run_tdnn.sh

#!/bin/bash

# 1h is as 1g but a re-tuned model based on resnet-style TDNN-F layers with
# bypass connections.  Below, 1h2 and 1h3 are just reruns of 1h with different
# --affix options, to give some idea of the run-to-run variation.

# local/chain/compare_wer.sh --online exp/chain/tdnn1g_sp exp/chain/tdnn1h_sp exp/chain/tdnn1h2_sp exp/chain/tdnn1h3_sp
# System                tdnn1g_sp tdnn1h_sp tdnn1h2_sp tdnn1h3_sp
#WER dev_clean_2 (tgsmall)      13.50     12.09     12.23     12.19
#             [online:]         13.52     12.11     12.25     12.14
#WER dev_clean_2 (tglarge)       9.79      8.59      8.64      8.73
#             [online:]          9.79      8.76      8.65      8.78
# Final train prob        -0.0460   -0.0493   -0.0490   -0.0493
# Final valid prob        -0.0892   -0.0805   -0.0803   -0.0813
# Final train prob (xent)   -1.1739   -1.1730   -1.1742   -1.1749
# Final valid prob (xent)   -1.4487   -1.3872   -1.3857   -1.3913
# Num-params                 6234672   5207856   5207856   5207856


# exp/chain/tdnn1g_sp: num-iters=25 nj=2..5 num-params=6.2M dim=40+100->2328 combine=-0.056->-0.055 (over 3) xent:train/valid[15,24,final]=(-1.50,-1.23,-1.17/-1.73,-1.52,-1.45) logprob:train/valid[15,24,final]=(-0.063,-0.051,-0.046/-0.101,-0.094,-0.089)
# exp/chain/tdnn1h_sp: num-iters=34 nj=2..5 num-params=5.2M dim=40+100->2328 combine=-0.049->-0.046 (over 4) xent:train/valid[21,33,final]=(-1.50,-1.22,-1.17/-1.66,-1.44,-1.39) logprob:train/valid[21,33,final]=(-0.068,-0.055,-0.049/-0.097,-0.088,-0.080)
# exp/chain/tdnn1h2_sp: num-iters=34 nj=2..5 num-params=5.2M dim=40+100->2328 combine=-0.049->-0.046 (over 4) xent:train/valid[21,33,final]=(-1.50,-1.22,-1.17/-1.67,-1.43,-1.39) logprob:train/valid[21,33,final]=(-0.068,-0.055,-0.049/-0.096,-0.087,-0.080)
# exp/chain/tdnn1h3_sp: num-iters=34 nj=2..5 num-params=5.2M dim=40+100->2328 combine=-0.050->-0.046 (over 4) xent:train/valid[21,33,final]=(-1.51,-1.23,-1.17/-1.67,-1.45,-1.39) logprob:train/valid[21,33,final]=(-0.068,-0.055,-0.049/-0.097,-0.089,-0.081)

# Set -e here so that we catch if any executable fails immediately
set -euo pipefail

# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
stage=0
decode_nj=10
train_set=train
test_sets=test
gmm=tri3
nnet3_affix=

# The rest are configs specific to this script.  Most of the parameters
# are just hardcoded at this level, in the commands below.
affix=1h   # affix for the TDNN directory name
tree_affix=
train_stage=-10
get_egs_stage=-10
decode_iter=

# training options
# training chunk-options
chunk_width=140,100,160
dropout_schedule='0,0@0.20,0.3@0.50,0'
common_egs_dir=
xent_regularize=0.1

# training options
srand=0
remove_egs=true
reporting_email=

#decode options
test_online_decoding=true  # if true, it will run the last decoding stage.


# 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

# The iVector-extraction and feature-dumping parts are the same as the standard
# nnet3 setup, and you can skip them by setting "--stage 11" if you have already
# run those things.
local/nnet3/run_ivector_common.sh --stage $stage \
                                  --train-set $train_set \
                                  --gmm $gmm \
                                  --nnet3-affix "$nnet3_affix" || exit 1;

# Problem: We have removed the "train_" prefix of our training set in
# the alignment directory names! Bad!
gmm_dir=exp/$gmm
ali_dir=exp/${gmm}_ali_${train_set}_sp
tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
lang=data/lang_chain
lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
train_data_dir=data/${train_set}_sp_hires
lores_train_data_dir=data/${train_set}_sp
train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires

for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
    $lores_train_data_dir/feats.scp $ali_dir/ali.1.gz; do
  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
done

if [ $stage -le 10 ]; then
  echo "$0: creating lang directory $lang with chain-type topology"
  # 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.]
  if [ -d $lang ]; then
    if [ $lang/L.fst -nt data/lang/L.fst ]; then
      echo "$0: $lang already exists, not overwriting it; continuing"
    else
      echo "$0: $lang already exists and seems to be older than data/lang..."
      echo " ... not sure what to do.  Exiting."
      exit 1;
    fi
  else
    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
fi

if [ $stage -le 11 ]; then
  # Get the alignments as lattices (gives the chain training more freedom).
  # use the same num-jobs as the alignments
  steps/align_fmllr_lats.sh --nj 75 --cmd "$train_cmd" ${lores_train_data_dir} \
    data/lang $gmm_dir $lat_dir
  rm $lat_dir/fsts.*.gz # save space
fi

if [ $stage -le 12 ]; then
  # Build a tree using our new topology.  We know we have alignments for the
  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
  # those.  The num-leaves is always somewhat less than the num-leaves from
  # the GMM baseline.
   if [ -f $tree_dir/final.mdl ]; then
     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
     exit 1;
  fi
  steps/nnet3/chain/build_tree.sh \
    --frame-subsampling-factor 3 \
    --context-opts "--context-width=2 --central-position=1" \
    --cmd "$train_cmd" 3500 ${lores_train_data_dir} \
    $lang $ali_dir $tree_dir
fi


if [ $stage -le 13 ]; then
  mkdir -p $dir
  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)

  tdnn_opts="l2-regularize=0.03 dropout-proportion=0.0 dropout-per-dim-continuous=true"
  tdnnf_opts="l2-regularize=0.03 dropout-proportion=0.0 bypass-scale=0.66"
  linear_opts="l2-regularize=0.03 orthonormal-constraint=-1.0"
  prefinal_opts="l2-regularize=0.03"
  output_opts="l2-regularize=0.015"

