[WIP] [egs] port tdnn_7m23t script to librispeech

egs/librispeech/s5/local/chain/run_chain_common.sh

@@ -13,6 +13,8 @@ gmm_dir=
 ali_dir=
 lores_train_data_dir=
 
+num_leaves=6000
+
 # output directory names. They are also compulsory.
 lang=
 lat_dir=
@@ -74,7 +76,7 @@ if [ $stage -le 13 ]; then
   fi
   steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
       --context-opts "--context-width=2 --central-position=1" \
-      --cmd "$train_cmd" 6000 ${lores_train_data_dir} $lang $ali_dir $tree_dir
+      --cmd "$train_cmd" $num_leaves ${lores_train_data_dir} $lang $ali_dir $tree_dir
 fi
 
 exit 0;

egs/librispeech/s5/local/chain/run_tdnn.sh

@@ -1 +1 @@
-tuning/run_tdnn_1b.sh
+tuning/run_tdnn_1c.sh

egs/librispeech/s5/local/chain/tuning/run_tdnn_1c.sh

@@ -0,0 +1,265 @@
+#!/bin/bash
+set -e
+
+## Adapted from swbd for librispeech by David van Leeuwen
+
+# 7n is a kind of factorized TDNN, with skip connections
+
+# steps/info/chain_dir_info.pl exp/chain_cleaned/tdnn_1c_sp
+# exp/chain_cleaned/tdnn_1c_sp: num-iters=1307 nj=3..16 num-params=20.1M dim=40+100->6024 combine=-0.051->-0.050 (over 23) xent:train/valid[869,1306,final]=(-0.808,-0.767,-0.771/-0.828,-0.780,-0.787) logprob:train/valid[869,1306,final]=(-0.051,-0.049,-0.047/-0.059,-0.056,-0.056)
+
+# local/chain/compare_wer.sh exp/chain_cleaned/tdnn_1b_sp exp/chain_cleaned/tdnn_1c_sp
+# System                      tdnn_1b_sp tdnn_1c_sp
+# WER on dev(fglarge)              3.77      3.35
+# WER on dev(tglarge)              3.90      3.49
+# WER on dev(tgmed)                4.89      4.30
+# WER on dev(tgsmall)              5.47      4.78
+# WER on dev_other(fglarge)       10.05      8.76
+# WER on dev_other(tglarge)       10.80      9.26
+# WER on dev_other(tgmed)         13.07     11.21
+# WER on dev_other(tgsmall)       14.46     12.47
+# WER on test(fglarge)             4.20      3.87
+# WER on test(tglarge)             4.28      4.08
+# WER on test(tgmed)               5.31      4.80
+# WER on test(tgsmall)             5.97      5.25
+# WER on test_other(fglarge)      10.44      8.95
+# WER on test_other(tglarge)      11.05      9.41
+# WER on test_other(tgmed)        13.36     11.52
+# WER on test_other(tgsmall)      14.90     12.66
+# Final train prob              -0.0670   -0.0475
+# Final valid prob              -0.0704   -0.0555
+# Final train prob (xent)       -1.0502   -0.7708
+# Final valid prob (xent)       -1.0441   -0.7874
+
+# configs for 'chain'
+stage=0
+decode_nj=50
+min_seg_len=1.55
+train_set=train_960_cleaned
+gmm=tri6b_cleaned
+nnet3_affix=_cleaned
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=1c
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+
+# TDNN options
+frames_per_eg=150,110,100
+remove_egs=true
+common_egs_dir=
+xent_regularize=0.1
+
+test_online_decoding=true  # if true, it will run the last decoding stage.
+
+# 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
+
+# The iVector-extraction and feature-dumping parts are the same as the standard
+# nnet3 setup, and you can skip them by setting "--stage 11" if you have already
+# run those things.
+
+local/nnet3/run_ivector_common.sh --stage $stage \
+                                  --min-seg-len $min_seg_len \
+                                  --train-set $train_set \
+                                  --gmm $gmm \
+				  --num-threads-ubm 6 --num-processes 3 \
+                                  --nnet3-affix "$nnet3_affix" || exit 1;
+
+gmm_dir=exp/$gmm
+ali_dir=exp/${gmm}_ali_${train_set}_sp_comb
+tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+lang=data/lang_chain
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_comb_lats
+dir=exp/chain${nnet3_affix}/tdnn${affix:+_$affix}_sp
+train_data_dir=data/${train_set}_sp_hires_comb
+lores_train_data_dir=data/${train_set}_sp_comb
+train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires_comb
+
+# if we are using the speed-perturbed data we need to generate
+# alignments for it.
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
+    $lores_train_data_dir/feats.scp $ali_dir/ali.1.gz; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+# Please take this as a reference on how to specify all the options of
+# local/chain/run_chain_common.sh
+local/chain/run_chain_common.sh --stage $stage \
+                                --gmm-dir $gmm_dir \
+                                --ali-dir $ali_dir \
+                                --lores-train-data-dir ${lores_train_data_dir} \
+                                --lang $lang \
+                                --lat-dir $lat_dir \
+				--num-leaves 7000 \
+                                --tree-dir $tree_dir || exit 1;
+
+if [ $stage -le 14 ]; then
+  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)
+  opts="l2-regularize=0.002"
+  linear_opts="orthonormal-constraint=1.0"
+  output_opts="l2-regularize=0.0005 bottleneck-dim=256"
+
+  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-batchnorm-layer name=tdnn1 $opts dim=1280
