update hkust

egs/hkust/s5/RESULTS

@@ -7,6 +7,17 @@ exp/tri5a/decode/cer_13:%WER 49.67 [ 27891 / 56154, 2877 ins, 4538 del, 20476 su
 exp/tri5a_mce/decode/cer_11:%WER 44.74 [ 25125 / 56154, 2112 ins, 4108 del, 18905 sub ]
 exp/tri5a_mmi_b0.1/decode/cer_11:%WER 44.24 [ 24840 / 56154, 2060 ins, 4118 del, 18662 sub ]
 exp/tri5a_mpe/decode/cer_12:%WER 44.96 [ 25247 / 56154, 2233 ins, 4174 del, 18840 sub ]
+exp/sgmm2_5a/decode/cer_11:%WER 43.44 [ 24391 / 56154, 2646 ins, 4066 del, 17679 sub ]
 
+# nnet1 results
+exp/dnn5b_pretrain-dbn_dnn/decode/cer_10:%WER 39.42 [ 22134 / 56154, 2507 ins, 3730 del, 15897 sub ]
+exp/dnn5b_pretrain-dbn_dnn_smbr/decode/cer_11:%WER 36.50 [ 20499 / 56154, 1915 ins, 3312 del, 15272 sub ]
+exp/dnn5b_pretrain-dbn_dnn_smbr_i1lats/decode/cer_12:%WER 35.93 [ 20177 / 56154, 1949 ins, 3188 del, 15040 sub ]
+exp/cnn5c/decode/cer_10:%WER 40.13 [ 22536 / 56154, 2329 ins, 3962 del, 16245 sub ]
+exp/cnn5c_pretrain-dbn_dnn/decode/cer_10:%WER 38.80 [ 21790 / 56154, 2470 ins, 3582 del, 15738 sub ]
+exp/lstm5e/decode/cer_10:%WER 37.61 [ 21121 / 56154, 1829 ins, 3941 del, 15351 sub ]
+
+# nnet2 results
+exp/nnet2_5d/decode/cer_10:%WER 38.59 [ 21669 / 56154, 2498 ins, 3581 del, 15590 sub ]
 # ConvNet with 2 convolutional layers and 2 ReLU layers
 exp/nnet2_convnet/decode/cer_10:%WER 40.73 [ 22873 / 56154, 2609 ins, 3712 del, 16552 sub ]

