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egs/bentham/v1/local/chain/tuning/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 |