+  linear-component name=tdnn2l dim=256 $linear_opts input=Append(-1,0)
+  relu-batchnorm-layer name=tdnn2 $opts input=Append(0,1) dim=1280
+  linear-component name=tdnn3l dim=256 $linear_opts
+  relu-batchnorm-layer name=tdnn3 $opts dim=1280
+  linear-component name=tdnn4l dim=256 $linear_opts input=Append(-1,0)
+  relu-batchnorm-layer name=tdnn4 $opts input=Append(0,1) dim=1280
+  linear-component name=tdnn5l dim=256 $linear_opts
+  relu-batchnorm-layer name=tdnn5 $opts dim=1280 input=Append(tdnn5l, tdnn3l)
+  linear-component name=tdnn6l dim=256 $linear_opts input=Append(-3,0)
+  relu-batchnorm-layer name=tdnn6 $opts input=Append(0,3) dim=1280
+  linear-component name=tdnn7l dim=256 $linear_opts input=Append(-3,0)
+  relu-batchnorm-layer name=tdnn7 $opts input=Append(0,3,tdnn6l,tdnn4l,tdnn2l) dim=1280
+  linear-component name=tdnn8l dim=256 $linear_opts input=Append(-3,0)
+  relu-batchnorm-layer name=tdnn8 $opts input=Append(0,3) dim=1280
+  linear-component name=tdnn9l dim=256 $linear_opts input=Append(-3,0)
+  relu-batchnorm-layer name=tdnn9 $opts input=Append(0,3,tdnn8l,tdnn6l,tdnn4l) dim=1280
+  linear-component name=tdnn10l dim=256 $linear_opts input=Append(-3,0)
+  relu-batchnorm-layer name=tdnn10 $opts input=Append(0,3) dim=1280
+  linear-component name=tdnn11l dim=256 $linear_opts input=Append(-3,0)
+  relu-batchnorm-layer name=tdnn11 $opts input=Append(0,3,tdnn10l,tdnn8l,tdnn6l) dim=1280
+  linear-component name=prefinal-l dim=256 $linear_opts
+
+  relu-batchnorm-layer name=prefinal-chain input=prefinal-l $opts dim=1280
+  output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
+
+  relu-batchnorm-layer name=prefinal-xent input=prefinal-l $opts dim=1280
+  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor $output_opts
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 15 ]; then
+  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 $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.0 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --egs.dir "$common_egs_dir" \
+    --egs.stage $get_egs_stage \
+    --egs.opts "--frames-overlap-per-eg 0" \
+    --egs.chunk-width $frames_per_eg \
+    --trainer.num-chunk-per-minibatch 128 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.num-epochs 6 \
+    --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.max-param-change 2.0 \
+    --cleanup.remove-egs $remove_egs \
+    --feat-dir $train_data_dir \
+    --tree-dir $tree_dir \
+    --lat-dir $lat_dir \
+    --dir $dir  || exit 1;
+
+fi
+
+graph_dir=$dir/graph_tgsmall
+if [ $stage -le 16 ]; 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 --remove-oov data/lang_test_tgsmall $dir $graph_dir
+  # remove <UNK> from the graph, and convert back to const-FST.
+  fstrmsymbols --apply-to-output=true --remove-arcs=true "echo 3|" $graph_dir/HCLG.fst - | \
+    fstconvert --fst_type=const > $graph_dir/temp.fst
+  mv $graph_dir/temp.fst $graph_dir/HCLG.fst
+fi
+
+iter_opts=
+if [ ! -z $decode_iter ]; then
+  iter_opts=" --iter $decode_iter "
+fi
+if [ $stage -le 17 ]; then
+  rm $dir/.error 2>/dev/null || true
+  for decode_set in test_clean test_other dev_clean dev_other; do
+      (
+      steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+          --nj $decode_nj --cmd "$decode_cmd" $iter_opts \
+          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${decode_set}_hires \
+          $graph_dir data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_tgsmall || exit 1
+      steps/lmrescore.sh --cmd "$decode_cmd" --self-loop-scale 1.0 data/lang_test_{tgsmall,tgmed} \
+          data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_{tgsmall,tgmed} || exit 1
+      steps/lmrescore_const_arpa.sh \
+          --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
+          data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_{tgsmall,tglarge} || exit 1
+      steps/lmrescore_const_arpa.sh \
+          --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
+          data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_{tgsmall,fglarge} || exit 1
+      ) || touch $dir/.error &
+  done
+  wait
+  if [ -f $dir/.error ]; then
+    echo "$0: something went wrong in decoding"
+    exit 1
+  fi
+fi
+
+if $test_online_decoding && [ $stage -le 18 ]; 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 \
+       $lang exp/nnet3${nnet3_affix}/extractor $dir ${dir}_online
+
+  rm $dir/.error 2>/dev/null || true
+  for data in test_clean test_other dev_clean dev_other; do
+    (
+      nspk=$(wc -l <data/${data}_hires/spk2utt)
+      # note: we just give it "data/${data}" as it only uses the wav.scp, the
+      # feature type does not matter.
+      steps/online/nnet3/decode.sh \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --nj $nspk --cmd "$decode_cmd" \
+          $graph_dir data/${data} ${dir}_online/decode_${data}_tgsmall || 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;

