run_cnn_e2eali_1a.sh
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#!/bin/bash
# local/chain/compare_wer.sh exp/chain/e2e_cnn_1a/ exp/chain/cnn_e2eali_1a
# System e2e_cnn_1a cnn_e2eali_1a
# WER 13.72 8.14
# WER (rescored) 13.40 8.00
# CER 6.56 2.82
# CER (rescored) 6.33 2.73
# WER val 13.51 8.19
# WER (rescored) val 13.38 7.97
# CER val 6.40 2.93
# CER (rescored) val 6.29 2.90
# Final train prob 0.1037 -0.0613
# Final valid prob 0.0720 -0.0988
# Final train prob (xent) -0.3706
# Final valid prob (xent) -0.4669
# Parameters 11.54M 4.29M
# steps/info/chain_dir_info.pl exp/chain/cnn_e2eali_1a
# exp/chain/cnn_e2eali_1a: num-iters=20 nj=3..5 num-params=4.3M dim=40->336 combine=-0.066->-0.066 (over 1) xent:train/valid[12,19,final]=(-0.822,-0.437,-0.371/-0.859,-0.514,-0.467) logprob:train/valid[12,19,final]=(-0.188,-0.078,-0.061/-0.204,-0.114,-0.099)
set -e -o pipefail
stage=0
nj=30
train_set=train
decode_val=true
nnet3_affix= # affix for exp dirs, e.g. it was _cleaned in tedlium.
affix=_1a #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
e2echain_model_dir=exp/chain/e2e_cnn_1a
common_egs_dir=
reporting_email=
# chain options
train_stage=-10
xent_regularize=0.1
frame_subsampling_factor=4
# training chunk-options
chunk_width=340,300,200,100
num_leaves=500
# we don't need extra left/right context for TDNN systems.
chunk_left_context=0
chunk_right_context=0
tdnn_dim=550
# training options
srand=0
remove_egs=true
lang_decode=data/lang
lang_rescore=data/lang_rescore_6g
if $decode_val; then maybe_val=val; else maybe_val= ; fi
dropout_schedule='0,0@0.20,0.2@0.50,0'
# End configuration section.
echo "$0 $@" # Print the command line for logging
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
if ! cuda-compiled; then
cat <<EOF && exit 1
This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
If you want to use GPUs (and have them), go to src/, and configure and make on a machine
where "nvcc" is installed.
EOF
fi
ali_dir=exp/chain/e2e_ali_train
lat_dir=exp/chain${nnet3_affix}/e2e_${train_set}_lats
dir=exp/chain${nnet3_affix}/cnn_e2eali${affix}
train_data_dir=data/${train_set}
tree_dir=exp/chain${nnet3_affix}/tree_e2e
# the 'lang' directory is created by this script.
# If you create such a directory with a non-standard topology
# you should probably name it differently.
lang=data/lang_chain
for f in $train_data_dir/feats.scp $ali_dir/ali.1.gz $ali_dir/final.mdl; do
[ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
done
if [ $stage -le 1 ]; 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 2 ]; then
