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67 lines
2.3 KiB
67 lines
2.3 KiB
3 years ago
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#!/usr/bin/env bash
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# 2020 Author Jiayu DU
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# Apache 2.0
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# This script uses kenlm to estimate an arpa model from plain text,
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# it is a resort when you hit memory limit dealing with large corpus
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# kenlm estimates arpa using on-disk structure,
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# as long as you have big enough hard disk, memory shouldn't be a problem.
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# by default, kenlm use up to 50% of your local memory,
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# you can control this through -S option
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[ -f path.sh ] && . ./path.sh;
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kenlm_opts="" # e.g. "-o 4 -S 50% --prune 0 5 7 7"
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if [ $# != 4 ]; then
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echo "$0 <text> <kaldi_symbol_table> <working_dir> <arpa_name>"
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echo "e.g. $0 train.txt words.txt wdir 4gram"
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exit 1
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fi
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text=$1
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symbol_table=$2
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dir=$3
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arpa_name=$4
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if ! which lmplz >& /dev/null ; then
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echo "$0: cannot find training tool *lmplz*."
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echo "tools/extras/install_kenlm_query_only.sh installs kenlm at tools/kenlm"
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echo "it only supports runtime mode, to actually train an arpa using KenLM,"
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echo "you need a complete KenLM installation(depends on EIGEN and BOOST),"
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echo "follow KenLM's building instructions at (https://github.com/kpu/kenlm)"
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exit 1
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fi
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# the text should be properly pre-processed, e.g:
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# cleand, normalized and possibly word-segmented
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# get rid off irrelavent symbols
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grep -v '<eps>' $symbol_table \
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| grep -v '#0' \
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| grep -v '<unk>' | grep -v '<UNK>' \
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| grep -v '<s>' | grep -v '</s>' \
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| awk '{print $1}' \
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> $dir/ngram.vocab
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# To make sure that kenlm & kaldi have strictly the same vocabulary:
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# 1. feed vocabulary into kenlm via --limit_vocab_file
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# 2. cat vocabulary to training text, so each word at least appear once
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#
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# TL;DR reason:
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# Unlike SRILM's -limit-vocab, kenlm's --limit_vocab_file option
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# spcifies a *valid* set of vocabulary, whereas *valid but unseen*
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# words are discarded in final arpa.
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# So the trick is,
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# we explicitly add kaldi's vocab(one word per line) to training text,
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# making each word appear at least once.
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# kenlm never prunes unigram,
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# so this always generates consistent kenlm vocabuary as kaldi has.
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# The effect of this is like add-one smoothing to unigram counts,
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# shouldn't have significant impacts in practice.
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cat $dir/ngram.vocab $text \
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| lmplz $kenlm_opts --limit_vocab_file $dir/ngram.vocab \
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> $dir/${arpa_name}.arpa
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echo "$0: Done training arpa to: $dir/${arpa_name}.arpa"
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