run_tdnn_lstm_1a.sh
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#!/bin/bash
# Copyright 2012 Johns Hopkins University (author: Daniel Povey)
# 2018 Ke Li
# This script trains LMs on the librispeech-lm-norm.txt.gz.
# rnnlm/train_rnnlm.sh: best iteration (out of 143) was 142, linking it to final iteration.
# rnnlm/train_rnnlm.sh: train/dev perplexity was 109.2 / 110.7.
# Train objf: -5.74 -5.54 -5.44 -5.37 -5.32 -5.28 -5.25 -5.23 -5.20 -5.18 -5.15 -5.14 -5.12 -5.10 -5.09 -5.08 -5.07 -5.05 -5.04 -5.04 -5.03 -5.02 -5.01 -5.00 -4.99 -4.99 -4.98 -4.97 -4.96 -4.96 -4.95 -4.95 -4.94 -4.93 -4.93 -4.92 -4.92 -4.92 -4.91 -4.90 -4.90 -4.89 -4.89 -4.89 -4.88 -4.88 -4.87 -4.87 -4.87 -4.86 -4.86 -4.86 -4.85 -4.85 -4.84 -4.84 -4.84 -4.84 -4.84 -4.83 -4.83 -4.83 -4.82 -4.82 -4.82 -4.82 -4.81 -4.81 -4.81 -4.81 -4.80 -4.80 -4.80 -4.79 -4.79 -4.79 -4.79 -4.78 -4.79 -4.78 -4.78 -4.78 -4.78 -4.77 -4.77 -4.77 -4.77 -4.77 -4.76 -4.76 -4.76 -4.76 -4.76 -4.75 -4.75 -4.75 -4.75 -4.75 -4.74 -4.74 -4.74 -4.74 -4.74 -4.74 -4.73 -4.74 -4.74 -4.73 -4.73 -4.73 -4.73 -4.73 -4.72 -4.73 -4.73 -4.73 -4.72 -4.72 -4.72 -4.72 -4.72 -4.72 -4.72 -4.72 -4.71 -4.71 -4.71 -4.71 -4.71 -4.70 -4.70 -4.70 -4.70 -4.70 -4.69 -4.69 -4.69 -4.69 -4.69 -4.69 -4.68 -4.68
# Dev objf: -5.99 -5.65 -5.53 -5.44 -5.38 -5.34 -5.30 -5.27 -5.22 -5.20 -5.18 -5.16 -5.14 -5.12 -5.11 -5.10 -5.09 -5.08 -5.07 -5.05 -5.04 -5.04 -5.03 -5.01 -5.00 -4.99 -4.99 -4.98 -4.97 -4.97 0.00 -4.96 -4.95 -4.95 -4.94 -4.93 -4.93 -4.92 -4.92 -4.91 -4.91 -4.90 -4.90 -4.89 -4.89 -4.89 -4.88 -4.88 -4.88 -4.87 -4.87 -4.87 -4.86 -4.86 -4.85 -4.85 -4.87 -4.84 -4.84 -4.84 -4.83 -4.91 -4.83 -4.83 -4.83 -4.82 -4.82 -4.82 -4.82 -4.81 -4.81 -4.81 -4.80 -4.80 -4.80 -4.80 -4.80 -4.79 -4.79 -4.79 -4.79 -4.79 -4.79 -4.78 -4.78 -4.79 -4.78 -4.77 -4.77 -4.77 -4.77 -4.77 -4.77 -4.77 -4.76 -4.76 -4.76 -4.76 -4.76 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.74 -4.74 -4.74 -4.74 -4.74 -4.74 -4.74 -4.73 -4.74 -4.73 -4.73 -4.73 -4.73 -4.73 -4.73 -4.72 -4.72 -4.72 -4.72 -4.72 -4.72 -4.72 -4.72 -4.71 -4.71 -4.71 -4.71 -4.71 -4.71 -4.71 -4.71
# WER summary on dev and test sets
# System tdnn_1d_sp +lattice_rescore +nbest_rescore
# WER on dev(fglarge) 3.34 2.71 2.62
# WER on dev(tglarge) 3.44 2.75 2.66
# WER on dev_other(fglarge) 8.70 7.37 7.55
# WER on dev_other(tglarge) 9.25 7.56 7.73
# WER on test(fglarge) 3.77 3.12 3.06
# WER on test(tglarge) 3.85 3.18 3.11
# WER on test_other(fglarge) 8.91 7.63 7.68
# WER on test_other(tglarge) 9.31 7.83 7.95
# command to get the WERs above:
# tdnn_1d_sp
# for test in dev_clean test_clean dev_other test_other; do for lm in fglarge tglarge; do grep WER exp/chain_cleaned/tdnn_1d_sp/decode_${test}_${lm}/wer* | best_wer.sh; done; done
# tdnn_1d_sp with lattice rescoring
# for test in dev_clean test_clean dev_other test_other; do for lm in fglarge tglarge; do grep WER exp/chain_cleaned/tdnn_1d_sp/decode_${test}_${lm}_rnnlm_1a_rescore/wer* | best_wer.sh; done; done
# tdnn_1d_sp with nbest rescoring
# for test in dev_clean test_clean dev_other test_other; do for lm in fglarge tglarge; do grep WER exp/chain_cleaned/tdnn_1d_sp/decode_${test}_${lm}_rnnlm_1a_nbest_rescore/wer* | best_wer.sh; done; done
