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authorDaniel Povey2017-10-04 14:40:57 -0500
committerDaniel Povey2017-10-04 14:40:57 -0500
commit0f2df80141268206214c550ab1edc9888a09c6ac (patch)
tree9f2c1eecd0015d1a7ff5907aa2fea90ac1bb879f
parente082c17d4a8f8a791428ae4d9f7ceb776aef3f0b (diff)
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[egs] Updating WSJ TDNN example to use batchnorm instead of renorm.
l---------egs/wsj/s5/local/chain/run_tdnn.sh2
-rwxr-xr-xegs/wsj/s5/local/chain/tuning/run_tdnn_1c.sh337
2 files changed, 338 insertions, 1 deletions
diff --git a/egs/wsj/s5/local/chain/run_tdnn.sh b/egs/wsj/s5/local/chain/run_tdnn.sh
index 61f8f4991..d48449e28 120000
--- a/egs/wsj/s5/local/chain/run_tdnn.sh
+++ b/egs/wsj/s5/local/chain/run_tdnn.sh
@@ -1 +1 @@
tuning/run_tdnn_1b.sh \ No newline at end of file tuning/run_tdnn_1c.sh \ No newline at end of file
diff --git a/egs/wsj/s5/local/chain/tuning/run_tdnn_1c.sh b/egs/wsj/s5/local/chain/tuning/run_tdnn_1c.sh
new file mode 100755
index 000000000..7dc30ecf8
--- /dev/null
+++ b/egs/wsj/s5/local/chain/tuning/run_tdnn_1c.sh
@@ -0,0 +1,337 @@
1#!/bin/bash
2
3# 1c is as 1b but using batchnorm instead of renorm
4# 1b is as 1a but using --proportional-shrink=60.0
5
6# local/chain/compare_wer.sh exp/chain/tdnn1a_sp exp/chain/tdnn1b_sp
7# System tdnn1a_sp tdnn1b_sp
8#WER dev93 (tgpr) 7.87 7.24
9#WER dev93 (tg) 7.61 6.95
10#WER dev93 (big-dict,tgpr) 5.71 5.19
11#WER dev93 (big-dict,fg) 5.10 4.52
12#WER eval92 (tgpr) 5.23 5.09
13#WER eval92 (tg) 4.87 4.64
14#WER eval92 (big-dict,tgpr) 3.24 2.91
15#WER eval92 (big-dict,fg) 2.71 2.39
16# Final train prob -0.0414 -0.0570
17# Final valid prob -0.0634 -0.0680
18# Final train prob (xent) -0.8216 -0.9587
19# Final valid prob (xent) -0.9208 -1.0039
20
21
22# steps/info/chain_dir_info.pl exp/chain/tdnn1b_sp
23# exp/chain/tdnn1b_sp: num-iters=102 nj=2..5 num-params=7.6M dim=40+100->2889 combine=-0.066->-0.063 xent:train/valid[67,101,final]=(-1.12,-0.979,-0.959/-1.13,-1.03,-1.00) logprob:train/valid[67,101,final]=(-0.071,-0.058,-0.057/-0.077,-0.069,-0.068)
24
25set -e -o pipefail
26
27# First the options that are passed through to run_ivector_common.sh
28# (some of which are also used in this script directly).
29stage=0
30nj=30
31train_set=train_si284
32test_sets="test_dev93 test_eval92"
33gmm=tri4b # this is the source gmm-dir that we'll use for alignments; it
34 # should have alignments for the specified training data.
35num_threads_ubm=32
36nnet3_affix= # affix for exp dirs, e.g. it was _cleaned in tedlium.
37
38# Options which are not passed through to run_ivector_common.sh
39affix=1c #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
40common_egs_dir=
41reporting_email=
42
43# LSTM/chain options
44train_stage=-10
45xent_regularize=0.1
46
47# training chunk-options
48chunk_width=140,100,160
49# we don't need extra left/right context for TDNN systems.
