You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/examples/timit/asr1/local/timit_data_prep.sh

95 lines
3.4 KiB

#!/usr/bin/env bash
# Copyright 2013 (Authors: Bagher BabaAli, Daniel Povey, Arnab Ghoshal)
# 2014 Brno University of Technology (Author: Karel Vesely)
# Apache 2.0.
if [ $# -ne 1 ]; then
echo "Argument should be the Timit directory, see ../run.sh for example."
exit 1;
fi
dir=`pwd`/data/local
mkdir -p $dir
local=`pwd`/local
utils=`pwd`/utils
conf=`pwd`/conf
function error_exit () {
echo -e "$@" >&2; exit 1;
}
PROG=$(basename $0)
[ -f $conf/test_spk.list ] || error_exit "$PROG line $LINENO: Eval-set speaker list not found.";
[ -f $conf/dev_spk.list ] || error_exit "$PROG line $LINENO: dev-set speaker list not found.";
# First check if the train & test directories exist (these can either be upper-
# or lower-cased
if [ ! -d $*/TRAIN -o ! -d $*/TEST ] && [ ! -d $*/train -o ! -d $*/test ]; then
echo "timit_data_prep.sh: Spot check of command line argument failed"
echo "Command line argument must be absolute pathname to TIMIT directory"
echo "with name like /export/corpora5/LDC/LDC93S1/timit/TIMIT"
exit 1;
fi
# Now check what case the directory structure is
uppercased=false
train_dir=train
test_dir=test
if [ -d $*/TRAIN ]; then
uppercased=true
train_dir=TRAIN
test_dir=TEST
fi
tmpdir=$(mktemp -d /tmp/kaldi.XXXX);
trap 'rm -rf "$tmpdir"' EXIT
# Get the list of speakers. The list of speakers in the 24-speaker core test
# set and the 50-speaker development set must be supplied to the script. All
# speakers in the 'train' directory are used for training.
if $uppercased; then
tr '[:lower:]' '[:upper:]' < $conf/dev_spk.list > $tmpdir/dev_spk
tr '[:lower:]' '[:upper:]' < $conf/test_spk.list > $tmpdir/test_spk
ls -d "$*"/TRAIN/DR*/* | sed -e "s:^.*/::" > $tmpdir/train_spk
else
tr '[:upper:]' '[:lower:]' < $conf/dev_spk.list > $tmpdir/dev_spk
tr '[:upper:]' '[:lower:]' < $conf/test_spk.list > $tmpdir/test_spk
ls -d "$*"/train/dr*/* | sed -e "s:^.*/::" > $tmpdir/train_spk
fi
cd $dir
for x in train dev test; do
# First, find the list of audio files (use only si & sx utterances).
# Note: train & test sets are under different directories, but doing find on
# both and grepping for the speakers will work correctly.
find $*/{$train_dir,$test_dir} -not \( -iname 'SA*' \) -iname '*.WAV' \
| grep -f $tmpdir/${x}_spk > ${x}_sph.flist
sed -e 's:.*/\(.*\)/\(.*\).\(WAV\|wav\)$:\1_\2:' ${x}_sph.flist \
> $tmpdir/${x}_sph.uttids
paste $tmpdir/${x}_sph.uttids ${x}_sph.flist \
| sort -k1,1 > ${x}_sph.scp
cat ${x}_sph.scp | awk '{print $1}' > ${x}.uttids
# Now, Convert the transcripts into our format (no normalization yet)
# Get the transcripts: each line of the output contains an utterance
# ID followed by the transcript.
find $*/{$train_dir,$test_dir} -not \( -iname 'SA*' \) -iname '*.PHN' \
| grep -f $tmpdir/${x}_spk > $tmpdir/${x}_phn.flist
sed -e 's:.*/\(.*\)/\(.*\).\(PHN\|phn\)$:\1_\2:' $tmpdir/${x}_phn.flist \
> $tmpdir/${x}_phn.uttids
while read line; do
[ -f $line ] || error_exit "Cannot find transcription file '$line'";
cut -f3 -d' ' "$line" | tr '\n' ' ' | perl -ape 's: *$:\n:;'
done < $tmpdir/${x}_phn.flist > $tmpdir/${x}_phn.trans
paste $tmpdir/${x}_phn.uttids $tmpdir/${x}_phn.trans \
| sort -k1,1 > ${x}.trans
# Do normalization steps.
cat ${x}.trans | $local/timit_norm_trans.pl -i - -m $conf/phones.60-48-39.map -to 39 | sort > $x.text || exit 1;
done
echo "Data preparation succeeded"