run_e2e_cnn_1a.sh
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#!/bin/bash
# Copyright 2017 Hossein Hadian
# This script does end2end chain training (i.e. from scratch)
# ./local/chain/compare_wer.sh exp/chain/e2e_cnn_1a/
# System e2e_cnn_1b
# WER 13.72
# WER (rescored) 13.40
# CER 6.56
# CER (rescored) 6.33
# WER val 13.51
# WER (rescored) val 13.38
# CER val 6.40
# CER (rescored) val 6.29
# Final train prob 0.1037
# Final valid prob 0.0720
# Final train prob (xent)
# Final valid prob (xent)
# Parameters 11.54M
# steps/info/chain_dir_info.pl exp/chain/e2e_cnn_1a
# exp/chain/e2e_cnn_1a: num-iters=26 nj=2..4 num-params=11.5M dim=40->17112 combine=0.054->0.054 (over 1) logprob:train/valid[16,25,final]=(0.078,0.102,0.104/0.051,0.069,0.072)
set -e
# configs for 'chain'
stage=0
train_stage=-10
get_egs_stage=-10
affix=1a
nj=30
# training options
tdnn_dim=450
minibatch_size=150=100,64/300=50,32/600=25,16/1200=16,8
common_egs_dir=
train_set=train
decode_val=true
lang_decode=data/lang
lang_rescore=data/lang_rescore_6g
if $decode_val; then maybe_val=val; else maybe_val= ; fi
# End configuration section.
echo "$0 $@" # Print the command line for logging
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
if ! cuda-compiled; then
cat <<EOF && exit 1
This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
If you want to use GPUs (and have them), go to src/, and configure and make on a machine
where "nvcc" is installed.
EOF
fi
lang=data/lang_e2e
treedir=exp/chain/e2e_bitree # it's actually just a trivial tree (no tree building)
dir=exp/chain/e2e_cnn_${affix}
if [ $stage -le 0 ]; 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 [ $stage -le 1 ]; then
steps/nnet3/chain/e2e/prepare_e2e.sh --nj 30 --cmd "$cmd" \
--shared-phones true \
--type biphone \
data/$train_set $lang $treedir
$cmd $treedir/log/make_phone_lm.log \
cat data/$train_set/text \| \
steps/nnet3/chain/e2e/text_to_phones.py data/lang \| \
utils/sym2int.pl -f 2- data/lang/phones.txt \| \
chain-est-phone-lm --num-extra-lm-states=500 \
ark:- $treedir/phone_lm.fst
fi
if [ $stage -le 2 ]; then
echo "$0: creating neural net configs using the xconfig parser";
num_targets=$(tree-info $treedir/tree | grep num-pdfs | awk '{print $2}')
common1="height-offsets=-2,-1,0,1,2 num-filters-out=36"
common2="height-offsets=-2,-1,0,1,2 num-filters-out=70"
mkdir -p $dir/configs
cat <<EOF > $dir/configs/network.xconfig
input dim=40 name=input
conv-relu-batchnorm-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
conv-relu-batchnorm-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
conv-relu-batchnorm-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim
relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim
relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim
relu-batchnorm-layer name=tdnn4 input=Append(-4,0,4) dim=$tdnn_dim
## adding the layers for chain branch
relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $output_opts
output-layer name=output include-log-softmax=false dim=$num_targets 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 3 ]; then
# no need to store the egs in a shared storage because we always
# remove them. Anyway, it takes only 5 minutes to generate them.
steps/nnet3/chain/e2e/train_e2e.py --stage $train_stage \
--cmd "$cmd" \
--feat.cmvn-opts="--norm-means=false --norm-vars=false" \
--chain.leaky-hmm-coefficient 0.1 \
--chain.l2-regularize 0.00005 \
--chain.apply-deriv-weights false \
--egs.dir "$common_egs_dir" \
--egs.stage $get_egs_stage \
--egs.opts "--num_egs_diagnostic 100 --num_utts_subset 400" \
--chain.frame-subsampling-factor 4 \
--chain.alignment-subsampling-factor 4 \
--trainer.num-chunk-per-minibatch $minibatch_size \
--trainer.frames-per-iter 1000000 \
--trainer.num-epochs 4 \
--trainer.optimization.momentum 0 \
--trainer.optimization.num-jobs-initial 2 \
--trainer.optimization.num-jobs-final 4 \
--trainer.optimization.initial-effective-lrate 0.001 \
--trainer.optimization.final-effective-lrate 0.0001 \
--trainer.optimization.shrink-value 1.0 \
--trainer.max-param-change 2.0 \
--cleanup.remove-egs true \
--feat-dir data/${train_set} \
--tree-dir $treedir \
--dir $dir || exit 1;
fi
if [ $stage -le 4 ]; 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 5 ]; then
for decode_set in test $maybe_val; do
steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
--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