50chunk_left_context=0
51chunk_right_context=0
52
53# training options
54srand=0
55remove_egs=true
56
57#decode options
58test_online_decoding=false # if true, it will run the last decoding stage.
59
60# End configuration section.
61echo "$0 $@" # Print the command line for logging
62
63
64. ./cmd.sh
65. ./path.sh
66. ./utils/parse_options.sh
67
68
69if ! cuda-compiled; then
70 cat <<EOF && exit 1
71This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
72If you want to use GPUs (and have them), go to src/, and configure and make on a machine
73where "nvcc" is installed.
74EOF
75fi
76
77local/nnet3/run_ivector_common.sh \
78 --stage $stage --nj $nj \
79 --train-set $train_set --gmm $gmm \
80 --num-threads-ubm $num_threads_ubm \
81 --nnet3-affix "$nnet3_affix"
82
83
84
85gmm_dir=exp/${gmm}
86ali_dir=exp/${gmm}_ali_${train_set}_sp
87lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
88dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
89train_data_dir=data/${train_set}_sp_hires
90train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
91lores_train_data_dir=data/${train_set}_sp
92
93# note: you don't necessarily have to change the treedir name
94# each time you do a new experiment-- only if you change the
95# configuration in a way that affects the tree.
96tree_dir=exp/chain${nnet3_affix}/tree_a_sp
97# the 'lang' directory is created by this script.
98# If you create such a directory with a non-standard topology
99# you should probably name it differently.
100lang=data/lang_chain
101
102for f in $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
103 $lores_train_data_dir/feats.scp $gmm_dir/final.mdl \
104 $ali_dir/ali.1.gz $gmm_dir/final.mdl; do
105 [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
106done
107
108
109if [ $stage -le 12 ]; then
110 echo "$0: creating lang directory $lang with chain-type topology"
111 # Create a version of the lang/ directory that has one state per phone in the
112 # topo file. [note, it really has two states.. the first one is only repeated
113 # once, the second one has zero or more repeats.]
114 if [ -d $lang ]; then
115 if [ $lang/L.fst -nt data/lang/L.fst ]; then
116 echo "$0: $lang already exists, not overwriting it; continuing"
117 else
118 echo "$0: $lang already exists and seems to be older than data/lang..."
119 echo " ... not sure what to do. Exiting."
120 exit 1;
121 fi
122 else
123 cp -r data/lang $lang
124 silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
125 nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
126 # Use our special topology... note that later on may have to tune this
127 # topology.
128 steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
129 fi
130fi
131
132if [ $stage -le 13 ]; then
133 # Get the alignments as lattices (gives the chain training more freedom).
134 # use the same num-jobs as the alignments
135 steps/align_fmllr_lats.sh --nj 100 --cmd "$train_cmd" ${lores_train_data_dir} \
136 data/lang $gmm_dir $lat_dir
137 rm $lat_dir/fsts.*.gz # save space
138fi
139
140if [ $stage -le 14 ]; then
141 # Build a tree using our new topology. We know we have alignments for the
142 # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
143 # those. The num-leaves is always somewhat less than the num-leaves from
144 # the GMM baseline.