# Begin configuration section.
dir=exp/rnnlm_lstm_1a
embedding_dim=1024
lstm_rpd=256
lstm_nrpd=256
stage=-10
train_stage=-10
epochs=4
# variables for lattice rescoring
run_lat_rescore=true
run_nbest_rescore=true
run_backward_rnnlm=false
ac_model_dir=exp/chain_cleaned/tdnn_1d_sp
decode_dir_suffix=rnnlm_1a
ngram_order=4 # approximate the lattice-rescoring by limiting the max-ngram-order
# if it's set, it merges histories in the lattice if they share
# the same ngram history and this prevents the lattice from
# exploding exponentially
pruned_rescore=true
. ./cmd.sh
. ./utils/parse_options.sh
text=data/local/lm/librispeech-lm-norm.txt.gz
lexicon=data/lang_nosp/words.txt
text_dir=data/rnnlm/text
mkdir -p $dir/config
set -e
for f in $lexicon; do
[ ! -f $f ] && \
echo "$0: expected file $f to exist; search for run.sh in run.sh" && exit 1
done
if [ $stage -le 0 ]; then
mkdir -p $text_dir
if [ ! -f $text ]; then
wget http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz -P data/local/lm
fi
echo -n >$text_dir/dev.txt
# hold out one in every 2000 lines as dev data.
gunzip -c $text | cut -d ' ' -f2- | awk -v text_dir=$text_dir '{if(NR%2000 == 0) { print >text_dir"/dev.txt"; } else {print;}}' >$text_dir/librispeech.txt
fi
if [ $stage -le 1 ]; then
cp $lexicon $dir/config/
n=`cat $dir/config/words.txt | wc -l`
echo "<brk> $n" >> $dir/config/words.txt
# words that are not present in words.txt but are in the training or dev data, will be
# mapped to <SPOKEN_NOISE> during training.
echo "<UNK>" >$dir/config/oov.txt
cat > $dir/config/data_weights.txt <<EOF
librispeech 1 1.0
EOF
rnnlm/get_unigram_probs.py --vocab-file=$dir/config/words.txt \
--unk-word="<UNK>" \
--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 \
--top-word-features=5000 \
--use-constant-feature=true \
--special-words='<s>,</s>,<brk>,<UNK>,<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(-3))
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(-3))
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 400 \
$text_dir $dir/config $dir
fi
if [ $stage -le 3 ]; then
rnnlm/train_rnnlm.sh --num-jobs-final 8 \
--stage $train_stage \
--num-epochs $epochs \
--cmd "$train_cmd" $dir
fi
if [ $stage -le 4 ] && $run_lat_rescore; then
echo "$0: Perform lattice-rescoring on $ac_model_dir"
# LM=tgsmall # if using the original 3-gram G.fst as old lm
pruned=
if $pruned_rescore; then
pruned=_pruned
fi
for decode_set in test_clean test_other dev_clean dev_other; do
for LM in fglarge tglarge; do
decode_dir=${ac_model_dir}/decode_${decode_set}_${LM}
# Lattice rescoring
rnnlm/lmrescore$pruned.sh \
--cmd "$decode_cmd --mem 8G" \
--weight 0.45 --max-ngram-order $ngram_order \
data/lang_test_$LM $dir \
data/${decode_set}_hires ${decode_dir} \
exp/chain_cleaned/tdnn_1d_sp/decode_${decode_set}_${LM}_${decode_dir_suffix}_rescore
done
done
fi
if [ $stage -le 5 ] && $run_nbest_rescore; then
echo "$0: Perform nbest-rescoring on $ac_model_dir"
for decode_set in test_clean test_other dev_clean dev_other; do
for LM in fglarge tglarge; do
decode_dir=${ac_model_dir}/decode_${decode_set}_${LM}
# Nbest rescoring
rnnlm/lmrescore_nbest.sh \
--cmd "$decode_cmd --mem 8G" --N 20 \
0.4 data/lang_test_$LM $dir \
data/${decode_set}_hires ${decode_dir} \
exp/chain_cleaned/tdnn_1d_sp/decode_${decode_set}_${LM}_${decode_dir_suffix}_nbest_rescore
done
done
fi
exit 0