egs/hkust/s5/local/nnet/run_cnn.sh

+#!/bin/bash
+
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+
+. ./path.sh ## Source the tools/utils (import the queue.pl)
+
+dev=data_fbank/dev
+train=data_fbank/train
+
+dev_original=data/dev
+train_original=data/train
+
+gmm=exp/tri5a
+
+stage=0
+. utils/parse_options.sh || exit 1;
+
+
+# Make the FBANK features
+if [ $stage -le 0 ]; then
+  # Dev set
+  utils/copy_data_dir.sh $dev_original $dev || exit 1; rm $dev/{cmvn,feats}.scp
+  steps/make_fbank_pitch.sh --nj 10 --cmd "$train_cmd" \
+     $dev $dev/log $dev/data || exit 1;
+  steps/compute_cmvn_stats.sh $dev $dev/log $dev/data || exit 1;
+  # Training set
+  utils/copy_data_dir.sh $train_original $train || exit 1; rm $train/{cmvn,feats}.scp
+  steps/make_fbank_pitch.sh --nj 10 --cmd "$train_cmd" \
+     $train $train/log $train/data || exit 1;
+  steps/compute_cmvn_stats.sh $train $train/log $train/data || exit 1;
+
+  # Split the training set
+  utils/subset_data_dir_tr_cv.sh --cv-spk-percent 10 $train ${train}_tr90 ${train}_cv10
+fi
+
+# Run the CNN pre-training.
+if [ $stage -le 1 ]; then
+  dir=exp/cnn5c
+  ali=${gmm}_ali
+  # Train
+  $cuda_cmd $dir/log/train_nnet.log \
+    steps/nnet/train.sh \
+      --cmvn-opts "--norm-means=true --norm-vars=true" \
+      --delta-opts "--delta-order=2" --splice 5 \
+      --network-type cnn1d --cnn-proto-opts "--patch-dim1 7 --pitch-dim 3" \
+      --hid-layers 2 --learn-rate 0.008 \
+      ${train}_tr90 ${train}_cv10 data/lang $ali $ali $dir || exit 1;
+
+  # Decode with the trigram language model.
+  steps/nnet/decode.sh --nj 10 --cmd "$decode_cmd" \
+    --config conf/decode_dnn.config --acwt 0.1 \
+    $gmm/graph $dev $dir/decode || exit 1;
+fi
+
+# Pre-train stack of RBMs on top of the convolutional layers (2 layers, 2000 units)
+if [ $stage -le 2 ]; then
+  dir=exp/cnn5c_pretrain-dbn
+  transf_cnn=exp/cnn5c/final.feature_transform_cnn # transform with convolutional layers
+  # Train
+  $cuda_cmd $dir/log/pretrain_dbn.log \
+    steps/nnet/pretrain_dbn.sh --nn-depth 2 --hid-dim 2000 --rbm-iter 1 \
+    --feature-transform $transf_cnn --input-vis-type bern \
+    --param-stddev-first 0.05 --param-stddev 0.05 \
+    $train $dir || exit 1
+fi
+
+# Re-align using CNN
+if [ $stage -le 3 ]; then
+  dir=exp/cnn5c
+  steps/nnet/align.sh --nj 10 --cmd "$train_cmd" \
+    $train data/lang $dir ${dir}_ali || exit 1
+fi
+
+# Train the DNN optimizing cross-entropy.
+if [ $stage -le 4 ]; then
+  dir=exp/cnn5c_pretrain-dbn_dnn; [ ! -d $dir ] && mkdir -p $dir/log;
+  ali=exp/cnn5c_ali
+  feature_transform=exp/cnn5c/final.feature_transform
+  feature_transform_dbn=exp/cnn5c_pretrain-dbn/final.feature_transform
+  dbn=exp/cnn5c_pretrain-dbn/2.dbn
+  cnn_dbn=$dir/cnn_dbn.nnet
+  { # Concatenate CNN layers and DBN,
+    num_components=$(nnet-info $feature_transform | grep -m1 num-components | awk '{print $2;}')
+    nnet-concat "nnet-copy --remove-first-layers=$num_components $feature_transform_dbn - |" $dbn $cnn_dbn \
+      2>$dir/log/concat_cnn_dbn.log || exit 1 
+  }
+  # Train
+  $cuda_cmd $dir/log/train_nnet.log \
+    steps/nnet/train.sh --feature-transform $feature_transform --dbn $cnn_dbn --hid-layers 0 \
+    ${train}_tr90 ${train}_cv10 data/lang $ali $ali $dir || exit 1;
+
+  # Decode with the trigram language model.
+  steps/nnet/decode.sh --nj 10 --cmd "$decode_cmd" \
+    --config conf/decode_dnn.config --acwt 0.1 \
+    $gmm/graph $dev $dir/decode || exit 1;
+fi
+
+# Sequence training using sMBR criterion, we do Stochastic-GD 
+# with per-utterance updates. For RM good acwt is 0.2 (For WSJ maybe 0.1)
+dir=exp/cnn5c_pretrain-dbn_dnn_smbr
+srcdir=exp/cnn5c_pretrain-dbn_dnn
+acwt=0.1
+
+# First we generate lattices and alignments:
+if [ $stage -le 5 ]; then
+  steps/nnet/align.sh --nj 10 --cmd "$train_cmd" \
+    $train data/lang $srcdir ${srcdir}_ali || exit 1;
+  steps/nnet/make_denlats.sh --nj 10 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt $acwt \
+    $train data/lang $srcdir ${srcdir}_denlats || exit 1;
+fi
+
+# Re-train the DNN by 2 iterations of sMBR 
+if [ $stage -le 6 ]; then
+  steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 2 --acwt $acwt --do-smbr true \
+    $train data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
+  # Decode
+  for ITER in 1 2; do
+    steps/nnet/decode.sh --nj 10 --cmd "$decode_cmd" \
+      --config conf/decode_dnn.config --acwt $acwt --nnet $dir/${ITER}.nnet \
+      $gmm/graph $dev $dir/decode_it${ITER} || exit 1;
+  done 
+fi
+
+# Re-generate lattices, run 4 more sMBR iterations
+dir=exp/cnn5c_pretrain-dbn_dnn_smbr_i1lats
+srcdir=exp/cnn5c_pretrain-dbn_dnn_smbr
+
+if [ $stage -le 7 ]; then
+  steps/nnet/align.sh --nj 10 --cmd "$train_cmd" \
+    $train data/lang $srcdir ${srcdir}_ali || exit 1;
+  steps/nnet/make_denlats.sh --nj 10 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt $acwt \
+    $train data/lang $srcdir ${srcdir}_denlats || exit 1;
+fi
+    
+if [ $stage -le 8 ]; then
+  steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 4 --acwt $acwt --do-smbr true \
+    $train data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
+  # Decode
+  for ITER in 1 2 3 4; do
+    steps/nnet/decode.sh --nj 10 --cmd "$decode_cmd" \
+      --config conf/decode_dnn.config --acwt $acwt --nnet $dir/${ITER}.nnet \
+      $gmm/graph $dev $dir/decode_it${ITER} || exit 1;
+  done 
+fi
+
+echo Success
+exit 0
+
+