egs/librispeech/s5/local/nnet3/run_ivector_common.sh

@@ -16,6 +16,7 @@ train_set=train_960_cleaned    # you might set this to e.g. train_960
 gmm=tri6b_cleaned         # This specifies a GMM-dir from the features of the type you're training the system on;
                          # it should contain alignments for 'train_set'.
 num_threads_ubm=32
+num_processes=4
 nnet3_affix=_cleaned     # affix for exp/nnet3 directory to put iVector stuff in, so it
                          # becomes exp/nnet3_cleaned or whatever.
 
@@ -161,7 +162,7 @@ if [ $stage -le 8 ]; then
   # this one has a fairly small dim (defaults to 100) so we don't use all of it,
   # we use just the 60k subset (about one fifth of the data, or 200 hours).
   echo "$0: training the iVector extractor"
-  steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 \
+  steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 --num-processes $num_processes \
     data/${train_set}_sp_mixed_hires_60k exp/nnet3${nnet3_affix}/diag_ubm exp/nnet3${nnet3_affix}/extractor || exit 1;
 fi
 

egs/wsj/s5/utils/parallel/slurm.pl

@@ -477,7 +477,7 @@ sub exec_command {
         # the following (.kick) commands are basically workarounds for NFS bugs.
         if (rand() < 0.25) { # don't do this every time...
           if (rand() > 0.5) {
-            system("touch $qdir/.kick");
+            system("touch $qdir/.kick 2>/dev/null");
           } else {
             system("rm $qdir/.kick 2>/dev/null");
           }