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Merge pull request #6 from GaofengCheng/nnet3-dropout
adding frame level dropout on AMI kaldi-asr#1248
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| #!/bin/bash | ||
| # Copyright 2016 University of Chinese Academy of Sciences (Author: Cheng Gaofeng) | ||
| # Apache 2.0 | ||
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| # same as 1i but with frame level dropout | ||
| # (num-params 1g:21309812 1i: 43447156) | ||
| # results on sdm1 using ihm ali | ||
| #System tdnn_lstm1i_sp_bi_ihmali_ld5 | ||
| #WER on dev 37.6 36.7 | ||
| #WER on eval 40.9 39.9 | ||
| #Final train prob -0.114135 -0.118 | ||
| #Final valid prob -0.245208 -0.246 | ||
| #Final train prob (xent) -1.47648 -1.54 | ||
| #Final valid prob (xent) -2.16365 -2.10 | ||
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| set -e -o pipefail | ||
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| # First the options that are passed through to run_ivector_common.sh | ||
| # (some of which are also used in this script directly). | ||
| stage=0 | ||
| mic=ihm | ||
| nj=30 | ||
| min_seg_len=1.55 | ||
| use_ihm_ali=false | ||
| train_set=train_cleaned | ||
| gmm=tri3_cleaned # the gmm for the target data | ||
| ihm_gmm=tri3 # the gmm for the IHM system (if --use-ihm-ali true). | ||
| num_threads_ubm=32 | ||
| nnet3_affix=_cleaned # cleanup affix for nnet3 and chain dirs, e.g. _cleaned | ||
| dropout_schedule='0,[email protected],[email protected],[email protected],0' | ||
| chunk_width=150 | ||
| chunk_left_context=40 | ||
| chunk_right_context=0 | ||
| label_delay=5 | ||
| # The rest are configs specific to this script. Most of the parameters | ||
| # are just hardcoded at this level, in the commands below. | ||
| train_stage=-10 | ||
| tree_affix= # affix for tree directory, e.g. "a" or "b", in case we change the configuration. | ||
| tlstm_affix=1i #affix for TDNN-LSTM directory, e.g. "a" or "b", in case we change the configuration. | ||
| common_egs_dir= # you can set this to use previously dumped egs. | ||
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| # decode options | ||
| extra_left_context=50 | ||
| frames_per_chunk= | ||
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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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| local/nnet3/run_ivector_common.sh --stage $stage \ | ||
| --mic $mic \ | ||
| --nj $nj \ | ||
| --min-seg-len $min_seg_len \ | ||
| --train-set $train_set \ | ||
| --gmm $gmm \ | ||
| --num-threads-ubm $num_threads_ubm \ | ||
| --nnet3-affix "$nnet3_affix" | ||
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| # Note: the first stage of the following script is stage 8. | ||
| local/nnet3/prepare_lores_feats.sh --stage $stage \ | ||
| --mic $mic \ | ||
| --nj $nj \ | ||
| --min-seg-len $min_seg_len \ | ||
| --use-ihm-ali $use_ihm_ali \ | ||
| --train-set $train_set | ||
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| if $use_ihm_ali; then | ||
| gmm_dir=exp/ihm/${ihm_gmm} | ||
| ali_dir=exp/${mic}/${ihm_gmm}_ali_${train_set}_sp_comb_ihmdata | ||
| lores_train_data_dir=data/$mic/${train_set}_ihmdata_sp_comb | ||
| tree_dir=exp/$mic/chain${nnet3_affix}/tree_bi${tree_affix}_ihmdata | ||
| lat_dir=exp/$mic/chain${nnet3_affix}/${gmm}_${train_set}_sp_comb_lats_ihmdata | ||
| dir=exp/$mic/chain${nnet3_affix}/tdnn_lstm${tlstm_affix}_sp_bi_ihmali | ||
| # note: the distinction between when we use the 'ihmdata' suffix versus | ||
| # 'ihmali' is pretty arbitrary. | ||
| else | ||
| gmm_dir=exp/${mic}/$gmm | ||
| ali_dir=exp/${mic}/${gmm}_ali_${train_set}_sp_comb | ||
| lores_train_data_dir=data/$mic/${train_set}_sp_comb | ||
| tree_dir=exp/$mic/chain${nnet3_affix}/tree_bi${tree_affix} | ||
| lat_dir=exp/$mic/chain${nnet3_affix}/${gmm}_${train_set}_sp_comb_lats | ||
| dir=exp/$mic/chain${nnet3_affix}/tdnn_lstm${tlstm_affix}_sp_bi | ||
| fi | ||
