update (#32)

.gitignore

@@ -73,15 +73,17 @@ GSYMS
 /src/kaldi.mk.bak
 
 # /egs/
-/egs/*/s*/mfcc
-/egs/*/s*/plp
-/egs/*/s*/exp
-/egs/*/s*/data
+/egs/*/*/mfcc
+/egs/*/*/plp
+/egs/*/*/exp
+/egs/*/*/data
 
 # /tools/
+/tools/pocolm/
 /tools/ATLAS/
 /tools/atlas3.8.3.tar.gz
 /tools/irstlm/
+/tools/mitlm/
 /tools/openfst
 /tools/openfst-1.3.2.tar.gz
 /tools/openfst-1.3.2/
@@ -101,6 +103,8 @@ GSYMS
 /tools/openfst-1.6.2/
 /tools/openfst-1.6.5.tar.gz
 /tools/openfst-1.6.5/
+/tools/openfst-1.6.7.tar.gz
+/tools/openfst-1.6.7/
 /tools/BeamformIt/
 /tools/libsndfile-1.0.25.tar.gz
 /tools/libsndfile-1.0.25/
@@ -141,3 +145,6 @@ GSYMS
 /tools/mmseg-1.3.0.tar.gz
 /tools/mmseg-1.3.0/
 /kaldiwin_vs*
+/tools/cub-1.8.0.zip
+/tools/cub-1.8.0/
+/tools/cub

.travis.yml

@@ -21,6 +21,7 @@ addons:
       - gfortran-4.9
       - liblapack-dev
       - clang-3.8
+      - sox
 
 branches:
   only:
@@ -47,8 +48,8 @@ script:
   # http://peter.eisentraut.org/blog/2014/12/01/ccache-and-clang-part-3/
   # for the explanation why extra switches needed for clang with ccache.
   - CXX="ccache clang++-3.8 -Qunused-arguments -fcolor-diagnostics -Wno-tautological-compare"
-    CFLAGS="-march=native"
-    LDFLAGS="-llapack"
+    CFLAGS=""
+    LDFLAGS="-llapack -Wl,-fuse-ld=gold"
     INCDIRS="$XROOT/usr/include"
     LIBDIRS="$XROOT/usr/lib"
       tools/extras/travis_script.sh

COPYING

@@ -56,7 +56,7 @@ contributors and original source material as well as the full text of the Apache
 License v 2.0 are set forth below.
 
 Individual Contributors (in alphabetical order)
-      
+
       Mohit Agarwal
       Tanel Alumae
       Gilles Boulianne
@@ -123,7 +123,7 @@ Individual Contributors (in alphabetical order)
       Haihua Xu
       Hainan Xu
       Xiaohui Zhang
-      
+
 Other Source Material
 
     This project includes a port and modification of materials from JAMA: A Java
@@ -136,9 +136,9 @@ Other Source Material
   "Signal processing with lapped transforms," Artech House, Inc., 1992.  The
   current copyright holder, Henrique S. Malvar, has given his permission for the
   release of this modified version under the Apache License 2.0.
-  
-  This project includes material from the OpenFST Library v1.2.7 available at 
-  http://www.openfst.org and released under the Apache License v. 2.0.   
+
+  This project includes material from the OpenFST Library v1.2.7 available at
+  http://www.openfst.org and released under the Apache License v. 2.0.
 
   [OpenFst COPYING file begins here]
 
@@ -147,7 +147,7 @@ Other Source Material
     You may obtain a copy of the License at
 
        http://www.apache.org/licenses/LICENSE-2.0
- 
+
     Unless required by applicable law or agreed to in writing, software
     distributed under the License is distributed on an "AS IS" BASIS,
     WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

