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modifications for improving WER/CER while reducing number of parameters
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| #!/bin/bash | ||
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| # chainali_1b uses chain model for lattice instead of gmm-hmm model. It has more cnn layers as compared to 1a | ||
| # (15.17% -> 14.54%) | ||
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| # steps/info/chain_dir_info.pl exp/chain/chainali_cnn_1b/ | ||
| # exp/chain/chainali_cnn_1b/: num-iters=21 nj=2..4 num-params=4.0M dim=40->364 combine=-0.009->-0.006 xent:train/valid[13,20,final]=(-0.870,-0.593,-0.568/-1.08,-0.889,-0.874) logprob:train/valid[13,20,final]=(-0.035,-0.003,-0.001/-0.077,-0.055,-0.054) | ||
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| set -e -o pipefail | ||
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| stage=0 | ||
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| nj=30 | ||
| train_set=train | ||
| gmm=tri3 # this is the source gmm-dir that we'll use for alignments; it | ||
| # should have alignments for the specified training data. | ||
| nnet3_affix= # affix for exp dirs, e.g. it was _cleaned in tedlium. | ||
| affix=_1b #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration. | ||
| ali=tri3_ali | ||
| chain_model_dir=exp/chain${nnet3_affix}/cnn${affix} | ||
| common_egs_dir= | ||
| reporting_email= | ||
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| # chain options | ||
| train_stage=-10 | ||
| xent_regularize=0.1 | ||
| frame_subsampling_factor=4 | ||
| alignment_subsampling_factor=1 | ||
| # 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=450 | ||
| # training options | ||
| srand=0 | ||
| remove_egs=false | ||
| lang_test=lang_test | ||
| # End configuration section. | ||
| echo "$0 $@" # Print the command line for logging | ||
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| . ./cmd.sh | ||
| . ./path.sh | ||
| . ./utils/parse_options.sh | ||
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| if ! cuda-compiled; then | ||
| cat <<EOF && exit 1 | ||
| This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA | ||
| If you want to use GPUs (and have them), go to src/, and configure and make on a machine | ||
| where "nvcc" is installed. | ||
| EOF | ||
| fi | ||
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| gmm_dir=exp/${gmm} | ||
| ali_dir=exp/${ali} | ||
| lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_lats_chain | ||
| gmm_lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_lats | ||
| dir=exp/chain${nnet3_affix}/cnn_chainali${affix} | ||
| train_data_dir=data/${train_set} | ||
| lores_train_data_dir=$train_data_dir # for the start, use the same data for gmm and chain | ||
| tree_dir=exp/chain${nnet3_affix}/tree_chain | ||
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| # 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 \ | ||
| $lores_train_data_dir/feats.scp $gmm_dir/final.mdl \ | ||
| $ali_dir/ali.1.gz $gmm_dir/final.mdl; do | ||
| [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1 | ||
| done | ||
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| 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_test/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_test $lang | ||
| silphonelist=$(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 | ||
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| if [ $stage -le 2 ]; then | ||
| # Get the alignments as lattices (gives the chain training more freedom). | ||
| # use the same num-jobs as the alignments | ||
| local/chain/align_nnet3_lats.sh --nj $nj --cmd "$train_cmd" ${lores_train_data_dir} \ | ||
| data/$lang_test $chain_model_dir $lat_dir | ||
| cp $gmm_lat_dir/splice_opts $lat_dir/splice_opts | ||
| fi | ||
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| 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 $frame_subsampling_factor \ | ||
| --context-opts "--context-width=2 --central-position=1" \ | ||
| --cmd "$train_cmd" $num_leaves ${lores_train_data_dir} \ | ||
| $lang $ali_dir $tree_dir | ||
| fi | ||
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| if [ $stage -le 4 ]; then | ||
| mkdir -p $dir | ||
| echo "$0: creating neural net configs using the xconfig parser"; | ||
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| num_targets=$(tree-info $tree_dir/tree | grep num-pdfs | awk '{print $2}') | ||
| learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python) | ||
| common1="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36" | ||
| common2="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70" | ||
| common3="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-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=20 time-offsets=-4,-2,0,2,4 $common2 | ||
| conv-relu-batchnorm-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2 | ||
| conv-relu-batchnorm-layer name=cnn6 height-in=10 height-out=10 time-offsets=-1,0,1 $common3 | ||
| conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-1,0,1 $common3 | ||
| relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$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 | ||
| ## adding the layers for chain branch | ||
| relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 | ||
| output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 | ||
| # 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=$tdnn_dim target-rms=0.5 | ||
| output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5 | ||
| EOF | ||
| steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/ | ||
| fi | ||
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| 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 $dir/egs/storage | ||
| fi | ||
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| steps/nnet3/chain/train.py --stage=$train_stage \ | ||
| --cmd="$decode_cmd" \ | ||
| --feat.cmvn-opts="--norm-means=false --norm-vars=false" \ | ||
| --chain.xent-regularize $xent_regularize \ | ||
| --chain.leaky-hmm-coefficient=0.1 \ | ||
| --chain.l2-regularize=0.00005 \ | ||
| --chain.apply-deriv-weights=false \ | ||
| --chain.lm-opts="--num-extra-lm-states=500" \ | ||
| --chain.frame-subsampling-factor=$frame_subsampling_factor \ | ||
| --chain.alignment-subsampling-factor=$alignment_subsampling_factor \ | ||
| --trainer.srand=$srand \ | ||
| --trainer.max-param-change=2.0 \ | ||
| --trainer.num-epochs=4 \ | ||
| --trainer.frames-per-iter=1000000 \ | ||
| --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.num-chunk-per-minibatch=64,32 \ | ||
| --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" \ | ||
| --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 | ||
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| if [ $stage -le 6 ]; then | ||
| # The reason we are using data/lang here, instead of $lang, is just to | ||
| # emphasize that it's not actually important to give mkgraph.sh the | ||
| # lang directory with the matched topology (since it gets the | ||
| # topology file from the model). So you could give it a different | ||
| # lang directory, one that contained a wordlist and LM of your choice, | ||
| # as long as phones.txt was compatible. | ||
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| utils/mkgraph.sh \ | ||
| --self-loop-scale 1.0 data/$lang_test \ | ||
| $dir $dir/graph || exit 1; | ||
| fi | ||
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| if [ $stage -le 7 ]; then | ||
| frames_per_chunk=$(echo $chunk_width | cut -d, -f1) | ||
| 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 $nj --cmd "$decode_cmd" \ | ||
| $dir/graph data/test $dir/decode_test || exit 1; | ||
| fi |
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