run_2_chain_ddt1500h.sh

#!/usr/bin/env bash
set -e

# configs for 'chain'
affix=cnn_tdnnf_ddt1500h
ivector_affix=ddt1500h
stage=3
train_stage=-10
get_egs_stage=-10

# training options
num_epochs=4
initial_effective_lrate=0.00015
final_effective_lrate=0.000015
max_param_change=2.0
final_layer_normalize_target=0.5
num_jobs_initial=2
num_jobs_final=8
nj=20 #15
minibatch_size=128
dropout_schedule='0,0@0.20,0.3@0.50,0'
frames_per_eg=150,110,90
remove_egs=true
common_egs_dir=
xent_regularize=0.1

decode_nj=20
gmm=tri3
nnet3_affix=
decode_iter=

# End configuration section.
echo "$0 $@"  # Print the command line for logging

. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh

if ! cuda-compiled; then
  cat <<EOF && exit 1
This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
If you want to use GPUs (and have them), go to src/, and configure and make on a machine
where "nvcc" is installed.
EOF
fi

train_set=train
test_sets="liv5h lgv5h stv5h"
lang=data/lang_chain

gmm_dir=exp/$gmm
ali_dir=exp/${gmm}_ali
tree_dir=exp/chain${nnet3_affix}/tri4_cd_tree
lat_dir=exp/${gmm}_lats
dir=exp/chain${nnet3_affix}/tdnn${affix:+_$affix}
train_data_dir=data/${train_set}_hires
lores_train_data_dir=data/${train_set}
train_ivector_dir=exp/chain${nnet3_affix}/ivectors_${train_set}_${ivector_affix}


if [ $stage -le 3 ]; then
  mfccdir=mfcc_hires
  for datadir in ${train_set}; do
  	utils/copy_data_dir.sh data/${datadir} data/${datadir}_hires
    utils/data/perturb_data_dir_volume.sh data/${datadir}_hires_pitch || exit 1;
	steps/make_mfcc_pitch.sh --mfcc-config conf/mfcc_hires.conf --pitch-config conf/pitch.conf \
      --nj $nj data/${datadir}_hires_pitch exp/make_mfcc/ ${mfccdir}
    steps/compute_cmvn_stats.sh data/${datadir}_hires_pitch exp/make_mfcc ${mfccdir}
    utils/data/limit_feature_dim.sh 0:39 data/${datadir}_hires_pitch data/${datadir}_hires
    steps/compute_cmvn_stats.sh data/${datadir}_hires exp/make_mfcc ${mfccdir}
  done
fi

# extract ivector from unified data using the trained
if [ $stage -le 4 ]; then
  echo "$0: computing a subset of data to train the diagonal UBM."
  # We'll use about a quarter of the data.
  mkdir -p exp/chain${nnet3_affix}/diag_ubm_${ivector_affix}
  temp_data_root=exp/chain${nnet3_affix}/diag_ubm_${ivector_affix}

  num_utts_total=$(wc -l < data/${train_set}_hires/utt2spk)
  num_utts=$[$num_utts_total/4]
  utils/data/subset_data_dir.sh data/${train_set}_hires \
    $num_utts ${temp_data_root}/${train_set}_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 \
    --dim $(feat-to-dim scp:${temp_data_root}/${train_set}_subset/feats.scp -) \
    ${temp_data_root}/${train_set}_subset \
    exp/chain${nnet3_affix}/pca_transform_${ivector_affix}

  echo "$0: training the diagonal UBM."
  # Use 512 Gaussians in the UBM.
  steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj $nj \
    --num-frames 700000 \
    --num-threads 8 \
    ${temp_data_root}/${train_set}_subset 512 \
    exp/chain${nnet3_affix}/pca_transform_${ivector_affix} exp/chain${nnet3_affix}/diag_ubm_${ivector_affix}

  echo "$0: training the iVector extractor"
  steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj $nj \
    data/${train_set}_hires exp/chain${nnet3_affix}/diag_ubm_${ivector_affix} \
    exp/chain${nnet3_affix}/extractor_${ivector_affix} || exit 1;

  for datadir in ${train_set} ${test_sets}; do
    steps/online/nnet2/copy_data_dir.sh --utts-per-spk-max 2 data/${datadir}_hires data/${datadir}_hires_max2
    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj $nj \
      data/${datadir}_hires_max2 exp/chain${nnet3_affix}/extractor_${ivector_affix} exp/chain${nnet3_affix}/ivectors_${datadir}_${ivector_affix} || exit 1;
  done
fi

if [ $stage -le 5 ]; then
  # Get the alignments as lattices (gives the LF-MMI training more freedom).
  # use the same num-jobs as the alignments
  nj=$(cat $ali_dir/num_jobs) || exit 1;
  steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" $lores_train_data_dir \
    data/lang $gmm_dir $lat_dir
  rm $lat_dir/fsts.*.gz # save space
fi

if [ $stage -le 6 ]; then
  # Create a version of the lang/ directory that has one state per phone in the
  # topo file. [note, it really has two states.. the first one is only repeated
  # once, the second one has zero or more repeats.]
  rm -rf $lang
  cp -r data/lang $lang
  silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
  nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
  # Use our special topology... note that later on may have to tune this
  # topology.
  steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
fi

if [ $stage -le 7 ]; then
  # Build a tree using our new topology. This is the critically different
  # step compared with other recipes.
  steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
    --context-opts "--context-width=2 --central-position=1" \
    --cmd "$train_cmd" 5000 data/$train_set $lang $ali_dir $tree_dir
fi

if [ $stage -le 8 ]; then
  echo "$0: creating neural net configs using the xconfig parser";

  num_targets=$(tree-info $tree_dir/tree | grep num-pdfs | awk '{print $2}')
  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
  cnn_opts="l2-regularize=0.01"
  ivector_affine_opts="l2-regularize=0.0"
  affine_opts="l2-regularize=0.008 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
  tdnnf_first_opts="l2-regularize=0.008 dropout-proportion=0.0 bypass-scale=0.0"
  tdnnf_opts="l2-regularize=0.008 dropout-proportion=0.0 bypass-scale=0.75"
  linear_opts="l2-regularize=0.008 orthonormal-constraint=-1.0"
  prefinal_opts="l2-regularize=0.008"
  output_opts="l2-regularize=0.005"

  mkdir -p $dir/configs

  cat <<EOF > $dir/configs/network.xconfig
  input dim=100 name=ivector
  input dim=40 name=input

