rhubarb-lip-sync/lib/pocketsphinx-rev13216/regression/word_align.pl

298 lines
8.7 KiB
Perl

#!/usr/bin/perl -w
# word_align.pl - Calculate word error and accuracy for a recognition
# hypothesis file vs. a reference transcription
#
# Written by David Huggins-Daines <dhuggins@cs.cmu.edu> for Speech
# Recognition and Understanding 11-751, Carnegie Mellon University,
# October 2004.
use strict;
use Getopt::Long;
use Pod::Usage;
use vars qw($Verbose $IgnoreUttID);
my ($help,%hyphash);
GetOptions(
'help|?' => \$help,
'verbose|v' => \$Verbose,
'ignore-uttid|i' => \$IgnoreUttID,
) or pod2usage(1);
pod2usage(1) if $help;
pod2usage(2) unless @ARGV == 2;
my ($ref, $hyp) = @ARGV;
open HYP, "<$hyp" or die "Failed to open $hyp: $!";
while (defined(my $hyp_utt=<HYP>)){
my $hyp_uttid;
($hyp_utt, $hyp_uttid) = s3_magic_norm($hyp_utt);
$hyphash{$hyp_uttid} = "$hyp_utt ($hyp_uttid)";
}
close HYP;
open REF, "<$ref" or die "Failed to open $ref: $!";
open HYP, "<$hyp" or die "Failed to open $hyp: $!";
use constant INS => 1;
use constant DEL => 2;
use constant MATCH => 3;
use constant SUBST => 4;
use constant BIG_NUMBER => 1e50;
my ($total_words, $total_match, $total_cost);
my ($total_ins, $total_del, $total_subst);
while (defined(my $ref_utt = <REF>)) {
my $hyp_utt;
my $ref_uttid;
my $hyp_uttid;
($ref_utt,$ref_uttid)=s3_magic_norm($ref_utt);
if(defined $IgnoreUttID){
$hyp_utt = <HYP>;
die "UttID is ignored but file size mismatch between $ref and $hyp" unless defined($hyp_utt);
}else{
$hyp_utt=$hyphash{$ref_uttid};
die "UttID is not ignored but it could not found in any entries of the hypothesis file on line3 $. UTTID: $ref_uttid\n" unless defined($hyp_utt);
}
($hyp_utt,$hyp_uttid)=s3_magic_norm($hyp_utt);
if(! defined $IgnoreUttID){
die "Utterance ID mismatch on line $.: $ref_uttid != $hyp_uttid"
unless $ref_uttid eq $hyp_uttid;
}
# Split the text into an array of words
my @ref_words = split ' ', $ref_utt;
my @hyp_words = split ' ', $hyp_utt;
my (@align_matrix, @backtrace_matrix);
# Initialize the alignment and backtrace matrices
initialize(\@ref_words, \@hyp_words, \@align_matrix, \@backtrace_matrix);
# Do DP alignment maintaining backtrace pointers
my $cost = align(\@ref_words, \@hyp_words, \@align_matrix, \@backtrace_matrix);
# Find the backtrace
my ($alignment, $ins, $del, $subst, $match) = backtrace(\@ref_words, \@hyp_words,
\@align_matrix, \@backtrace_matrix);
# Format the alignment nicely
my ($ref_align, $hyp_align) = ("", "");
foreach (@$alignment) {
my ($ref, $hyp) = @$_;
my $width = 0;
# Capitalize errors (they already are...), lowercase matches
if (defined($ref) and defined($hyp) and $ref eq $hyp) {
$ref = lc $ref;
$hyp = lc $hyp;
}
# Replace deletions with ***
foreach ($ref, $hyp) { $_ = "***" unless defined $_ };
# Find the width of this column
foreach ($ref, $hyp) { $width = length if length > $width };
$width = 3 if $width < 3; # Make it long enough for ***
# Space out the words and concatenate them to the output
$ref_align .= sprintf("%-*s ", $width, $ref);
$hyp_align .= sprintf("%-*s ", $width, $hyp);
}
print "$ref_align ($ref_uttid)\n$hyp_align ($hyp_uttid)\n";
# Print out the word error and accuracy rates
my $error = @ref_words == 0 ? 1 : $cost/@ref_words;
my $acc = @ref_words == 0 ? 0 : $match/@ref_words;
printf("Words: %d Correct: %d Errors: %d Percent correct = %.2f%% Error = %.2f%% Accuracy = %.2f%%\n",
scalar(@ref_words), $match, $cost, $acc*100, $error*100, 100-$error*100);
print "Insertions: $ins Deletions: $del Substitutions: $subst\n";
$total_cost += $cost;
$total_match += $match;
$total_words += @ref_words;
$total_ins += $ins;
$total_del += $del;
$total_subst += $subst;
}
# Print out the total word error and accuracy rates
my $error = $total_cost/$total_words;
my $acc = $total_match/$total_words;
printf("TOTAL Words: %d Correct: %d Errors: %d\nTOTAL Percent correct = %.2f%% Error = %.2f%% Accuracy = %.2f%%\n",
$total_words, $total_match, $total_cost, $acc*100, $error*100, 100-$error*100);
print "TOTAL Insertions: $total_ins Deletions: $total_del Substitutions: $total_subst\n";
# This function normalizes a line of a match file.
sub s3_magic_norm{
my ($word)=@_;
# Remove line endings
chomp $word;
$word =~ s/\r$//;
# Normalize case
$word = uc $word;
# Remove filler words and context cues
$word =~ s/<[^>]+>//g;
$word =~ s/\+\+[^+]+\+\+//g;
$word =~ s/\+[^+]+\+//g;
# Remove alternative pronunciations
$word =~ s/\([1-9]\)//g;
# Remove class tags
$word =~ s/:\S+//g;
