зеркало из https://github.com/mozilla/kaldi.git
adding chime wsj eg
git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@3291 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8
This commit is contained in:
Chao Weng
Родитель
57fe6cc8ee
Коммит
94c4646aba
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# "queue.pl" uses qsub. The options to it are
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# options to qsub. If you have GridEngine installed,
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# change this to a queue you have access to.
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# Otherwise, use "run.pl", which will run jobs locally
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# (make sure your --num-jobs options are no more than
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# the number of cpus on your machine.
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#a) JHU cluster options
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export train_cmd="queue.pl -l arch=*64"
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export decode_cmd="queue.pl -l arch=*64,mem_free=2G,ram_free=2G"
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export mkgraph_cmd="queue.pl -l arch=*64,ram_free=4G,mem_free=4G"
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#export cuda_cmd="..."
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#b) BUT cluster options
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#export train_cmd="queue.pl -q all.q@@blade -l ram_free=1200M,mem_free=1200M"
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#export decode_cmd="queue.pl -q all.q@@blade -l ram_free=1700M,mem_free=1700M"
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#export decodebig_cmd="queue.pl -q all.q@@blade -l ram_free=4G,mem_free=4G"
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#export cuda_cmd="queue.pl -q long.q@@pco203 -l gpu=1"
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#export cuda_cmd="queue.pl -q long.q@pcspeech-gpu"
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#export mkgraph_cmd="queue.pl -q all.q@@servers -l ram_free=4G,mem_free=4G"
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#c) run it locally...
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#export train_cmd=run.pl
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#export decode_cmd=run.pl
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export cuda_cmd=run.pl
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#export mkgraph_cmd=run.pl
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beam=18.0 # beam for decoding. Was 13.0 in the scripts.
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latbeam=10.0 # this has most effect on size of the lattices.
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# No non-default options for now.
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--window-type=hamming # disable Dans window, use the standard
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--use-energy=false # only fbank outputs
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--sample-frequency=16000 # Cantonese is sampled at 8kHz
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--low-freq=64 # typical setup from Frantisek Grezl
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--high-freq=8000
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--dither=1
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--num-mel-bins=40 # 8kHz so we use 15 bins
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--htk-compat=true # try to make it compatible with HTK
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@ -0,0 +1 @@
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--use-energy=false # only non-default option.
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#!/bin/bash
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set -e
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# Copyright 2009-2012 Microsoft Corporation Johns Hopkins University (Author: Daniel Povey)
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# Apache 2.0.
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# This is modified from the script in standard Kaldi recipe to account
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# for the way the WSJ data is structured on the Edinburgh systems.
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# - Arnab Ghoshal, 29/05/12
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if [ $# -ne 1 ]; then
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printf "\nUSAGE: %s <corpus-directory>\n\n" `basename $0`
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echo "The argument should be a the top-level WSJ corpus directory."
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echo "It is assumed that there will be a 'wsj0' and a 'wsj1' subdirectory"
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echo "within the top-level corpus directory."
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exit 1;
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fi
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CORPUS=$1
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dir=`pwd`/data/local/data
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lmdir=`pwd`/data/local/nist_lm
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mkdir -p $dir $lmdir
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local=`pwd`/local
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utils=`pwd`/utils
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. ./path.sh # Needed for KALDI_ROOT
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export PATH=$PATH:$KALDI_ROOT/tools/irstlm/bin
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sph2pipe=$KALDI_ROOT/tools/sph2pipe_v2.5/sph2pipe
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if [ ! -x $sph2pipe ]; then
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echo "Could not find (or execute) the sph2pipe program at $sph2pipe";
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exit 1;
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fi
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cd $dir
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# reverb list for SI-84
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find $1/si_tr_s -name '*.wav' | sort -u > train_si84_binmask.flist
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# Dev-set Hub 1,2 (503, 913 utterances)
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# Note: the ???'s below match WSJ and SI_DT, or wsj and si_dt.
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# Sometimes this gets copied from the CD's with upcasing, don't know
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# why (could be older versions of the disks).