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| if [ $label_delay -gt 0 ]; then dir=${dir}_ld$label_delay; fi | ||
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| train_data_dir=data/$mic/${train_set}_sp_hires_comb | ||
| train_ivector_dir=exp/$mic/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires_comb | ||
| final_lm=`cat data/local/lm/final_lm` | ||
| LM=$final_lm.pr1-7 | ||
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| for f in $gmm_dir/final.mdl $lores_train_data_dir/feats.scp \ | ||
| $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp; do | ||
| [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1 | ||
| done | ||
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| if [ $stage -le 11 ]; then | ||
| if [ -f $ali_dir/ali.1.gz ]; then | ||
| echo "$0: alignments in $ali_dir appear to already exist. Please either remove them " | ||
| echo " ... or use a later --stage option." | ||
| exit 1 | ||
| fi | ||
| echo "$0: aligning perturbed, short-segment-combined ${maybe_ihm}data" | ||
| steps/align_fmllr.sh --nj $nj --cmd "$train_cmd" \ | ||
| ${lores_train_data_dir} data/lang $gmm_dir $ali_dir | ||
| fi | ||
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| [ ! -f $ali_dir/ali.1.gz ] && echo "$0: expected $ali_dir/ali.1.gz to exist" && exit 1 | ||
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| if [ $stage -le 12 ]; then | ||
| echo "$0: creating lang directory with one state per phone." | ||
| # 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 data/lang_chain ]; then | ||
| if [ data/lang_chain/L.fst -nt data/lang/L.fst ]; then | ||
| echo "$0: data/lang_chain already exists, not overwriting it; continuing" | ||
| else | ||
| echo "$0: data/lang_chain already exists and seems to be older than data/lang..." | ||
| echo " ... not sure what to do. Exiting." | ||
| exit 1; | ||
| fi | ||
| else | ||
| cp -r data/lang data/lang_chain | ||
| silphonelist=$(cat data/lang_chain/phones/silence.csl) || exit 1; | ||
| nonsilphonelist=$(cat data/lang_chain/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 >data/lang_chain/topo | ||
| fi | ||
| fi | ||
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| if [ $stage -le 13 ]; then | ||
| # Get the alignments as lattices (gives the chain training more freedom). | ||
| # use the same num-jobs as the alignments | ||
| steps/align_fmllr_lats.sh --nj 100 --cmd "$train_cmd" ${lores_train_data_dir} \ | ||
| data/lang $gmm_dir $lat_dir | ||
| rm $lat_dir/fsts.*.gz # save space | ||
| fi | ||
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| if [ $stage -le 14 ]; 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. | ||
| if [ -f $tree_dir/final.mdl ]; then | ||
| echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it." | ||
| exit 1; | ||
| fi | ||
| steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \ | ||
| --context-opts "--context-width=2 --central-position=1" \ | ||
| --leftmost-questions-truncate -1 \ | ||
| --cmd "$train_cmd" 4200 ${lores_train_data_dir} data/lang_chain $ali_dir $tree_dir | ||
| fi | ||
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| xent_regularize=0.1 | ||
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| if [ $stage -le 15 ]; then | ||
| 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) | ||
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| mkdir -p $dir/configs | ||
| cat <<EOF > $dir/configs/network.xconfig | ||
| input dim=100 name=ivector | ||
| input dim=40 name=input | ||
| # please note that it is important to have input layer with the name=input | ||
| # as the layer immediately preceding the fixed-affine-layer to enable | ||
| # the use of short notation for the descriptor | ||
| fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat | ||
| # the first splicing is moved before the lda layer, so no splicing here | ||
| relu-renorm-layer name=tdnn1 dim=1024 | ||
| relu-renorm-layer name=tdnn2 input=Append(-1,0,1) dim=1024 | ||
| relu-renorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024 | ||
| # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults | ||
| lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 | ||
| relu-renorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024 | ||
| relu-renorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024 | ||
| relu-renorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024 | ||
| lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 | ||
| relu-renorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024 | ||
| relu-renorm-layer name=tdnn8 input=Append(-3,0,3) dim=1024 | ||
| relu-renorm-layer name=tdnn9 input=Append(-3,0,3) dim=1024 | ||
| lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 | ||
| ## adding the layers for chain branch | ||
| output-layer name=output input=lstm3 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=lstm3 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5 | ||
| EOF | ||
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| steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/ | ||
| fi | ||
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| if [ $stage -le 16 ]; 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/ami-$(date +'%m_%d_%H_%M')/s5b/$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 $train_ivector_dir \ | ||
| --feat.cmvn-opts "--norm-means=false --norm-vars=false" \ | ||
| --chain.xent-regularize $xent_regularize \ | ||
| --chain.leaky-hmm-coefficient 0.1 \ | ||
| --chain.l2-regularize 0.00005 \ | ||
| --chain.apply-deriv-weights false \ | ||
| --chain.lm-opts="--num-extra-lm-states=2000" \ | ||
| --egs.dir "$common_egs_dir" \ | ||
| --egs.opts "--frames-overlap-per-eg 0" \ | ||
| --egs.chunk-width $chunk_width \ | ||
| --egs.chunk-left-context $chunk_left_context \ | ||
| --egs.chunk-right-context $chunk_right_context \ | ||
| --trainer.dropout-schedule $dropout_schedule \ | ||
| --trainer.num-chunk-per-minibatch 64 \ | ||
| --trainer.frames-per-iter 1500000 \ | ||
| --trainer.num-epochs 4 \ | ||
| --trainer.optimization.shrink-value 0.99 \ | ||
| --trainer.optimization.num-jobs-initial 2 \ | ||
| --trainer.optimization.num-jobs-final 12 \ | ||
| --trainer.optimization.initial-effective-lrate 0.001 \ | ||
| --trainer.optimization.final-effective-lrate 0.0001 \ | ||
| --trainer.max-param-change 2.0 \ | ||
| --trainer.deriv-truncate-margin 8 \ | ||
| --cleanup.remove-egs true \ | ||
| --feat-dir $train_data_dir \ | ||
| --tree-dir $tree_dir \ | ||
| --lat-dir $lat_dir \ | ||
| --dir $dir | ||
| fi | ||
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| graph_dir=$dir/graph_${LM} | ||
| if [ $stage -le 17 ]; then | ||
| # Note: it might appear that this data/lang_chain directory is mismatched, and it is as | ||
| # far as the 'topo' is concerned, but this script doesn't read the 'topo' from | ||
| # the lang directory. | ||
| utils/mkgraph.sh --left-biphone --self-loop-scale 1.0 data/lang_${LM} $dir $graph_dir | ||
| fi | ||
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| if [ $stage -le 18 ]; then | ||
| rm $dir/.error 2>/dev/null || true | ||
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| [ -z $extra_left_context ] && extra_left_context=$chunk_left_context; | ||
| [ -z $frames_per_chunk ] && frames_per_chunk=$chunk_width; | ||
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| for decode_set in dev eval; do | ||
| ( | ||
| steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \ | ||
| --nj $nj --cmd "$decode_cmd" \ | ||
| --extra-left-context $extra_left_context \ | ||
| --frames-per-chunk "$frames_per_chunk" \ | ||
| --online-ivector-dir exp/$mic/nnet3${nnet3_affix}/ivectors_${decode_set}_hires \ | ||
| --scoring-opts "--min-lmwt 5 " \ | ||
| $graph_dir data/$mic/${decode_set}_hires $dir/decode_${decode_set} || exit 1; | ||
| ) || touch $dir/.error & | ||
| done | ||
| wait | ||
| if [ -f $dir/.error ]; then | ||
| echo "$0: something went wrong in decoding" | ||
| exit 1 | ||
| fi | ||
| fi | ||
| exit 0 |
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