egs/hkust/s5/local/nnet/run_dnn.sh

+#!/bin/bash
+
+# Copyright 2012-2014  Brno University of Technology (Author: Karel Vesely)
+#                2014  Guoguo Chen
+# Apache 2.0
+
+# This example script trains a DNN on top of fMLLR features. 
+# The training is done in 3 stages,
+#
+# 1) RBM pre-training:
+#    in this unsupervised stage we train stack of RBMs, 
+#    a good starting point for frame cross-entropy trainig.
+# 2) frame cross-entropy training:
+#    the objective is to classify frames to correct pdfs.
+# 3) sequence-training optimizing sMBR: 
+#    the objective is to emphasize state-sequences with better 
+#    frame accuracy w.r.t. reference alignment.
+
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+
+. ./path.sh ## Source the tools/utils (import the queue.pl)
+
+# Config:
+gmmdir=exp/tri5a
+data_fmllr=data-fmllr-tri5a
+stage=0 # resume training with --stage=N
+# End of config.
+. utils/parse_options.sh || exit 1;
+#
+
+if [ $stage -le 0 ]; then
+  # Store fMLLR features, so we can train on them easily,
+  # dev
+  dir=$data_fmllr/dev
+  steps/nnet/make_fmllr_feats.sh --nj 10 --cmd "$train_cmd" \
+     --transform-dir $gmmdir/decode \
+     $dir data/dev $gmmdir $dir/log $dir/data || exit 1
+  # train
+  dir=$data_fmllr/train
+  steps/nnet/make_fmllr_feats.sh --nj 10 --cmd "$train_cmd" \
+     --transform-dir ${gmmdir}_ali \
+     $dir data/train $gmmdir $dir/log $dir/data || exit 1
+  # split the data : 90% train 10% cross-validation (held-out)
+  utils/subset_data_dir_tr_cv.sh $dir ${dir}_tr90 ${dir}_cv10 || exit 1
+fi
+
+if [ $stage -le 1 ]; then
+  # Pre-train DBN, i.e. a stack of RBMs
+  dir=exp/dnn5b_pretrain-dbn
+  (tail --pid=$$ -F $dir/log/pretrain_dbn.log 2>/dev/null)& # forward log
+  $cuda_cmd $dir/log/pretrain_dbn.log \
+    steps/nnet/pretrain_dbn.sh --rbm-iter 1 --nn-depth 4 --hid-dim 2000 \
+    $data_fmllr/train $dir || exit 1;
+fi
+
+if [ $stage -le 2 ]; then
+  # Train the DNN optimizing per-frame cross-entropy.
+  dir=exp/dnn5b_pretrain-dbn_dnn
+  ali=${gmmdir}_ali
+  feature_transform=exp/dnn5b_pretrain-dbn/final.feature_transform
+  dbn=exp/dnn5b_pretrain-dbn/4.dbn
+  (tail --pid=$$ -F $dir/log/train_nnet.log 2>/dev/null)& # forward log
+  # Train
+  $cuda_cmd $dir/log/train_nnet.log \
+    steps/nnet/train.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 \
+    $data_fmllr/train_tr90 $data_fmllr/train_cv10 data/lang $ali $ali $dir || exit 1;
+  # Decode with the trigram language model.
+  steps/nnet/decode.sh --nj 10 --cmd "$decode_cmd" \
+    --config conf/decode_dnn.config --acwt 0.1 \
+    $gmmdir/graph $data_fmllr/dev \
+    $dir/decode || exit 1;
+fi
+
+# Sequence training using sMBR criterion, we do Stochastic-GD 
+# with per-utterance updates. We use usually good acwt 0.1
+# Lattices are re-generated after 1st epoch, to get faster convergence.
+dir=exp/dnn5b_pretrain-dbn_dnn_smbr
+srcdir=exp/dnn5b_pretrain-dbn_dnn
+acwt=0.1
+
+if [ $stage -le 3 ]; then
+  # First we generate lattices and alignments:
+  steps/nnet/align.sh --nj 10 --cmd "$train_cmd" \
+    $data_fmllr/train data/lang $srcdir ${srcdir}_ali || exit 1;
+  steps/nnet/make_denlats.sh --nj 10 --sub-split 20 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+    --acwt $acwt $data_fmllr/train data/lang $srcdir ${srcdir}_denlats || exit 1;
+fi
+
+if [ $stage -le 4 ]; then
+  # Re-train the DNN by 1 iteration of sMBR 
+  steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 1 --acwt $acwt --do-smbr true \
+    $data_fmllr/train data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
+  # Decode (reuse HCLG graph)
+  for ITER in 1; do
+    # Decode with the trigram swbd language model.
+    steps/nnet/decode.sh --nj 10 --cmd "$decode_cmd" \
+      --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      $gmmdir/graph $data_fmllr/dev \
+      $dir/decode || exit 1;
+  done 
+fi
+
+# Re-generate lattices, run 2 more sMBR iterations
+dir=exp/dnn5b_pretrain-dbn_dnn_smbr_i1lats
+srcdir=exp/dnn5b_pretrain-dbn_dnn_smbr
+acwt=0.0909
+
+if [ $stage -le 5 ]; then
+  # First we generate lattices and alignments:
+  #steps/nnet/align.sh --nj 10 --cmd "$train_cmd" \
+  #  $data_fmllr/train data/lang $srcdir ${srcdir}_ali || exit 1;
+  steps/nnet/make_denlats.sh --nj 10 --sub-split 20 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+    --acwt $acwt $data_fmllr/train data/lang $srcdir ${srcdir}_denlats || exit 1;
+fi
+
+if [ $stage -le 6 ]; then
+  # Re-train the DNN by 2 iteration of sMBR 
+  steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 2 --acwt $acwt --do-smbr true \
+    $data_fmllr/train data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
+  # Decode (reuse HCLG graph)
+  for ITER in 1 2; do
+    # Decode with the trigram language model.
+    steps/nnet/decode.sh --nj 10 --cmd "$decode_cmd" \
+      --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      $gmmdir/graph $data_fmllr/dev \
+      $dir/decode || exit 1;
+  done 
+fi
+
+# Getting results [see RESULTS file]
+# for x in exp/*/decode*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done