  # MFCC to filterbank
  idct-layer name=idct input=input dim=40 cepstral-lifter=22 affine-transform-file=$dir/configs/idct.mat

  linear-component name=ivector-linear $ivector_affine_opts dim=200 input=ReplaceIndex(ivector, t, 0)
  batchnorm-component name=ivector-batchnorm target-rms=0.025
  batchnorm-component name=idct-batchnorm input=idct

  combine-feature-maps-layer name=combine_inputs input=Append(idct-batchnorm, ivector-batchnorm) num-filters1=1 num-filters2=5 height=40
  conv-relu-batchnorm-layer name=cnn1 $cnn_opts height-in=40 height-out=40 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=64
  conv-relu-batchnorm-layer name=cnn2 $cnn_opts height-in=40 height-out=40 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=64
  conv-relu-batchnorm-layer name=cnn3 $cnn_opts height-in=40 height-out=20 height-subsample-out=2 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=128
  conv-relu-batchnorm-layer name=cnn4 $cnn_opts height-in=20 height-out=20 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=128
  conv-relu-batchnorm-layer name=cnn5 $cnn_opts height-in=20 height-out=10 height-subsample-out=2 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=256
  conv-relu-batchnorm-layer name=cnn6 $cnn_opts height-in=10 height-out=10 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=256

  # the first TDNN-F layer has no bypass
  tdnnf-layer name=tdnnf7 $tdnnf_first_opts dim=1536 bottleneck-dim=256 time-stride=0
  tdnnf-layer name=tdnnf8 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  tdnnf-layer name=tdnnf9 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  tdnnf-layer name=tdnnf10 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  tdnnf-layer name=tdnnf11 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  tdnnf-layer name=tdnnf12 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  tdnnf-layer name=tdnnf13 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  tdnnf-layer name=tdnnf14 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  tdnnf-layer name=tdnnf15 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  tdnnf-layer name=tdnnf16 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  tdnnf-layer name=tdnnf17 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  tdnnf-layer name=tdnnf18 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
  linear-component name=prefinal-l dim=256 $linear_opts

  prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts big-dim=1536 small-dim=256
  output-layer name=output include-log-softmax=false dim=$num_targets $output_opts

  prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts big-dim=1536 small-dim=256
  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor $output_opts
EOF

  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
fi

if [ $stage -le 9 ]; then
  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
    utils/create_split_dir.pl \
     /export/b{09,10,11,12}/$USER/kaldi-data/egs/swbd-$(date +'%m_%d_%H_%M')/s5c/$dir/egs/storage $dir/egs/storage
  fi

  steps/nnet3/chain/train.py --stage $train_stage \
    --use-gpu "wait" \
    --cmd "$decode_cmd" \
    --feat.online-ivector-dir exp/chain${nnet3_affix}/ivectors_${train_set}_${ivector_affix} \
    --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
    --chain.xent-regularize $xent_regularize \
    --chain.leaky-hmm-coefficient 0.1 \
    --chain.l2-regularize 0.00005 \
    --chain.apply-deriv-weights false \
    --chain.lm-opts="--num-extra-lm-states=2000" \
    --egs.dir "$common_egs_dir" \
    --egs.stage $get_egs_stage \
    --egs.opts "--frames-overlap-per-eg 0" \
    --egs.chunk-width $frames_per_eg \
    --trainer.dropout-schedule $dropout_schedule \
    --trainer.num-chunk-per-minibatch $minibatch_size \
    --trainer.frames-per-iter 1500000 \
    --trainer.num-epochs $num_epochs \
    --trainer.optimization.num-jobs-initial $num_jobs_initial \
    --trainer.optimization.num-jobs-final $num_jobs_final \
    --trainer.optimization.initial-effective-lrate $initial_effective_lrate \
    --trainer.optimization.final-effective-lrate $final_effective_lrate \
    --trainer.max-param-change $max_param_change \
    --cleanup.remove-egs $remove_egs \
    --feat-dir ${train_data_dir} \
    --tree-dir $tree_dir \
    --lat-dir $lat_dir \
    --dir $dir  || exit 1;
fi

if [ $stage -le 10 ]; then
  # Note: it might appear that this $lang directory is mismatched, and it is as
  # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
  # the lang directory.
  utils/mkgraph.sh --self-loop-scale 1.0 $lang $dir $dir/graph
fi

graph_dir=$dir/graph
if [ $stage -le 11 ]; then
  for test_set in $test_sets; do
    (
    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
      --nj $decode_nj --cmd "$decode_cmd" \
      --online-ivector-dir exp/chain${nnet3_affix}/ivectors_${test_set}_${ivector_affix} \
      $graph_dir data/${test_set}_hires $dir/decode_${test_set} || exit 1
    ) &
  done
  wait
fi

echo "chain model succeeded"
exit 0;