Update kaldi-run-chain.sh

added no ivector small model

data/src/speech/kaldi-run-chain.sh

@@ -469,3 +469,107 @@ if [ $stage -le 13 ]; then
 
 fi
 
+#
+# smaller model for embedded use without ivector
+#
+
+if [ $stage -le 11 ]; then
+
+    dir=exp/nnet3${nnet3_affix}/tdnn_250_no_ivector
+
+    mkdir -p $dir
+
+    echo
+    echo "$0: creating neural net configs using the xconfig parser";
+    echo
+
+    num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
+    learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+
+    mkdir -p $dir/configs
+    cat <<EOF > $dir/configs/network.xconfig
+#input dim=100 name=ivector
+input dim=40 name=input
+
+# please note that it is important to have input layer with the name=input
+# as the layer immediately preceding the fixed-affine-layer to enable
+# the use of short notation for the descriptor
+fixed-affine-layer name=lda input=Append(-1,0,1) affine-transform-file=$dir/configs/lda.mat
+
+# the first splicing is moved before the lda layer, so no splicing here
+relu-batchnorm-layer name=tdnn1 dim=250 self-repair-scale=1.0e-04
+relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=250
+relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1,2) dim=250
+relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=250
+relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=250
+relu-batchnorm-layer name=tdnn6 input=Append(-6,-3,0) dim=250
+
+## adding the layers for chain branch
+relu-batchnorm-layer name=prefinal-chain input=tdnn6 dim=250 target-rms=0.5
+output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
+
+# adding the layers for xent branch
+# This block prints the configs for a separate output that will be
+# trained with a cross-entropy objective in the 'chain' models... this
+# has the effect of regularizing the hidden parts of the model.  we use
+# 0.5 / args.xent_regularize as the learning rate factor- the factor of
+# 0.5 / args.xent_regularize is suitable as it means the xent
+# final-layer learns at a rate independent of the regularization
+# constant; and the 0.5 was tuned so as to make the relative progress
+# similar in the xent and regular final layers.
+relu-batchnorm-layer name=prefinal-xent input=tdnn6 dim=250 target-rms=0.5
+output-layer name=output-xent dim=$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/
+
+    echo
+    echo train.py 
+    echo
+
+    steps/nnet3/chain/train.py --stage $train_stage \
+      --cmd "$decode_cmd" \
+      --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
+      --chain.xent-regularize 0.1 \
+      --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 150 \
+      --trainer.num-chunk-per-minibatch 512 \
+      --trainer.frames-per-iter 1500000 \
+      --trainer.num-epochs 4 \
+      --trainer.optimization.proportional-shrink 20 \
+      --trainer.optimization.num-jobs-initial 1 \
+      --trainer.optimization.num-jobs-final 1 \
+      --trainer.optimization.initial-effective-lrate 0.001 \
+      --trainer.optimization.final-effective-lrate 0.0001 \
+      --trainer.max-param-change 2.0 \
+      --use-gpu wait \
+      --cleanup.remove-egs true \
+      --feat-dir $train_data_dir \
+      --tree-dir $tree_dir \
+      --lat-dir $lat_dir \
+      --dir $dir
+
+    echo
+    echo mkgraph
+    echo
+
+    utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test $dir $dir/graph
+
+    echo
+    echo decode
+    echo
+
+    steps/nnet3/decode.sh --num-threads 1 --nj $nDecodeJobs --cmd "$decode_cmd" \
+                          --acwt 1.0 --post-decode-acwt 10.0 \
+                          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_test_hires \
+                          --scoring-opts "--min-lmwt 5 " \
+                          $dir/graph data/test_hires $dir/decode_test || exit 1;
+
+    grep WER $dir/decode_test/scoring_kaldi/best_wer >>RESULTS.txt
+
+fi