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egs/wsj/s5/local/rnnlm/tuning/run_lstm_tdnn_1a.sh
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#!/bin/bash
# Copyright 2012 Johns Hopkins University (author: Daniel Povey) Tony Robinson
# 2017 Hainan Xu
# 2017 Ke Li
# Begin configuration section.
dir=exp/rnnlm_lstm_tdnn_1a
embedding_dim=800
lstm_rpd=200
lstm_nrpd=200
epochs=20
stage=-10
train_stage=-10
. ./cmd.sh
. ./utils/parse_options.sh
[ -z "$cmd" ] && cmd=$train_cmd
text=data/local/dict_nosp_larger/cleaned.gz
wordlist=data/lang_nosp/words.txt
text_dir=data/rnnlm/text_nosp
mkdir -p $dir/config
set -e
for f in $text $wordlist; do
[ ! -f $f ] && \
echo "$0: expected file $f to exist; search for local/wsj_extend_dict.sh in run.sh" && exit 1
done
if [ $stage -le 0 ]; then
mkdir -p $text_dir
echo -n >$text_dir/dev.txt
# hold out one in every 500 lines as dev data.
gunzip -c $text | awk -v text_dir=$text_dir '{if(NR%500 == 0) { print >text_dir"/dev.txt"; } else {print;}}' >$text_dir/wsj.txt
fi
if [ $stage -le 1 ]; then
# the training scripts require that <s>, </s> and <brk> be present in a particular
# order.
cp $wordlist $dir/config/
n=`cat $dir/config/words.txt | wc -l`
echo "<brk> $n" >> $dir/config/words.txt
# words that are not present in words.txt but are in the training or dev data, will be
# mapped to <SPOKEN_NOISE> during training.
echo "<SPOKEN_NOISE>" >$dir/config/oov.txt
cat > $dir/config/data_weights.txt <<EOF
wsj 1 1.0
EOF
rnnlm/get_unigram_probs.py --vocab-file=$dir/config/words.txt \
--unk-word="<SPOKEN_NOISE>" \
--data-weights-file=$dir/config/data_weights.txt \
$text_dir | awk 'NF==2' >$dir/config/unigram_probs.txt
# choose features
rnnlm/choose_features.py --unigram-probs=$dir/config/unigram_probs.txt \
--use-constant-feature=true \
--top-word-features=50000 \
--min-frequency 1.0e-03 \
--special-words='<s>,</s>,<brk>,<SPOKEN_NOISE>' \
$dir/config/words.txt > $dir/config/features.txt
cat >$dir/config/xconfig <<EOF
input dim=$embedding_dim name=input
relu-renorm-layer name=tdnn1 dim=$embedding_dim input=Append(0, IfDefined(-1))
fast-lstmp-layer name=lstm1 cell-dim=$embedding_dim recurrent-projection-dim=$lstm_rpd non-recurrent-projection-dim=$lstm_nrpd
relu-renorm-layer name=tdnn2 dim=$embedding_dim input=Append(0, IfDefined(-2))
fast-lstmp-layer name=lstm2 cell-dim=$embedding_dim recurrent-projection-dim=$lstm_rpd non-recurrent-projection-dim=$lstm_nrpd
relu-renorm-layer name=tdnn3 dim=$embedding_dim input=Append(0, IfDefined(-1))
output-layer name=output include-log-softmax=false dim=$embedding_dim
EOF
rnnlm/validate_config_dir.sh $text_dir $dir/config
fi
if [ $stage -le 2 ]; then
# the --unigram-factor option is set larger than the default (100)
# in order to reduce the size of the sampling LM, because rnnlm-get-egs
# was taking up too much CPU (as much as 10 cores).
rnnlm/prepare_rnnlm_dir.sh --unigram-factor 200.0 \
$text_dir $dir/config $dir
fi
if [ $stage -le 3 ]; then
rnnlm/train_rnnlm.sh --num-jobs-initial 1 --num-jobs-final 3 \
--stage $train_stage --num-epochs $epochs --cmd "$cmd" $dir
fi
exit 0
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