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find $1/si_dt_05 -name '*.wav' | sort -u > dev_dt_05_binmask.flist
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find $1/si_et_05 -name '*.wav' | sort -u > test_eval92_5k_binmask.flist
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# Finding the transcript files:
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#find -L $CORPUS -iname '*.dot' > dot_files.flist
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if [ ! -e $dir/dot_files.flist ]; then
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echo "Could not find $dir/dot_files.flist files, first run clean_data_prep.sh";
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exit 1;
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fi
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# Convert the transcripts into our format (no normalization yet)
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# adding suffix to utt_id
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# 1 for reverb condition
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for x in train_si84_binmask dev_dt_05_binmask test_eval92_5k_binmask; do
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cat $x.flist | perl -e '
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while(<>) {
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m:^\S+/(\w+)\.wav$: || die "Bad line $_";
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$id = $1;
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$id =~ tr/A-Z/a-z/;
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print "$id $_";
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}
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' | sort > ${x}_wav_tmp.scp
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#cat ${x}_wav_tmp.scp | awk '{print $1}' \
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# | $local/find_transcripts.pl dot_files.flist > ${x}_tmp.trans1
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cat ${x}_wav_tmp.scp | perl -e '
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while(<STDIN>) {
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@A=split(" ", $_);
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@B=split("/", $_);
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$abs_path_len=@B;
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$condition=$B[$abs_path_len-3];
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if ($condition eq "9dB") {$key_suffix=8;}
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elsif ($condition eq "6dB") {$key_suffix=9;}
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elsif ($condition eq "3dB") {$key_suffix=a;}
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elsif ($condition eq "0dB") {$key_suffix=b;}
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elsif ($condition eq "m3dB") {$key_suffix=c;}
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elsif ($condition eq "m6dB") {$key_suffix=d;}
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else {print STDERR "error condition $condition";}
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print $A[0].$key_suffix." ".$A[1]."\n";
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}
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' | sort -k1 > ${x}_wav.scp
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cat ${x}_wav.scp | awk '{print $1}' \
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| $local/find_noisy_transcripts.pl dot_files.flist > ${x}.trans1
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done
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# Do some basic normalization steps. At this point we don't remove OOVs--
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# that will be done inside the training scripts, as we'd like to make the
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# data-preparation stage independent of the specific lexicon used.
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noiseword="<NOISE>";
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for x in train_si84_binmask dev_dt_05_binmask test_eval92_5k_binmask; do
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cat $x.trans1 | $local/normalize_transcript.pl $noiseword \
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| sort > $x.txt || exit 1;
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done
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# Create scp's with wav's. (the wv1 in the distribution is not really wav, it is sph.)
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#for x in train_si84_clean test_eval92_clean test_eval92_5k_clean dev_dt_05_clean dev_dt_20_clean; do
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# awk '{printf("%s '$sph2pipe' -f wav %s |\n", $1, $2);}' < ${x}_sph.scp \
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# > ${x}_wav.scp
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#done
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# Make the utt2spk and spk2utt files.
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for x in train_si84_binmask dev_dt_05_binmask test_eval92_5k_binmask; do
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cat ${x}_wav.scp | awk '{print $1}' \
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| perl -ane 'chop; m:^...:; print "$_ $&\n";' > $x.utt2spk
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cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
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done
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echo "Data preparation succeeded"
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@ -0,0 +1,86 @@
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#!/bin/bash
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# Copyright 2012 Microsoft Corporation Johns Hopkins University (Author: Daniel Povey)
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# Apache 2.0
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# This script takes data prepared in a corpus-dependent way
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# in data/local/, and converts it into the "canonical" form,
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# in various subdirectories of data/, e.g. data/lang, data/lang_test_ug,
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# data/train_si284, data/train_si84, etc.
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# Don't bother doing train_si84 separately (although we have the file lists
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# in data/local/) because it's just the first 7138 utterances in train_si284.
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# We'll create train_si84 after doing the feature extraction.