egs/hkust/s5/local/nnet/run_lstm.sh

+#!/bin/bash
+
+# Copyright 2015  Brno University of Technology (Author: Karel Vesely)
+# Apache 2.0
+
+# This example script trains a LSTM network on FBANK features.
+# The LSTM code comes from Yiayu DU, and Wei Li, thanks!
+
+. ./cmd.sh
+. ./path.sh
+
+dev=data_fbank/dev
+train=data_fbank/train
+
+dev_original=data/dev
+train_original=data/train
+
+gmm=exp/tri5a
+
+stage=0
+. utils/parse_options.sh || exit 1;
+
+# Make the FBANK features
+[ ! -e $dev ] && if [ $stage -le 0 ]; then
+  # Dev set
+  utils/copy_data_dir.sh $dev_original $dev || exit 1; rm $dev/{cmvn,feats}.scp
+  steps/make_fbank_pitch.sh --nj 10 --cmd "$train_cmd" \
+     $dev $dev/log $dev/data || exit 1;
+  steps/compute_cmvn_stats.sh $dev $dev/log $dev/data || exit 1;
+  # Training set
+  utils/copy_data_dir.sh $train_original $train || exit 1; rm $train/{cmvn,feats}.scp
+  steps/make_fbank_pitch.sh --nj 10 --cmd "$train_cmd" \
+     $train $train/log $train/data || exit 1;
+  steps/compute_cmvn_stats.sh $train $train/log $train/data || exit 1;
+
+  # Split the training set
+  utils/subset_data_dir_tr_cv.sh --cv-spk-percent 10 $train ${train}_tr90 ${train}_cv10
+fi
+
+if [ $stage -le 1 ]; then
+  # Train the DNN optimizing per-frame cross-entropy.
+  dir=exp/lstm5e
+  ali=${gmm}_ali
+
+  # Train
+  $cuda_cmd $dir/log/train_nnet.log \
+    steps/nnet/train.sh --network-type lstm --learn-rate 0.0001 \
+      --cmvn-opts "--norm-means=true --norm-vars=true" --feat-type plain --splice 0 \
+      --train-opts "--momentum 0.9 --halving-factor 0.5" \
+      --delta-opts "--delta-order=2" \
+      --train-tool "nnet-train-lstm-streams --num-stream=4 --targets-delay=5" \
+      --proto-opts "--num-cells 2000 --num-recurrent 750 --num-layers 1 --clip-gradient 5.0" \
+    ${train}_tr90 ${train}_cv10 data/lang $ali $ali $dir || exit 1;
+
+  # Decode with the trigram language model.
+  steps/nnet/decode.sh --nj 10 --cmd "$decode_cmd" \
+    --config conf/decode_dnn.config --acwt 0.1 \
+    $gmm/graph $dev $dir/decode || exit 1;
+fi
+
+
+# TODO : sequence training,
+
+echo Success
+exit 0
+
+# Getting results [see RESULTS file]
+# for x in exp/*/decode*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done