README.md

@@ -1,5 +1,4 @@
-[![Build Status](https://travis-ci.org/kaldi-asr/kaldi.svg?branch=master)](https://travis-ci.org/kaldi-asr/kaldi)
-
+[![Build Status](https://travis-ci.com/kaldi-asr/kaldi.svg?branch=master)](https://travis-ci.com/kaldi-asr/kaldi)
 Kaldi Speech Recognition Toolkit
 ================================
 

egs/aishell/s5/RESULTS

@@ -1,8 +1,18 @@
-%WER 33.82 [ 35432 / 104765, 743 ins, 3991 del, 30698 sub ] exp/mono/decode_test/cer_12_0.0
-%WER 19.39 [ 20310 / 104765, 903 ins, 1452 del, 17955 sub ] exp/tri1/decode_test/cer_13_0.5
-%WER 19.23 [ 20147 / 104765, 910 ins, 1287 del, 17950 sub ] exp/tri2/decode_test/cer_14_0.5
-%WER 17.14 [ 17961 / 104765, 812 ins, 1024 del, 16125 sub ] exp/tri3a/decode_test/cer_14_0.0
-%WER 13.64 [ 14294 / 104765, 669 ins, 736 del, 12889 sub ] exp/tri4a/decode_test/cer_14_0.5
-%WER 12.23 [ 12809 / 104765, 656 ins, 580 del, 11573 sub ] exp/tri5a/decode_test/cer_13_1.0
-%WER 8.45 [ 8849 / 104765, 312 ins, 538 del, 7999 sub ] exp/nnet3/tdnn_sp/decode_test/cer_13_1.0
-%WER 7.46 [ 7813 / 104765, 287 ins, 472 del, 7054 sub ] exp/chain/tdnn_1a_sp/decode_test/cer_10_1.0
+%WER 36.41 [ 38146 / 104765, 837 ins, 3114 del, 34195 sub ] exp/mono/decode_test/cer_10_0.0
+%WER 18.76 [ 19654 / 104765, 949 ins, 1152 del, 17553 sub ] exp/tri1/decode_test/cer_13_0.5
+%WER 18.64 [ 19531 / 104765, 941 ins, 1159 del, 17431 sub ] exp/tri2/decode_test/cer_14_0.5
+%WER 17.04 [ 17849 / 104765, 810 ins, 1021 del, 16018 sub ] exp/tri3a/decode_test/cer_14_0.5
+%WER 13.82 [ 14482 / 104765, 764 ins, 670 del, 13048 sub ] exp/tri4a/decode_test/cer_13_0.5
+%WER 12.12 [ 12694 / 104765, 751 ins, 523 del, 11420 sub ] exp/tri5a/decode_test/cer_13_0.5
+%WER 8.65 [ 9064 / 104765, 367 ins, 455 del, 8242 sub ] exp/nnet3/tdnn_sp/decode_test/cer_14_0.5
+%WER 7.48 [ 7839 / 104765, 285 ins, 454 del, 7100 sub ] exp/chain/tdnn_1a_sp/decode_test/cer_10_1.0
+
+# nnet3 tdnn with online pitch, local/nnet3/tuning/tun_tdnn_2a.sh
+%WER 8.64 [ 9050 / 104765, 349 ins, 521 del, 8180 sub ] exp/nnet3/tdnn_sp/decode_test/cer_15_0.5
+%WER 8.72 [ 9135 / 104765, 367 ins, 422 del, 8346 sub ] exp/nnet3/tdnn_sp_online/decode_test/cer_12_1.0
+%WER 9.36 [ 9807 / 104765, 386 ins, 441 del, 8980 sub ] exp/nnet3/tdnn_sp_online/decode_test_per_utt/cer_13_1.0
+
+# chain with online pitch, local/chain/tuning/run_tdnn_2a.sh
+%WER 7.45 [ 7807 / 104765, 340 ins, 497 del, 6970 sub ] exp/chain/tdnn_2a_sp/decode_test/cer_11_0.5
+%WER 7.43 [ 7780 / 104765, 341 ins, 469 del, 6970 sub ] exp/chain/tdnn_2a_sp_online/decode_test/cer_11_0.5
+%WER 7.92 [ 8296 / 104765, 384 ins, 472 del, 7440 sub ] exp/chain/tdnn_2a_sp_online/decode_test_per_utt/cer_11_0.5

