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[scripts] nnet3 : fix issue where LDA estimation failed for LSTMs wit…
…h label delay (#1540) xconfig : Added delay option for FixedAffineLayer. This will be used for ensuring the model specified in ref.config has at least the context required by the model specified in init.config
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| #!/bin/bash | ||
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| # Copyright 2015 Johns Hopkins University (Author: Daniel Povey). | ||
| # 2015 Vijayaditya Peddinti | ||
| # 2015 Xingyu Na | ||
| # 2015 Pegah Ghahrmani | ||
| # 2017 Google Inc. (vpeddinti@google.com) | ||
| # Apache 2.0. | ||
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| # run_lstm_6k.sh is like run_lstm_6j.sh but making | ||
| # various kaldi-5.1-related upgrades to the script. | ||
| # For the list of changes compare tuning/run_tdnn_lstm_1{c,d}.sh | ||
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| set -e | ||
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| # configs for 'chain' | ||
| stage=12 | ||
| train_stage=-10 | ||
| get_egs_stage=-10 | ||
| speed_perturb=true | ||
| dir=exp/chain/lstm_6k # Note: _sp will get added to this if $speed_perturb == true. | ||
| decode_iter= | ||
| decode_nj=50 | ||
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| # training options | ||
| xent_regularize=0.01 | ||
| self_repair_scale=0.00001 | ||
| label_delay=5 | ||
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| chunk_left_context=40 | ||
| chunk_right_context=0 | ||
| # we'll put chunk-left-context-initial=0 and chunk-right-context-final=0 | ||
| # directly without variables. | ||
| frames_per_chunk=140,100,160 | ||
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| # (non-looped) decoding options | ||
| frames_per_chunk_primary=$(echo $frames_per_chunk | cut -d, -f1) | ||
| extra_left_context=50 | ||
| extra_right_context=0 | ||
| # we'll put extra-left-context-initial=0 and extra-right-context-final=0 | ||
| # directly without variables. | ||
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| remove_egs=false | ||
| common_egs_dir= | ||
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| test_online_decoding=false # if true, it will run the last decoding stage. | ||
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| # End configuration section. | ||
| echo "$0 $@" # Print the command line for logging | ||
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| . ./cmd.sh | ||
| . ./path.sh | ||
| . ./utils/parse_options.sh | ||
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| if ! cuda-compiled; then | ||
| cat <<EOF && exit 1 | ||
| This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA | ||
| If you want to use GPUs (and have them), go to src/, and configure and make on a machine | ||
| where "nvcc" is installed. | ||
| EOF | ||
| fi | ||
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| # The iVector-extraction and feature-dumping parts are the same as the standard | ||
| # nnet3 setup, and you can skip them by setting "--stage 8" if you have already | ||
| # run those things. | ||
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| suffix= | ||
| if [ "$speed_perturb" == "true" ]; then | ||
| suffix=_sp | ||
| fi | ||
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| dir=${dir}$suffix | ||
| train_set=train_nodup$suffix | ||
| ali_dir=exp/tri4_ali_nodup$suffix | ||
| treedir=exp/chain/tri5_7d_tree$suffix | ||
| lang=data/lang_chain_2y | ||
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| # if we are using the speed-perturbed data we need to generate | ||
| # alignments for it. | ||
| local/nnet3/run_ivector_common.sh --stage $stage \ | ||
| --speed-perturb $speed_perturb \ | ||
| --generate-alignments $speed_perturb || exit 1; | ||
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| if [ $stage -le 9 ]; then | ||
| # Get the alignments as lattices (gives the CTC training more freedom). | ||
| # use the same num-jobs as the alignments | ||
| nj=$(cat exp/tri4_ali_nodup$suffix/num_jobs) || exit 1; | ||
| steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/$train_set \ | ||
| data/lang exp/tri4 exp/tri4_lats_nodup$suffix | ||
| rm exp/tri4_lats_nodup$suffix/fsts.*.gz # save space | ||
| fi | ||