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

  # please note that it is important to have input layer with the name=input
  # as the layer immediately preceding the fixed-affine-layer to enable
  # the use of short notation for the descriptor
  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat

  # the first splicing is moved before the lda layer, so no splicing here
  relu-batchnorm-dropout-layer name=tdnn1 $tdnn_opts dim=768
  tdnnf-layer name=tdnnf2 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=1
  tdnnf-layer name=tdnnf3 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=1
  tdnnf-layer name=tdnnf4 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=1
  tdnnf-layer name=tdnnf5 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=0
  tdnnf-layer name=tdnnf6 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf7 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf8 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf9 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf10 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf11 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf12 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf13 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  linear-component name=prefinal-l dim=192 $linear_opts

  ## adding the layers for chain branch
  prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts small-dim=192 big-dim=768
  output-layer name=output include-log-softmax=false dim=$num_targets $output_opts

  # adding the layers for xent branch
  prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts small-dim=192 big-dim=768
  output-layer name=output-xent dim=$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 14 ]; then
  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
    utils/create_split_dir.pl \
     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/mini_librispeech-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
  fi

  steps/nnet3/chain/train.py --stage=$train_stage \
    --cmd="$decode_cmd" \
    --feat.online-ivector-dir=$train_ivector_dir \
    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
    --chain.xent-regularize $xent_regularize \
    --chain.leaky-hmm-coefficient=0.1 \
    --chain.l2-regularize=0.0 \
    --chain.apply-deriv-weights=false \
    --chain.lm-opts="--num-extra-lm-states=2000" \
    --trainer.dropout-schedule $dropout_schedule \
    --trainer.add-option="--optimization.memory-compression-level=2" \
    --trainer.srand=$srand \
    --trainer.max-param-change=2.0 \
    --trainer.num-epochs=20 \
    --trainer.frames-per-iter=3000000 \
    --trainer.optimization.num-jobs-initial=1 \
    --trainer.optimization.num-jobs-final=1 \
    --trainer.optimization.initial-effective-lrate=0.002 \
    --trainer.optimization.final-effective-lrate=0.0002 \
    --trainer.num-chunk-per-minibatch=128,64 \
    --egs.chunk-width=$chunk_width \
    --egs.dir="$common_egs_dir" \
    --egs.opts="--frames-overlap-per-eg 0" \
    --cleanup.remove-egs=$remove_egs \
    --use-gpu=wait \
    --reporting.email="$reporting_email" \
    --feat-dir=$train_data_dir \
    --tree-dir=$tree_dir \
    --lat-dir=$lat_dir \
    --dir=$dir  || exit 1;
fi

if [ $stage -le 15 ]; then
  # Note: it's not important to give mkgraph.sh the lang directory with the
  # matched topology (since it gets the topology file from the model).
  utils/mkgraph.sh \
    --self-loop-scale 1.0 data/lang \
    $tree_dir $tree_dir/graph || exit 1;
fi

if [ $stage -le 16 ]; then
  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
  rm $dir/.error 2>/dev/null || true

  for data in $test_sets; do
    (
      nspk=$(wc -l <data/${data}_hires/spk2utt)
      steps/nnet3/decode.sh \
          --acwt 1.0 --post-decode-acwt 10.0 \
          --frames-per-chunk $frames_per_chunk \
          --nj $nspk --cmd "$decode_cmd"  --num-threads 4 \
          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
          $tree_dir/graph data/${data}_hires ${dir}/decode_${data} || exit 1
      #right now we don't have mutliple LMs to run rescore scripts. ALso we can use consta arpa lm rescoring once we build it. Experiment with it later. 
      steps/lmrescore.sh --cmd "$decode_cmd" \
        data/lang data/lang \
       data/${data}_hires ${dir}/decode_${data} ${dir}/decode_${data}_new || exit 1
    ) || touch $dir/.error &
  done
  wait
  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
fi

# Not testing the 'looped' decoding separately, because for
# TDNN systems it would give exactly the same results as the
# normal decoding.

if $test_online_decoding && [ $stage -le 17 ]; then
  # note: if the features change (e.g. you add pitch features), you will have to
  # change the options of the following command line.
  steps/online/nnet3/prepare_online_decoding.sh \
    --mfcc-config conf/mfcc_hires.conf \
    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online

  rm $dir/.error 2>/dev/null || true

  for data in $test_sets; do
    (
      nspk=$(wc -l <data/${data}_hires/spk2utt)
      # note: we just give it "data/${data}" as it only uses the wav.scp, the
      # feature type does not matter.
      steps/online/nnet3/decode.sh \
        --acwt 1.0 --post-decode-acwt 10.0 \
        --nj $nspk --cmd "$decode_cmd" \
        $tree_dir/graph data/${data} ${dir}_online/decode_${data} || exit 1
      #Experiment with const arpa lm rescoring. Currently, I have not built it. 
      steps/lmrescore.sh --cmd "$decode_cmd" \
        data/lang data/lang \
       data/${data}_hires ${dir}_online/decode_${data} ${dir}_online/decode_${data}_new || exit 1
    ) || touch $dir/.error &
  done
  wait
  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
fi


exit 0;