Yannick Estève / ONTRAC-Kaldi

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egs/chime2/s5/local/reverb_wsj0_data_prep.sh 3.26 KB
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8dcb6dfcb   Yannick Estève   first commit 
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  #!/bin/bash
  set -e
  
  # Copyright 2009-2012  Microsoft Corporation  Johns Hopkins University (Author: Daniel Povey)
  # Apache 2.0.
  
  # This is modified from the script in standard Kaldi recipe to account
  # for the way the WSJ data is structured on the Edinburgh systems. 
  # - Arnab Ghoshal, 29/05/12
  
  if [ $# -ne 1 ]; then
    printf "
  USAGE: %s <corpus-directory>
  
  " `basename $0`
    echo "The argument should be a the top-level WSJ corpus directory."
    echo "It is assumed that there will be a 'wsj0' and a 'wsj1' subdirectory"
    echo "within the top-level corpus directory."
    exit 1;
  fi
  
  CORPUS=$1
  
  dir=`pwd`/data/local/data
  lmdir=`pwd`/data/local/nist_lm
  mkdir -p $dir $lmdir
  local=`pwd`/local
  utils=`pwd`/utils
  
  . ./path.sh # Needed for KALDI_ROOT
  export PATH=$PATH:$KALDI_ROOT/tools/irstlm/bin
  sph2pipe=$KALDI_ROOT/tools/sph2pipe_v2.5/sph2pipe
  if [ ! -x $sph2pipe ]; then
    echo "Could not find (or execute) the sph2pipe program at $sph2pipe";
    exit 1;
  fi
  
  cd $dir
  
  # reverb list for SI-84
  
  find $1/si_tr_s -name '*.wav' |  sort -u > train_si84_reverb.flist
  
  
  
  # Dev-set Hub 1,2 (503, 913 utterances)
  
  # Note: the ???'s below match WSJ and SI_DT, or wsj and si_dt.  
  # Sometimes this gets copied from the CD's with upcasing, don't know 
  # why (could be older versions of the disks).
  find $1/si_dt_20  -name '*.wav' | sort -u > dev_dt_20_reverb.flist
  find $1/si_dt_05  -name '*.wav' | sort -u > dev_dt_05_reverb.flist
  
  
  # Finding the transcript files:
  #find -L $CORPUS -iname '*.dot' > dot_files.flist
  if [ ! -e $dir/dot_files.flist ]; then
    echo "Could not find $dir/dot_files.flist files, first run clean_data_prep.sh";
    exit 1;
  fi
  
  # Convert the transcripts into our format (no normalization yet)
  # adding suffix to utt_id 
  # 1 for reverb condition
  for x in train_si84_reverb dev_dt_05_reverb dev_dt_20_reverb; do
    cat $x.flist | perl -e ' 
      while(<>) {
        m:^\S+/(\w+)\.wav$: || die "Bad line $_";
        $id = $1;
        $id =~ tr/A-Z/a-z/;
        print "$id $_"; 
      }
    ' | sort > ${x}_wav_tmp.scp
    cat ${x}_wav_tmp.scp | awk '{print $1}' \
      | $local/find_transcripts.pl dot_files.flist > ${x}_tmp.trans1
    cat ${x}_wav_tmp.scp | awk '{printf("%s1 %s
  ", $1, $2);}' > ${x}_wav.scp
    cat ${x}_tmp.trans1 | awk '{printf("%s1 ", $1); for(i=2;i<=NF;i++) printf("%s ", $i); printf("
  ");}' > ${x}.trans1
  done
  
  
  # Do some basic normalization steps.  At this point we don't remove OOVs--
  # that will be done inside the training scripts, as we'd like to make the
  # data-preparation stage independent of the specific lexicon used.
  noiseword="<NOISE>";
  for x in train_si84_reverb dev_dt_05_reverb dev_dt_20_reverb; do
    cat $x.trans1 | $local/normalize_transcript.pl $noiseword \
      | sort > $x.txt || exit 1;
  done
   
  # Create scp's with wav's. (the wv1 in the distribution is not really wav, it is sph.)
  #for x in train_si84_clean test_eval92_clean test_eval92_5k_clean dev_dt_05_clean dev_dt_20_clean; do
  #  awk '{printf("%s '$sph2pipe' -f wav %s |
  ", $1, $2);}' < ${x}_sph.scp \
  #    > ${x}_wav.scp
  #done
  
  # Make the utt2spk and spk2utt files.
  for x in train_si84_reverb dev_dt_05_reverb dev_dt_20_reverb; do
    cat ${x}_wav.scp | awk '{print $1}' \
      | perl -ane 'chop; m:^...:; print "$_ $&
  ";' > $x.utt2spk
    cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
  done
  
  echo "Data preparation succeeded"