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| if [ $stage -le 10 ]; 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 | ||
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| if [ $stage -le 11 ]; then | ||
| # Build a tree using our new topology. | ||
| steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \ | ||
| --context-opts "--context-width=2 --central-position=1" \ | ||
| --cmd "$train_cmd" 7000 data/$train_set $lang $ali_dir $treedir | ||
| fi | ||
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| if [ $stage -le 12 ]; then | ||
| echo "$0: creating neural net configs using the xconfig parser"; | ||
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| num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}') | ||
| [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; } | ||
| learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python) | ||
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| lstm_opts="decay-time=20" | ||
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| mkdir -p $dir/configs | ||
| cat <<EOF > $dir/configs/network.xconfig | ||
| input dim=100 name=ivector | ||
| input dim=40 name=input | ||
| # please note that it is important to have input layer with the name=input | ||
| # as the layer immediately preceding the fixed-affine-layer to enable | ||
| # the use of short notation for the descriptor | ||
| # Note : The delay variable will be used just in the init.config. | ||
| fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat delay=$label_delay | ||
| # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults | ||
| fast-lstmp-layer name=fastlstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts | ||
| fast-lstmp-layer name=fastlstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts | ||
| fast-lstmp-layer name=fastlstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts | ||
| ## adding the layers for chain branch | ||
| output-layer name=output input=fastlstm3 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5 | ||
| # adding the layers for xent branch | ||
| # This block prints the configs for a separate output that will be | ||
| # trained with a cross-entropy objective in the 'chain' models... this | ||
| # has the effect of regularizing the hidden parts of the model. we use | ||
| # 0.5 / args.xent_regularize as the learning rate factor- the factor of | ||
| # 0.5 / args.xent_regularize is suitable as it means the xent | ||
| # final-layer learns at a rate independent of the regularization | ||
| # constant; and the 0.5 was tuned so as to make the relative progress | ||
| # similar in the xent and regular final layers. | ||
| output-layer name=output-xent input=fastlstm3 output-delay=$label_delay 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 13 ]; then | ||
| if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then | ||
| utils/create_split_dir.pl \ | ||
| /export/b0{5,6,7,8}/$USER/kaldi-data/egs/swbd-$(date +'%m_%d_%H_%M')/s5c/$dir/egs/storage $dir/egs/storage | ||
| fi | ||
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| steps/nnet3/chain/train.py --stage $train_stage \ | ||
| --cmd "$decode_cmd" \ | ||
| --feat.online-ivector-dir exp/nnet3/ivectors_${train_set} \ | ||
| --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=2000" \ | ||
| --trainer.num-chunk-per-minibatch 64,32 \ | ||
| --trainer.frames-per-iter 1500000 \ | ||
| --trainer.max-param-change 2.0 \ | ||
| --trainer.num-epochs 4 \ | ||
| --trainer.optimization.shrink-value 0.99 \ | ||
| --trainer.optimization.num-jobs-initial 3 \ | ||
| --trainer.optimization.num-jobs-final 16 \ | ||
| --trainer.optimization.initial-effective-lrate 0.001 \ | ||
| --trainer.optimization.final-effective-lrate 0.0001 \ | ||
| --trainer.optimization.momentum 0.0 \ | ||
| --trainer.deriv-truncate-margin 8 \ | ||
| --egs.stage $get_egs_stage \ | ||
| --egs.opts "--frames-overlap-per-eg 0" \ | ||
| --egs.chunk-width $frames_per_chunk \ | ||
| --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" \ | ||
| --cleanup.remove-egs $remove_egs \ | ||
| --feat-dir data/${train_set}_hires \ | ||
| --tree-dir $treedir \ | ||
| --lat-dir exp/tri4_lats_nodup$suffix \ | ||
| --dir $dir || exit 1; | ||
| fi | ||
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| if [ $stage -le 14 ]; then | ||
| # Note: it might appear that this $lang directory is mismatched, and it is as | ||