145 if [ -f $tree_dir/final.mdl ]; then
146 echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
147 exit 1;
148 fi
149 steps/nnet3/chain/build_tree.sh \
150 --frame-subsampling-factor 3 \
151 --context-opts "--context-width=2 --central-position=1" \
152 --cmd "$train_cmd" 3500 ${lores_train_data_dir} \
153 $lang $ali_dir $tree_dir
154fi
155
156
157if [ $stage -le 15 ]; then
158 mkdir -p $dir
159 echo "$0: creating neural net configs using the xconfig parser";
160
161 num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
162 learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
163
164 mkdir -p $dir/configs
165 cat <<EOF > $dir/configs/network.xconfig
166 input dim=100 name=ivector
167 input dim=40 name=input
168
169 # please note that it is important to have input layer with the name=input
170 # as the layer immediately preceding the fixed-affine-layer to enable
171 # the use of short notation for the descriptor
172 fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
173
174 # the first splicing is moved before the lda layer, so no splicing here
175 relu-batchnorm-layer name=tdnn1 dim=512
176 relu-batchnorm-layer name=tdnn2 dim=512 input=Append(-1,0,1)
177 relu-batchnorm-layer name=tdnn3 dim=512 input=Append(-1,0,1)
178 relu-batchnorm-layer name=tdnn4 dim=512 input=Append(-3,0,3)
179 relu-batchnorm-layer name=tdnn5 dim=512 input=Append(-3,0,3)
180 relu-batchnorm-layer name=tdnn6 dim=512 input=Append(-6,-3,0)
181
182 ## adding the layers for chain branch
183 relu-batchnorm-layer name=prefinal-chain dim=512 target-rms=0.5
184 output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
185
186 # adding the layers for xent branch
187 # This block prints the configs for a separate output that will be
188 # trained with a cross-entropy objective in the 'chain' models... this
189 # has the effect of regularizing the hidden parts of the model. we use
190 # 0.5 / args.xent_regularize as the learning rate factor- the factor of
191 # 0.5 / args.xent_regularize is suitable as it means the xent
192 # final-layer learns at a rate independent of the regularization
193 # constant; and the 0.5 was tuned so as to make the relative progress
194 # similar in the xent and regular final layers.
195 relu-batchnorm-layer name=prefinal-xent input=tdnn6 dim=512 target-rms=0.5
196 output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
197EOF
198 steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
199fi
200
201
202if [ $stage -le 16 ]; then
203 if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
204 utils/create_split_dir.pl \
205 /export/b0{3,4,5,6}/$USER/kaldi-data/egs/wsj-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
206 fi
207
208 steps/nnet3/chain/train.py --stage=$train_stage \
209 --cmd="$decode_cmd" \
210 --feat.online-ivector-dir=$train_ivector_dir \
211 --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
212 --chain.xent-regularize $xent_regularize \
213 --chain.leaky-hmm-coefficient=0.1 \
214 --chain.l2-regularize=0.00005 \
215 --chain.apply-deriv-weights=false \
216 --chain.lm-opts="--num-extra-lm-states=2000" \
217 --trainer.srand=$srand \
218 --trainer.max-param-change=2.0 \
219 --trainer.num-epochs=4 \
220 --trainer.frames-per-iter=3000000 \
221 --trainer.optimization.num-jobs-initial=2 \
222 --trainer.optimization.num-jobs-final=5 \
223 --trainer.optimization.initial-effective-lrate=0.001 \
224 --trainer.optimization.final-effective-lrate=0.0001 \
225 --trainer.optimization.shrink-value=1.0 \
226 --trainer.optimization.proportional-shrink=60.0 \
227 --trainer.num-chunk-per-minibatch=256,128,64 \
228 --trainer.optimization.momentum=0.0 \
229 --egs.chunk-width=$chunk_width \
230 --egs.chunk-left-context=0 \
231 --egs.chunk-right-context=0 \
232 --egs.chunk-left-context-initial=0 \
233 --egs.chunk-right-context-final=0 \
234 --egs.dir="$common_egs_dir" \
235 --egs.opts="--frames-overlap-per-eg 0" \
236 --cleanup.remove-egs=$remove_egs \
237 --use-gpu=true \
238 --reporting.email="$reporting_email" \
239 --feat-dir=$train_data_dir \
240 --tree-dir=$tree_dir \
241 --lat-dir=$lat_dir \
242 --dir=$dir || exit 1;
243fi
244
245if [ $stage -le 17 ]; then
246 # The reason we are using data/lang here, instead of $lang, is just to
247 # emphasize that it's not actually important to give mkgraph.sh the
248 # lang directory with the matched topology (since it gets the
249 # topology file from the model). So you could give it a different
250 # lang directory, one that contained a wordlist and LM of your choice,
251 # as long as phones.txt was compatible.