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. ./path.sh || exit 1;
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echo "Preparing train and test data"
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srcdir=data/local/data
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lmdir=data/local/nist_lm
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tmpdir=data/local/lm_tmp
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lexicon=data/local/lang_tmp/lexiconp.txt
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mkdir -p $tmpdir
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for x in test_eval92_clean test_eval92_noisy test_eval92_5k_clean test_eval92_5k_noisy dev_dt_05_clean dev_dt_05_reverb dev_dt_05_noisy dev_dt_20_clean dev_dt_20_reverb dev_dt_20_noisy train_si84_clean train_si84_reverb train_si84_noisy; do
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mkdir -p data/$x
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cp $srcdir/${x}_wav.scp data/$x/wav.scp || exit 1;
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cp $srcdir/$x.txt data/$x/text || exit 1;
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cp $srcdir/$x.spk2utt data/$x/spk2utt || exit 1;
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cp $srcdir/$x.utt2spk data/$x/utt2spk || exit 1;
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utils/filter_scp.pl data/$x/spk2utt $srcdir/spk2gender > data/$x/spk2gender || exit 1;
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done
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# Next, for each type of language model, create the corresponding FST
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# and the corresponding lang_test_* directory.
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echo Preparing language models for test
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for lm_suffix in bg tgpr tg bg_5k tgpr_5k tg_5k; do
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test=data/lang_test_${lm_suffix}
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mkdir -p $test
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for f in phones.txt words.txt phones.txt L.fst L_disambig.fst \
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phones/; do
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cp -r data/lang/$f $test
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done
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gunzip -c $lmdir/lm_${lm_suffix}.arpa.gz | \
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utils/find_arpa_oovs.pl $test/words.txt > $tmpdir/oovs_${lm_suffix}.txt
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# grep -v '<s> <s>' because the LM seems to have some strange and useless
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# stuff in it with multiple <s>'s in the history. Encountered some other similar
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# things in a LM from Geoff. Removing all "illegal" combinations of <s> and </s>,
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# which are supposed to occur only at being/end of utt. These can cause
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# determinization failures of CLG [ends up being epsilon cycles].
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gunzip -c $lmdir/lm_${lm_suffix}.arpa.gz | \
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grep -v '<s> <s>' | \
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grep -v '</s> <s>' | \
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grep -v '</s> </s>' | \
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arpa2fst - | fstprint | \
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utils/remove_oovs.pl $tmpdir/oovs_${lm_suffix}.txt | \
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utils/eps2disambig.pl | utils/s2eps.pl | fstcompile --isymbols=$test/words.txt \
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--osymbols=$test/words.txt --keep_isymbols=false --keep_osymbols=false | \
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fstrmepsilon > $test/G.fst
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fstisstochastic $test/G.fst
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# The output is like:
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# 9.14233e-05 -0.259833
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# we do expect the first of these 2 numbers to be close to zero (the second is
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# nonzero because the backoff weights make the states sum to >1).
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# Because of the <s> fiasco for these particular LMs, the first number is not
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# as close to zero as it could be.
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# Everything below is only for diagnostic.
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# Checking that G has no cycles with empty words on them (e.g. <s>, </s>);
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# this might cause determinization failure of CLG.
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# #0 is treated as an empty word.
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mkdir -p $tmpdir/g
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awk '{if(NF==1){ printf("0 0 %s %s\n", $1,$1); }} END{print "0 0 #0 #0"; print "0";}' \
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< "$lexicon" >$tmpdir/g/select_empty.fst.txt
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fstcompile --isymbols=$test/words.txt --osymbols=$test/words.txt $tmpdir/g/select_empty.fst.txt | \
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fstarcsort --sort_type=olabel | fstcompose - $test/G.fst > $tmpdir/g/empty_words.fst
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fstinfo $tmpdir/g/empty_words.fst | grep cyclic | grep -w 'y' &&
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echo "Language model has cycles with empty words" && exit 1
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rm -r $tmpdir/g
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done
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echo "Succeeded in formatting data."
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rm -r $tmpdir
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@ -0,0 +1,190 @@
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#!/bin/bash
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set -e
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# Copyright 2009-2012 Microsoft Corporation Johns Hopkins University (Author: Daniel Povey)
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# Apache 2.0.