# This compute the uttid and remove it from a line.
$word =~ s/\(([^) ]+)[^)]*\)$// ;
# Split apart compound words and acronyms
$word =~ tr/_./ /;
return ($word,$1);
}
sub initialize {
my ($ref_words, $hyp_words, $align_matrix, $backtrace_matrix) = @_;
# All initial costs along the j axis are insertions
for (my $j = 0; $j <= @$hyp_words; ++$j) {
$$align_matrix[0][$j] = $j;
}
for (my $j = 0; $j <= @$hyp_words; ++$j) {
$$backtrace_matrix[0][$j] = INS;
}
# All initial costs along the i axis are deletions
for (my $i = 0; $i <= @$ref_words; ++$i) {
$$align_matrix[$i][0] = $i;
}
for (my $i = 0; $i <= @$ref_words; ++$i) {
$$backtrace_matrix[$i][0] = DEL;
}
}
sub align {
my ($ref_words, $hyp_words, $align_matrix, $backtrace_matrix) = @_;
for (my $i = 1; $i <= @$ref_words; ++$i) {
for (my $j = 1; $j <= @$hyp_words; ++$j) {
# Find insertion, deletion, substitution scores
my ($ins, $del, $subst);
# Cost of a substitution (0 if they are equal)
my $cost = $$ref_words[$i-1] ne $$hyp_words[$j-1];
# Find insertion, deletion, substitution costs
$ins = $$align_matrix[$i][$j-1] + 1;
$del = $$align_matrix[$i-1][$j] + 1;
$subst = $$align_matrix[$i-1][$j-1] + $cost;
print "Costs at $i $j: INS $ins DEL $del SUBST $subst\n" if $Verbose;
# Get the minimum one
my $min = BIG_NUMBER;
foreach ($ins, $del, $subst) {
if ($_ < $min) {
$min = $_;
}
}
$$align_matrix[$i][$j] = $min;
# If the costs are equal, prefer match or substitution
# (keep the path diagonal).
if ($min == $subst) {
print(($cost ? "SUBSTITUTION" : "MATCH"),
"($$ref_words[$i-1] <=> $$hyp_words[$j-1])\n") if $Verbose;
$$backtrace_matrix[$i][$j] = MATCH+$cost;
}
elsif ($min == $ins) {
print "INSERTION (0 => $$hyp_words[$j-1])\n" if $Verbose;
$$backtrace_matrix[$i][$j] = INS;
}
elsif ($min == $del) {
print "DELETION ($$ref_words[$i-1] => 0)\n" if $Verbose;
$$backtrace_matrix[$i][$j] = DEL;
}
}
}
return $$align_matrix[@$ref_words][@$hyp_words];
}
sub backtrace {
my ($ref_words, $hyp_words, $align_matrix, $backtrace_matrix) = @_;
# Backtrace to find number of ins/del/subst
my @alignment;
my $i = @$ref_words;
my $j = @$hyp_words;
my ($inspen, $delpen, $substpen, $match) = (0,0,0,0);
while (!($i == 0 and $j == 0)) {
my $pointer = $$backtrace_matrix[$i][$j];
print "Cost at $i $j: $$align_matrix[$i][$j]\n"
if $Verbose;
if ($pointer == INS) {
print "INSERTION (0 => $$hyp_words[$j-1])" if $Verbose;
# Append the pair 0:hyp[j] to the front of the alignment
unshift @alignment, [undef, $$hyp_words[$j-1]];
++$inspen;
--$j;
print " - moving to $i $j\n" if $Verbose;
}
elsif ($pointer == DEL) {
print "DELETION ($$ref_words[$i-1] => 0)" if $Verbose;
# Append the pair ref[i]:0 to the front of the alignment
unshift @alignment, [$$ref_words[$i-1], undef];
++$delpen;
--$i;
print " - moving to $i $j\n" if $Verbose;
}
elsif ($pointer == MATCH) {
print "MATCH ($$ref_words[$i-1] <=> $$hyp_words[$j-1])" if $Verbose;
# Append the pair ref[i]:hyp[j] to the front of the alignment
unshift @alignment, [$$ref_words[$i-1], $$hyp_words[$j-1]];
++$match;
--$j;
--$i;
print " - moving to $i $j\n" if $Verbose;
}
elsif ($pointer == SUBST) {
print "SUBSTITUTION ($$ref_words[$i-1] <=> $$hyp_words[$j-1])" if $Verbose;
# Append the pair ref[i]:hyp[j] to the front of the alignment
unshift @alignment, [$$ref_words[$i-1], $$hyp_words[$j-1]];
++$substpen;
--$j;
--$i;
print " - moving to $i $j\n" if $Verbose;
}
else {
last;
}
}
return (\@alignment, $inspen, $delpen, $substpen, $match);
}
__END__
=head1 NAME
calculate_wer - Calculate Word Error Rate from a reference and hypothesis file
=head1 SYNOPSIS
calculate_wer [options] reference_file hypothesis_file
=head1 OPTIONS
=over 8
=item B<--help>, B<-?>
Print a brief help message and exit.
=item B<--verbose>, B<-v>
Print out messages tracing the alignment algorithm.
=cut