egs/hkust/s5/local/nnet2/run_5d.sh

+#!/bin/bash
+
+
+# This runs on the full training set (with duplicates removed), with p-norm
+# units, on top of fMLLR features, on GPU.
+
+temp_dir=
+dir=exp/nnet2_5d
+stage=-5
+
+. ./cmd.sh
+. ./path.sh
+
+. utils/parse_options.sh
+
+parallel_opts="--gpu 1"  # This is suitable for the CLSP network, you'll
+                          # likely have to change it.
+
+( 
+  if [ ! -f $dir/final.mdl ]; then
+    steps/nnet2/train_pnorm_accel2.sh --parallel-opts "$parallel_opts" \
+      --cmd "$decode_cmd" --stage $stage \
+      --num-threads 1 --minibatch-size 512 \
+      --mix-up 20000 --samples-per-iter 300000 \
+      --num-epochs 15 \
+      --initial-effective-lrate 0.005 --final-effective-lrate 0.0005 \
+      --num-jobs-initial 3 --num-jobs-final 8 --num-hidden-layers 4 --splice-width 5 \
+      --pnorm-input-dim 4000 --pnorm-output-dim 400 --p 2 \
+      data/train data/lang exp/tri5a_ali $dir || exit 1;
+  fi
+
+  steps/nnet2/decode.sh --cmd "$decode_cmd" --nj 10 \
+    --config conf/decode.config \
+    --transform-dir exp/tri5a/decode \
+    exp/tri5a/graph data/dev \
+    $dir/decode || exit 1;
+)

egs/hkust/s5/run.sh

@@ -145,6 +145,26 @@ steps/decode.sh --cmd "$decode_cmd" --nj 10 --config conf/decode.config \
   --transform-dir exp/tri5a/decode \
   exp/tri5a/graph data/dev exp/tri5a_mpe/decode || exit 1 ;
 
+# SGMM system [sgmm5a]
+steps/train_ubm.sh --cmd "$train_cmd" \
+  900 data/train data/lang exp/tri5a_ali exp/ubm5a || exit 1;
+
+steps/train_sgmm2.sh --cmd "$train_cmd" \
+  14000 35000 data/train data/lang exp/tri5a_ali \
+  exp/ubm5a/final.ubm exp/sgmm2_5a || exit 1;
+
+utils/mkgraph.sh data/lang_test exp/sgmm2_5a exp/sgmm2_5a/graph || exit 1;
+steps/decode_sgmm2.sh --nj 10 --cmd "$decode_cmd" --config conf/decode.config \
+  --transform-dir exp/tri5a/decode \
+  exp/sgmm2_5a/graph data/dev exp/sgmm2_5a/decode || exit 1;
+
+# nnet1 dnn
+local/nnet/run_dnn.sh
+
+# nnet2
+local/nnet2/run_5d.sh
+local/nnet2/run_convnet.sh
+
 # getting results (see RESULTS file)
 for x in exp/*/decode; do [ -d $x ] && grep Sum $x/score_*/*.sys | utils/best_wer.sh; done 2>/dev/null
 for x in exp/*/decode; do [ -d $x ] && grep WER $x/cer_* | utils/best_wer.sh; done 2>/dev/null