forked from kaldi-asr/kaldi
-
Notifications
You must be signed in to change notification settings - Fork 2
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
modifications cnn-tdnn architecture for improving wer
- Loading branch information
Showing
3 changed files
with
271 additions
and
19 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,244 @@ | ||
| #!/bin/bash | ||
|
|
||
| # chainali_1b is as chainali_1a except it has 3 more cnn layers and 1 less tdnn layer. | ||
| # ./local/chain/compare_wer.sh exp/chain/cnn_chainali_1a/ exp/chain/cnn_chainali_1b/ | ||
| # System cnn_chainali_1a cnn_chainali_1b | ||
| # WER 15.85 14.51 | ||
| # Final train prob -0.0128 -0.0112 | ||
| # Final valid prob -0.0447 -0.0375 | ||
| # Final train prob (xent) -0.6448 -0.6230 | ||
| # Final valid prob (xent) -0.9924 -0.9399 | ||
|
|
||
| # 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->-0.005 xent:train/valid[13,20,final]=(-1.47,-0.728,-0.623/-1.69,-1.02,-0.940) logprob:train/valid[13,20,final]=(-0.068,-0.030,-0.011/-0.086,-0.056,-0.038) | ||
|
|
||
| # %WER 14.51 [ 2556 / 17616, 210 ins, 573 del, 1773 sub ] exp/chain/cnn_chainali_1b/decode_test/wer_10_0.0 | ||
| # %WER 7.02 [ 4629 / 65921, 742 ins, 1282 del, 2605 sub ] exp/chain/cnn_chainali_1b/decode_test/cer_9_0.0 | ||
|
|
||
| set -e -o pipefail | ||
|
|
||
| stage=0 | ||
|
|
||
| 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= | ||
|
|
||
| # 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 $@" # Print the command line for logging | ||
|
|
||
|
|
||
| . ./cmd.sh | ||
| . ./path.sh | ||
| . ./utils/parse_options.sh | ||
|
|
||
|
|
||
| if ! cuda-compiled; then | ||
| cat <<EOF && exit 1 | ||
| This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA | ||
| If you want to use GPUs (and have them), go to src/, and configure and make on a machine | ||
| where "nvcc" is installed. | ||
| EOF | ||
| fi | ||
|
|
||
| gmm_dir=exp/${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 | ||
|
|
||
| # 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 | ||
|
|
||
|
|
||
| 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 | ||
|
|
||
| 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 | ||
|
|
||
| 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 | ||
|
|
||
|
|
||
| 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) | ||
| 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 | ||
|
|
||
|
|
||
| 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="$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 | ||
|
|
||
| if [ $stage -le 6 ]; then | ||
| # The reason we are using data/lang here, instead of $lang, is just to | ||
| # emphasize that it's not actually important to give mkgraph.sh the | ||
| # lang directory with the matched topology (since it gets the | ||
| # topology file from the model). So you could give it a different | ||
| # lang directory, one that contained a wordlist and LM of your choice, | ||
| # as long as phones.txt was compatible. | ||
|
|
||
| utils/mkgraph.sh \ | ||
| --self-loop-scale 1.0 data/$lang_test \ | ||
| $dir $dir/graph || exit 1; | ||
| fi | ||
|
|
||
| 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 |