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[egs] add rnnlm recipe for librispeech (#2830)
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egs/librispeech/s5/local/rnnlm/tuning/run_tdnn_lstm_1a.sh
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#!/bin/bash | ||
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# Copyright 2012 Johns Hopkins University (author: Daniel Povey) | ||
# 2018 Ke Li | ||
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# This script trains LMs on the librispeech 960 hours training data. | ||
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# rnnlm/train_rnnlm.sh: best iteration (out of 26) was 21, linking it to final iteration. | ||
# rnnlm/train_rnnlm.sh: train/dev perplexity was 118.4 / 152.6. | ||
# Train objf: -5.74 -5.51 -5.38 -5.29 -5.22 -5.16 -5.12 -5.08 -5.05 -5.02 -4.99 -4.97 -4.97 -4.93 -4.90 -4.87 -4.84 -4.82 -4.79 -4.77 -4.75 -4.73 -4.71 -4.69 -4.67 | ||
# Dev objf: -6.00 -5.61 -5.45 -5.36 -5.29 -5.24 -5.20 -5.18 -5.16 -5.13 -5.12 -5.11 -5.11 -5.09 -5.07 -5.06 -5.05 -5.04 -5.03 -5.03 -5.03 -5.03 -5.03 -5.03 -5.03 -5.03 | ||
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# WER summary on dev and test sets | ||
# System tdnn_1d_sp +lattice_rescore +nbest_rescore | ||
# WER on dev(fglarge) 3.34 2.97 2.98 | ||
# WER on dev(tglarge) 3.44 3.02 3.07 | ||
# WER on dev_other(fglarge) 8.70 7.98 8.00 | ||
# WER on dev_other(tglarge) 9.25 8.28 8.35 | ||
# WER on test(fglarge) 3.77 3.41 3.40 | ||
# WER on test(tglarge) 3.85 3.50 3.47 | ||
# WER on test_other(fglarge) 8.91 8.22 8.21 | ||
# WER on test_other(tglarge) 9.31 8.55 8.49 | ||
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# 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 | ||
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# Begin configuration section. | ||
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dir=exp/rnnlm_lstm_1a | ||
embedding_dim=1024 | ||
lstm_rpd=256 | ||
lstm_nrpd=256 | ||
stage=-10 | ||
train_stage=-10 | ||
epochs=20 | ||
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# 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 | ||
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. ./cmd.sh | ||
. ./utils/parse_options.sh | ||
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# test of 960 hours training transcriptions | ||
text=data/train_960/text | ||
lexicon=data/lang_nosp/words.txt | ||
text_dir=data/rnnlm/text_960_1a | ||
mkdir -p $dir/config | ||
set -e | ||
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for f in $text $lexicon; do | ||
[ ! -f $f ] && \ | ||
echo "$0: expected file $f to exist; search for run.sh in run.sh" && exit 1 | ||
done | ||
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if [ $stage -le 0 ]; then | ||
mkdir -p $text_dir | ||
echo -n >$text_dir/dev.txt | ||
# hold out one in every 50 lines as dev data. | ||
cat $text | cut -d ' ' -f2- | awk -v text_dir=$text_dir '{if(NR%50 == 0) { print >text_dir"/dev.txt"; } else {print;}}' >$text_dir/librispeech.txt | ||
fi | ||
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if [ $stage -le 1 ]; then | ||
cp $lexicon $dir/config/ | ||
n=`cat $dir/config/words.txt | wc -l` | ||
echo "<brk> $n" >> $dir/config/words.txt | ||
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# words that are not present in words.txt but are in the training or dev data, will be | ||
# mapped to <SPOKEN_NOISE> during training. | ||
echo "<UNK>" >$dir/config/oov.txt | ||
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cat > $dir/config/data_weights.txt <<EOF | ||
librispeech 1 1.0 | ||
EOF | ||
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rnnlm/get_unigram_probs.py --vocab-file=$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 | ||
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# 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 > $dir/config/features.txt | ||
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cat >$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 | ||
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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 | ||
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if [ $stage -le 3 ]; then | ||
rnnlm/train_rnnlm.sh --num-jobs-final 2 \ | ||
--stage $train_stage \ | ||
--num-epochs $epochs \ | ||
--cmd "$train_cmd" $dir | ||
fi | ||
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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 | ||
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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 | ||
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exit 0 |
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../../../scripts/rnnlm/ |