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# This is modified from the script in standard Kaldi recipe to account
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# for the way the WSJ data is structured on the Edinburgh systems.
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# - Arnab Ghoshal, 29/05/12
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|
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if [ $# -ne 1 ]; then
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printf "\nUSAGE: %s <corpus-directory>\n\n" `basename $0`
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echo "The argument should be a the top-level WSJ corpus directory."
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echo "It is assumed that there will be a 'wsj0' and a 'wsj1' subdirectory"
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echo "within the top-level corpus directory."
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exit 1;
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fi
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CORPUS=$1
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dir=`pwd`/data/local/data
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lmdir=`pwd`/data/local/nist_lm
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mkdir -p $dir $lmdir
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local=`pwd`/local
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utils=`pwd`/utils
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. ./path.sh # Needed for KALDI_ROOT
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export PATH=$PATH:$KALDI_ROOT/tools/irstlm/bin
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sph2pipe=$KALDI_ROOT/tools/sph2pipe_v2.5/sph2pipe
|
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if [ ! -x $sph2pipe ]; then
|
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echo "Could not find (or execute) the sph2pipe program at $sph2pipe";
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exit 1;
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fi
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cd $dir
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# This version for SI-84
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cat $CORPUS/wsj0/doc/indices/train/tr_s_wv1.ndx \
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| $local/cstr_ndx2flist.pl $CORPUS | sort -u > train_si84_clean.flist
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# This version for SI-284
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#cat $CORPUS/wsj1/doc/indices/si_tr_s.ndx \
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# $CORPUS/wsj0/doc/indices/train/tr_s_wv1.ndx \
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# | $local/cstr_ndx2flist.pl $CORPUS | sort \
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# | grep -v wsj0/si_tr_s/401 > train_si284.flist
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# Now for the test sets.
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# $CORPUS/wsj1/doc/indices/readme.doc
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# describes all the different test sets.
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# Note: each test-set seems to come in multiple versions depending
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# on different vocabulary sizes, verbalized vs. non-verbalized
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# pronunciations, etc. We use the largest vocab and non-verbalized
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# pronunciations.
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# The most normal one seems to be the "baseline 60k test set", which
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# is h1_p0.
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# Nov'92 (333 utts)
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# These index files have a slightly different format;
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||||
# have to add .wv1, which is done in cstr_ndx2flist.pl
|
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cat $CORPUS/wsj0/doc/indices/test/nvp/si_et_20.ndx | \
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$local/cstr_ndx2flist.pl $CORPUS | sort > test_eval92_clean.flist
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||||
# Nov'92 (330 utts, 5k vocab)
|
||||
cat $CORPUS/wsj0/doc/indices/test/nvp/si_et_05.ndx | \
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$local/cstr_ndx2flist.pl $CORPUS | sort > test_eval92_5k_clean.flist