# Get the alignments as lattices (gives the chain training more freedom).
# use the same num-jobs as the alignments
steps/nnet3/align_lats.sh --nj $nj --cmd "$cmd" \
--acoustic-scale 1.0 \
--scale-opts '--transition-scale=1.0 --self-loop-scale=1.0' \
${train_data_dir} data/lang $e2echain_model_dir $lat_dir
echo "" >$lat_dir/splice_opts
fi
if [ $stage -le 3 ]; 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 $frame_subsampling_factor \
--alignment-subsampling-factor 1 \
--context-opts "--context-width=2 --central-position=1" \
--cmd "$cmd" $num_leaves ${train_data_dir} \
$lang $ali_dir $tree_dir
fi
if [ $stage -le 4 ]; 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/$xent_regularize)" | python)
cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
tdnn_opts="l2-regularize=0.03"
output_opts="l2-regularize=0.04"
common1="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
common2="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
common3="$cnn_opts required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
mkdir -p $dir/configs
cat <<EOF > $dir/configs/network.xconfig
input dim=40 name=input
conv-relu-batchnorm-dropout-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
conv-relu-batchnorm-dropout-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
conv-relu-batchnorm-dropout-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
conv-relu-batchnorm-dropout-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
conv-relu-batchnorm-dropout-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common3 height-subsample-out=2
conv-relu-batchnorm-dropout-layer name=cnn6 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
relu-batchnorm-dropout-layer name=tdnn1 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts dropout-proportion=0.0
relu-batchnorm-dropout-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts dropout-proportion=0.0
relu-batchnorm-dropout-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts dropout-proportion=0.0
## adding the layers for chain branch
relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $tdnn_opts
output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $output_opts
# adding the layers for xent branch
# This block prints the configs for a separate output that will be
# trained with a cross-entropy objective in the 'chain' mod?els... this
# has the effect of regularizing the hidden parts of the model. we use
# 0.5 / args.xent_regularize as the learning rate factor- the factor of
# 0.5 / args.xent_regularize is suitable as it means the xent
# final-layer learns at a rate independent of the regularization
# constant; and the 0.5 was tuned so as to make the relative progress
# similar in the xent and regular final layers.
relu-batchnorm-layer name=prefinal-xent input=tdnn3 dim=$tdnn_dim target-rms=0.5 $tdnn_opts
output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5 $output_opts
EOF
steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
fi
if [ $stage -le 5 ]; 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/iam-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
fi
steps/nnet3/chain/train.py --stage=$train_stage \
--cmd="$cmd" \
--feat.cmvn-opts="--norm-means=false --norm-vars=false" \
--chain.xent-regularize $xent_regularize \
--chain.leaky-hmm-coefficient=0.1 \
--chain.l2-regularize=0.00005 \
--chain.apply-deriv-weights=true \
--chain.lm-opts="--ngram-order=2 --no-prune-ngram-order=1 --num-extra-lm-states=1000" \
--chain.frame-subsampling-factor=$frame_subsampling_factor \
--chain.alignment-subsampling-factor=1 \
--chain.left-tolerance 3 \
--chain.right-tolerance 3 \
--trainer.srand=$srand \
--trainer.max-param-change=2.0 \
--trainer.num-epochs=5 \
--trainer.frames-per-iter=1500000 \
--trainer.optimization.num-jobs-initial=3 \
--trainer.optimization.num-jobs-final=5 \
--trainer.dropout-schedule $dropout_schedule \
--trainer.optimization.initial-effective-lrate=0.001 \
--trainer.optimization.final-effective-lrate=0.0001 \
--trainer.optimization.shrink-value=1.0 \
--trainer.num-chunk-per-minibatch=32,16 \
--trainer.optimization.momentum=0.0 \
--egs.chunk-width=$chunk_width \
--egs.chunk-left-context=$chunk_left_context \
--egs.chunk-right-context=$chunk_right_context \
--egs.chunk-left-context-initial=0 \
--egs.chunk-right-context-final=0 \
--egs.dir="$common_egs_dir" \
--egs.opts="--frames-overlap-per-eg 0 --constrained false" \
--cleanup.remove-egs=$remove_egs \
--use-gpu=true \
--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 6 ]; then
# The reason we are using data/lang here, instead of $lang, is just to
# emphasize that it's not actually important to give mkgraph.sh the
# lang directory with the matched topology (since it gets the
# topology file from the model). So you could give it a different
# lang directory, one that contained a wordlist and LM of your choice,
# as long as phones.txt was compatible.
utils/mkgraph.sh \
--self-loop-scale 1.0 $lang_decode \
$dir $dir/graph || exit 1;
fi
if [ $stage -le 7 ]; then
frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
for decode_set in test $maybe_val; do
steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
--extra-left-context $chunk_left_context \
--extra-right-context $chunk_right_context \
--extra-left-context-initial 0 \
--extra-right-context-final 0 \
--frames-per-chunk $frames_per_chunk \
--nj $nj --cmd "$cmd" \
$dir/graph data/$decode_set $dir/decode_$decode_set || exit 1;
steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
data/$decode_set $dir/decode_${decode_set}{,_rescored} || exit 1
done
fi
echo "Done. Date: $(date). Results:"
local/chain/compare_wer.sh $dir