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egs/wsj/s5/local/wsj_format_data.sh
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#!/bin/bash # Copyright 2012 Microsoft Corporation Johns Hopkins University (Author: Daniel Povey) # 2015 Guoguo Chen # Apache 2.0 # This script takes data prepared in a corpus-dependent way # in data/local/, and converts it into the "canonical" form, # in various subdirectories of data/, e.g. data/lang, data/lang_test_ug, # data/train_si284, data/train_si84, etc. # Don't bother doing train_si84 separately (although we have the file lists # in data/local/) because it's just the first 7138 utterances in train_si284. # We'll create train_si84 after doing the feature extraction. lang_suffix= echo "$0 $@" # Print the command line for logging . utils/parse_options.sh || exit 1; . ./path.sh || exit 1; echo "Preparing train and test data" srcdir=data/local/data lmdir=data/local/nist_lm tmpdir=data/local/lm_tmp lexicon=data/local/lang${lang_suffix}_tmp/lexiconp.txt mkdir -p $tmpdir for x in train_si284 test_eval92 test_eval93 test_dev93 test_eval92_5k test_eval93_5k test_dev93_5k dev_dt_05 dev_dt_20; do mkdir -p data/$x cp $srcdir/${x}_wav.scp data/$x/wav.scp || exit 1; cp $srcdir/$x.txt data/$x/text || exit 1; cp $srcdir/$x.spk2utt data/$x/spk2utt || exit 1; cp $srcdir/$x.utt2spk data/$x/utt2spk || exit 1; utils/filter_scp.pl data/$x/spk2utt $srcdir/spk2gender > data/$x/spk2gender || exit 1; done # Next, for each type of language model, create the corresponding FST # and the corresponding lang_test_* directory. echo Preparing language models for test for lm_suffix in bg tgpr tg bg_5k tgpr_5k tg_5k; do test=data/lang${lang_suffix}_test_${lm_suffix} mkdir -p $test cp -r data/lang${lang_suffix}/* $test || exit 1; gunzip -c $lmdir/lm_${lm_suffix}.arpa.gz | \ arpa2fst --disambig-symbol=#0 \ --read-symbol-table=$test/words.txt - $test/G.fst utils/validate_lang.pl --skip-determinization-check $test || exit 1; done echo "Succeeded in formatting data." rm -r $tmpdir |