egs/aishell/s5/conf/online_pitch.conf

@@ -0,0 +1,4 @@
+--sample-frequency=16000
+--simulate-first-pass-online=true
+--normalization-right-context=25
+--frames-per-chunk=10

egs/aishell/s5/local/aishell_prepare_dict.sh

@@ -15,21 +15,9 @@ mkdir -p $dict_dir
 cp $res_dir/lexicon.txt $dict_dir
 
 cat $dict_dir/lexicon.txt | awk '{ for(n=2;n<=NF;n++){ phones[$n] = 1; }} END{for (p in phones) print p;}'| \
-  sort -u |\
-  perl -e '
-  my %ph_cl;
-  while (<STDIN>) {
-    $phone = $_;
-    chomp($phone);
-    chomp($_);
-    $phone = $_;
-    next if ($phone eq "sil");
-    if (exists $ph_cl{$phone}) { push(@{$ph_cl{$phone}}, $_)  }
-    else { $ph_cl{$phone} = [$_]; }
-  }
-  foreach $key ( keys %ph_cl ) {
-     print "@{ $ph_cl{$key} }\n"
-  }
+  perl -e 'while(<>){ chomp($_); $phone = $_; next if ($phone eq "sil");
+    m:^([^\d]+)(\d*)$: || die "Bad phone $_"; $q{$1} .= "$phone "; }
+    foreach $l (values %q) {print "$l\n";}
   ' | sort -k1 > $dict_dir/nonsilence_phones.txt  || exit 1;
 
 echo sil > $dict_dir/silence_phones.txt

egs/aishell/s5/local/aishell_train_lms.sh

@@ -23,7 +23,7 @@ kaldi_lm=`which train_lm.sh`
 if [ -z $kaldi_lm ]; then
   echo "$0: train_lm.sh is not found. That might mean it's not installed"
   echo "$0: or it is not added to PATH"
-  echo "$0: Use the script tools/extra/install_kaldi_lm.sh to install it"
+  echo "$0: Use the script tools/extras/install_kaldi_lm.sh to install it"
   exit 1
 fi
 

egs/aishell/s5/local/chain/tuning/run_tdnn_1a.sh

@@ -90,7 +90,7 @@ if [ $stage -le 10 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig

egs/aishell/s5/local/chain/tuning/run_tdnn_2a.sh

@@ -0,0 +1,211 @@
+#!/bin/bash
+
+# This script is based on run_tdnn_1a.sh.
+# This setup used online pitch to train the neural network.
+# It requires a online_pitch.conf in the conf dir.
+
+set -e
+
+# configs for 'chain'
+affix=
+stage=0
+train_stage=-10
+get_egs_stage=-10
+dir=exp/chain/tdnn_2a  # Note: _sp will get added to this
+decode_iter=
+
+# training options
+num_epochs=4
+initial_effective_lrate=0.001
+final_effective_lrate=0.0001
+max_param_change=2.0
+final_layer_normalize_target=0.5
+num_jobs_initial=2
+num_jobs_final=12
+minibatch_size=128
+frames_per_eg=150,110,90
+remove_egs=true
+common_egs_dir=
+xent_regularize=0.1
+
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+# The iVector-extraction and feature-dumping parts are the same as the standard
+# nnet3 setup, and you can skip them by setting "--stage 8" if you have already
+# run those things.
+
+dir=${dir}${affix:+_$affix}_sp
+train_set=train_sp
+ali_dir=exp/tri5a_sp_ali
+treedir=exp/chain/tri6_7d_tree_sp
+lang=data/lang_chain
+
+
+# if we are using the speed-perturbed data we need to generate
+# alignments for it.
+local/nnet3/run_ivector_common.sh --stage $stage --online true || exit 1;
+
+if [ $stage -le 7 ]; then
+  # Get the alignments as lattices (gives the LF-MMI training more freedom).
+  # use the same num-jobs as the alignments
+  nj=$(cat $ali_dir/num_jobs) || exit 1;
+  steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/$train_set \
+    data/lang exp/tri5a exp/tri5a_sp_lats
+  rm exp/tri5a_sp_lats/fsts.*.gz # save space
+fi
+
+if [ $stage -le 8 ]; 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
+
+if [ $stage -le 9 ]; then
+  # Build a tree using our new topology. This is the critically different
+  # step compared with other recipes.
+  steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
+      --context-opts "--context-width=2 --central-position=1" \
+      --cmd "$train_cmd" 5000 data/$train_set $lang $ali_dir $treedir
+fi
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating neural net configs using the xconfig parser";
+
+  num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=43 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-layer name=tdnn1 dim=625
+  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=625
+  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=625
+  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=625
+  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=625
+  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=625
+
+  ## adding the layers for chain branch
+  relu-batchnorm-layer name=prefinal-chain input=tdnn6 dim=625 target-rms=0.5
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
+
+  # adding the layers for xent branch
+  # This block prints the configs for a separate output that will be
+  # trained with a cross-entropy objective in the 'chain' models... this
+  # has the effect of regularizing the hidden parts of the model.  we use
+  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+  # 0.5 / args.xent_regularize is suitable as it means the xent
+  # final-layer learns at a rate independent of the regularization
+  # constant; and the 0.5 was tuned so as to make the relative progress
+  # similar in the xent and regular final layers.
+  relu-batchnorm-layer name=prefinal-xent input=tdnn6 dim=625 target-rms=0.5
+  output-layer name=output-xent dim=$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 11 ]; 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/aishell-$(date +'%m_%d_%H_%M')/s5c/$dir/egs/storage $dir/egs/storage
+  fi
+
+  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 $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.00005 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --egs.dir "$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 $minibatch_size \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.optimization.num-jobs-initial $num_jobs_initial \
+    --trainer.optimization.num-jobs-final $num_jobs_final \
+    --trainer.optimization.initial-effective-lrate $initial_effective_lrate \
+    --trainer.optimization.final-effective-lrate $final_effective_lrate \
+    --trainer.max-param-change $max_param_change \
+    --cleanup.remove-egs $remove_egs \
+    --feat-dir data/${train_set}_hires_online \
+    --tree-dir $treedir \
+    --lat-dir exp/tri5a_sp_lats \
+    --dir $dir  || exit 1;
+fi
+
+if [ $stage -le 12 ]; 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_test $dir $dir/graph
+fi
+
+graph_dir=$dir/graph
+if [ $stage -le 13 ]; then
+  for test_set in dev test; do
+    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj 10 --cmd "$decode_cmd" \
+      --online-ivector-dir exp/nnet3/ivectors_$test_set \
+      $graph_dir data/${test_set}_hires_online $dir/decode_${test_set} || exit 1;
+  done
+fi
+
+if [ $stage -le 14 ]; then
+  steps/online/nnet3/prepare_online_decoding.sh --mfcc-config conf/mfcc_hires.conf \
+    --add-pitch true \
+    $lang exp/nnet3/extractor "$dir" ${dir}_online || exit 1;
+fi
+
+dir=${dir}_online
+if [ $stage -le 15 ]; then
+  for test_set in dev test; do
+    steps/online/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj 10 --cmd "$decode_cmd" \
+      --config conf/decode.config \
+      $graph_dir data/${test_set}_hires_online $dir/decode_${test_set} || exit 1;
+  done
+fi
+
+if [ $stage -le 16 ]; then
+  for test_set in dev test; do
+    steps/online/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj 10 --cmd "$decode_cmd" --per-utt true \
+      --config conf/decode.config \
+      $graph_dir data/${test_set}_hires_online $dir/decode_${test_set}_per_utt || exit 1;
+  done
+fi
+
+exit;