| # far as the 'topo' is concerned, but this script doesn't read the 'topo' from | ||
| # the lang directory. | ||
| utils/mkgraph.sh --self-loop-scale 1.0 data/lang_sw1_tg $dir $dir/graph_sw1_tg | ||
| fi | ||
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| graph_dir=$dir/graph_sw1_tg | ||
| iter_opts= | ||
| if [ ! -z $decode_iter ]; then | ||
| iter_opts=" --iter $decode_iter " | ||
| fi | ||
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| if [ $stage -le 15 ]; then | ||
| rm $dir/.error 2>/dev/null || true | ||
| for decode_set in train_dev eval2000; do | ||
| ( | ||
| steps/nnet3/decode.sh --num-threads 4 \ | ||
| --acwt 1.0 --post-decode-acwt 10.0 \ | ||
| --nj 25 --cmd "$decode_cmd" $iter_opts \ | ||
| --extra-left-context $extra_left_context \ | ||
| --extra-right-context $extra_right_context \ | ||
| --extra-left-context-initial 0 \ | ||
| --extra-right-context-final 0 \ | ||
| --frames-per-chunk "$frames_per_chunk_primary" \ | ||
| --online-ivector-dir exp/nnet3/ivectors_${decode_set} \ | ||
| $graph_dir data/${decode_set}_hires \ | ||
| $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_sw1_tg || exit 1; | ||
| if $has_fisher; then | ||
| steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \ | ||
| data/lang_sw1_{tg,fsh_fg} data/${decode_set}_hires \ | ||
| $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_sw1_{tg,fsh_fg} || exit 1; | ||
| fi | ||
| ) & | ||
| done | ||
| wait | ||
| if [ -f $dir/.error ]; then | ||
| echo "$0: something went wrong in decoding" | ||
| exit 1 | ||
| fi | ||
| fi | ||
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| if [ $stage -le 16 ]; then | ||
| # looped decoding. Note: this does not make sense for BLSTMs or other | ||
| # backward-recurrent setups, and for TDNNs and other non-recurrent there is no | ||
| # point doing it because it would give identical results to regular decoding. | ||
| rm $dir/.error 2>/dev/null || true | ||
| for decode_set in train_dev eval2000; do | ||
| ( | ||
| steps/nnet3/decode_looped.sh \ | ||
| --acwt 1.0 --post-decode-acwt 10.0 \ | ||
| --nj $decode_nj --cmd "$decode_cmd" $iter_opts \ | ||
| --online-ivector-dir exp/nnet3/ivectors_${decode_set} \ | ||
| $graph_dir data/${decode_set}_hires \ | ||
| $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_sw1_tg_looped || exit 1; | ||
| if $has_fisher; then | ||
| steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \ | ||
| data/lang_sw1_{tg,fsh_fg} data/${decode_set}_hires \ | ||
| $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_sw1_{tg,fsh_fg}_looped || exit 1; | ||
| fi | ||
| ) & | ||
| done | ||
| wait | ||
| if [ -f $dir/.error ]; then | ||
| echo "$0: something went wrong in looped decoding" | ||
| exit 1 | ||
| fi | ||
| fi | ||
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| if $test_online_decoding && [ $stage -le 17 ]; then | ||
| # note: if the features change (e.g. you add pitch features), you will have to | ||
| # change the options of the following command line. | ||
| steps/online/nnet3/prepare_online_decoding.sh \ | ||
| --mfcc-config conf/mfcc_hires.conf \ | ||
| $lang exp/nnet3/extractor $dir ${dir}_online | ||
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| rm $dir/.error 2>/dev/null || true | ||
| for decode_set in train_dev eval2000; do | ||
| ( | ||
| # note: we just give it "$decode_set" as it only uses the wav.scp, the | ||
| # feature type does not matter. | ||
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| steps/online/nnet3/decode.sh --nj $decode_nj --cmd "$decode_cmd" $iter_opts \ | ||
| --acwt 1.0 --post-decode-acwt 10.0 \ | ||
| $graph_dir data/${decode_set}_hires \ | ||
| ${dir}_online/decode_${decode_set}${decode_iter:+_$decode_iter}_sw1_tg || exit 1; | ||
| if $has_fisher; then | ||
| steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \ | ||
| data/lang_sw1_{tg,fsh_fg} data/${decode_set}_hires \ | ||
| ${dir}_online/decode_${decode_set}${decode_iter:+_$decode_iter}_sw1_{tg,fsh_fg} || exit 1; | ||
| fi | ||
| ) || touch $dir/.error & | ||
| done | ||
| wait | ||
| if [ -f $dir/.error ]; then | ||
| echo "$0: something went wrong in online decoding" | ||
| exit 1 | ||
| fi | ||
| fi | ||
|
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| exit 0; |
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