252
253 utils/lang/check_phones_compatible.sh \
254 data/lang_test_tgpr/phones.txt $lang/phones.txt
255 utils/mkgraph.sh \
256 --self-loop-scale 1.0 data/lang_test_tgpr \
257 $tree_dir $tree_dir/graph_tgpr || exit 1;
258
259 utils/lang/check_phones_compatible.sh \
260 data/lang_test_bd_tgpr/phones.txt $lang/phones.txt
261 utils/mkgraph.sh \
262 --self-loop-scale 1.0 data/lang_test_bd_tgpr \
263 $tree_dir $tree_dir/graph_bd_tgpr || exit 1;
264fi
265
266if [ $stage -le 18 ]; then
267 frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
268 rm $dir/.error 2>/dev/null || true
269
270 for data in $test_sets; do
271 (
272 data_affix=$(echo $data | sed s/test_//)
273 nspk=$(wc -l <data/${data}_hires/spk2utt)
274 for lmtype in tgpr bd_tgpr; do
275 steps/nnet3/decode.sh \
276 --acwt 1.0 --post-decode-acwt 10.0 \
277 --extra-left-context 0 --extra-right-context 0 \
278 --extra-left-context-initial 0 \
279 --extra-right-context-final 0 \
280 --frames-per-chunk $frames_per_chunk \
281 --nj $nspk --cmd "$decode_cmd" --num-threads 4 \
282 --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
283 $tree_dir/graph_${lmtype} data/${data}_hires ${dir}/decode_${lmtype}_${data_affix} || exit 1
284 done
285 steps/lmrescore.sh \
286 --self-loop-scale 1.0 \
287 --cmd "$decode_cmd" data/lang_test_{tgpr,tg} \
288 data/${data}_hires ${dir}/decode_{tgpr,tg}_${data_affix} || exit 1
289 steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
290 data/lang_test_bd_{tgpr,fgconst} \
291 data/${data}_hires ${dir}/decode_${lmtype}_${data_affix}{,_fg} || exit 1
292 ) || touch $dir/.error &
293 done
294 wait
295 [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
296fi
297
298# Not testing the 'looped' decoding separately, because for
299# TDNN systems it would give exactly the same results as the
300# normal decoding.
301
302if $test_online_decoding && [ $stage -le 19 ]; then
303 # note: if the features change (e.g. you add pitch features), you will have to
304 # change the options of the following command line.
305 steps/online/nnet3/prepare_online_decoding.sh \
306 --mfcc-config conf/mfcc_hires.conf \
307 $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
308
309 rm $dir/.error 2>/dev/null || true
310
311 for data in $test_sets; do
312 (
313 data_affix=$(echo $data | sed s/test_//)
314 nspk=$(wc -l <data/${data}_hires/spk2utt)
315 # note: we just give it "data/${data}" as it only uses the wav.scp, the
316 # feature type does not matter.
317 for lmtype in tgpr bd_tgpr; do
318 steps/online/nnet3/decode.sh \
319 --acwt 1.0 --post-decode-acwt 10.0 \
320 --nj $nspk --cmd "$decode_cmd" \
321 $tree_dir/graph_${lmtype} data/${data} ${dir}_online/decode_${lmtype}_${data_affix} || exit 1
322 done
323 steps/lmrescore.sh \
324 --self-loop-scale 1.0 \
325 --cmd "$decode_cmd" data/lang_test_{tgpr,tg} \
326 data/${data}_hires ${dir}_online/decode_{tgpr,tg}_${data_affix} || exit 1
327 steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
328 data/lang_test_bd_{tgpr,fgconst} \
329 data/${data}_hires ${dir}_online/decode_${lmtype}_${data_affix}{,_fg} || exit 1
330 ) || touch $dir/.error &
331 done
332 wait
333 [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
334fi
335
336
337exit 0;