      $train $dir || exit 1
  fi
  
  # Re-align using CNN,
  if [ $stage -le 3 ]; then
    dir=exp/cnn4g-2D
    steps/nnet/align.sh --nj 20 --cmd "$train_cmd" \
      $train data/lang $dir ${dir}_ali || exit 1
  fi
  
  # Train the DNN optimizing cross-entropy,
  if [ $stage -le 4 ]; then
    dir=exp/cnn4g-2D_pretrain-dbn_dnn; [ ! -d $dir ] && mkdir -p $dir/log;
    ali=exp/cnn4g-2D_ali
    feature_transform=exp/cnn4g-2D/final.feature_transform
    feature_transform_dbn=exp/cnn4g-2D_pretrain-dbn/final.feature_transform
    dbn=exp/cnn4g-2D_pretrain-dbn/4.dbn
    cnn_dbn=$dir/cnn_dbn.nnet
    { # Concatenate CNN layers and DBN,
      num_components=$(nnet-info $feature_transform | grep -m1 num-components | awk '{print $2;}')
      cnn="nnet-copy --remove-first-components=$num_components $feature_transform_dbn - |"
      nnet-concat "$cnn" $dbn $cnn_dbn 2>$dir/log/concat_cnn_dbn.log || exit 1 
    }
    # Train
    $cuda_cmd $dir/log/train_nnet.log \
      steps/nnet/train.sh --feature-transform $feature_transform --dbn $cnn_dbn --hid-layers 0 \
      ${train}_tr90 ${train}_cv10 data/lang $ali $ali $dir || exit 1;
    # Decode (reuse HCLG graph)
    steps/nnet/decode.sh --nj 20 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt 0.1 \
      $gmm/graph $dev $dir/decode || exit 1;
  fi
  
  
  # Sequence training using sMBR criterion, we do Stochastic-GD with per-utterance updates.
  # Note: With DNNs in RM, the optimal LMWT is 2-6. Don't be tempted to try acwt's like 0.2, 
  # the value 0.1 is better both for decoding and sMBR.
  dir=exp/cnn4g-2D_pretrain-dbn_dnn_smbr
  srcdir=exp/cnn4g-2D_pretrain-dbn_dnn
  acwt=0.1
  
  # First we generate lattices and alignments,
  if [ $stage -le 4 ]; then
    steps/nnet/align.sh --nj 20 --cmd "$train_cmd" \
      $train data/lang $srcdir ${srcdir}_ali || exit 1;
    steps/nnet/make_denlats.sh --nj 20 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt $acwt \
      $train data/lang $srcdir ${srcdir}_denlats || exit 1;
  fi
  
  # Re-train the DNN by 6 iterations of sMBR,
  if [ $stage -le 5 ]; then
    steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 6 --acwt $acwt --do-smbr true \
      $train data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
    # Decode
    for ITER in 6 3 1; do
      steps/nnet/decode.sh --nj 20 --cmd "$decode_cmd" --config conf/decode_dnn.config \
        --nnet $dir/${ITER}.nnet --acwt $acwt \
        $gmm/graph $dev $dir/decode_it${ITER} || exit 1
    done 
  fi
  
  echo Success
  exit 0