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||||
|
||||
# Nov'93: (213 utts)
|
||||
# Have to replace a wrong disk-id.
|
||||
#cat $CORPUS/wsj1/doc/indices/wsj1/eval/h1_p0.ndx | \
|
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# $local/cstr_ndx2flist.pl $CORPUS | sort > test_eval93.flist
|
||||
|
||||
# Nov'93: (215 utts, 5k)
|
||||
#cat $CORPUS/wsj1/doc/indices/wsj1/eval/h2_p0.ndx | \
|
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# $local/cstr_ndx2flist.pl $CORPUS | sort > test_eval93_5k.flist
|
||||
|
||||
# Dev-set for Nov'93 (503 utts)
|
||||
#cat $CORPUS/wsj1/doc/indices/h1_p0.ndx | \
|
||||
# $local/cstr_ndx2flist.pl $CORPUS | sort > test_dev93.flist
|
||||
|
||||
# Dev-set for Nov'93 (513 utts, 5k vocab)
|
||||
#cat $CORPUS/wsj1/doc/indices/h2_p0.ndx | \
|
||||
# $local/cstr_ndx2flist.pl $CORPUS | sort > test_dev93_5k.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 $CORPUS/wsj0/si_dt_20 -print | grep -i ".wv1" | sort > dev_dt_20_clean.flist
|
||||
find $CORPUS/wsj0/si_dt_05 -print | grep -i ".wv1" | sort > dev_dt_05_clean.flist
|
||||
|
||||
|
||||
# Finding the transcript files:
|
||||
find -L $CORPUS -iname '*.dot' > dot_files.flist
|
||||
|
||||
# Convert the transcripts into our format (no normalization yet)
|
||||
# adding suffix to utt_id
|
||||
# 0 for clean condition
|
||||
for x in train_si84_clean test_eval92_clean test_eval92_5k_clean dev_dt_05_clean dev_dt_20_clean; do
|
||||
$local/flist2scp.pl $x.flist | sort > ${x}_sph_tmp.scp
|
||||
cat ${x}_sph_tmp.scp | awk '{print $1}' \
|
||||
| $local/find_transcripts.pl dot_files.flist > ${x}_tmp.trans1
|
||||
cat ${x}_sph_tmp.scp | awk '{printf("%s0 %s\n", $1, $2);}' > ${x}_sph.scp
|
||||
cat ${x}_tmp.trans1 | awk '{printf("%s0 ", $1); for(i=2;i<=NF;i++) printf("%s ", $i); printf("\n");}' > ${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_clean test_eval92_clean test_eval92_5k_clean dev_dt_05_clean dev_dt_20_clean; 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 |\n", $1, $2);}' < ${x}_sph.scp \
|
||||
> ${x}_wav.scp
|
||||
done
|
||||
|
||||
# Make the utt2spk and spk2utt files.
|
||||
for x in train_si84_clean test_eval92_clean test_eval92_5k_clean dev_dt_05_clean dev_dt_20_clean; do
|
||||
cat ${x}_sph.scp | awk '{print $1}' \
|
||||
| perl -ane 'chop; m:^...:; print "$_ $&\n";' > $x.utt2spk
|
||||
cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
|
||||
done
|
||||
|
||||
#in case we want to limit lm's on most frequent words, copy lm training word frequency list
|
||||
cp $CORPUS/wsj0/doc/lng_modl/vocab/wfl_64.lst $lmdir
|
||||
chmod u+w $lmdir/*.lst # had weird permissions on source.
|
||||
|
||||
# The 20K vocab, open-vocabulary language model (i.e. the one with UNK), without
|
||||
# verbalized pronunciations. This is the most common test setup, I understand.
|
||||
|
||||
cp $CORPUS/wsj0/doc/lng_modl/base_lm/bcb20onp.z $lmdir/lm_bg.arpa.gz || exit 1;
|
||||
chmod u+w $lmdir/lm_bg.arpa.gz
|
||||
|
||||
# trigram would be:
|
||||
cat $CORPUS/wsj0/doc/lng_modl/base_lm/tcb20onp.z | \
|
||||
perl -e 'while(<>){ if(m/^\\data\\/){ print; last; } } while(<>){ print; }' \
|
||||
| gzip -c -f > $lmdir/lm_tg.arpa.gz || exit 1;
|
||||
|
||||
prune-lm --threshold=1e-7 $lmdir/lm_tg.arpa.gz $lmdir/lm_tgpr.arpa || exit 1;
|
||||
gzip -f $lmdir/lm_tgpr.arpa || exit 1;
|
||||
|
||||
# repeat for 5k language models
|
||||
cp $CORPUS/wsj0/doc/lng_modl/base_lm/bcb05onp.z $lmdir/lm_bg_5k.arpa.gz || exit 1;
|
||||
chmod u+w $lmdir/lm_bg_5k.arpa.gz
|
||||
|
||||
# trigram would be: !only closed vocabulary here!
|
||||
cp $CORPUS/wsj0/doc/lng_modl/base_lm/tcb05cnp.z $lmdir/lm_tg_5k.arpa.gz || exit 1;
|
||||
chmod u+w $lmdir/lm_tg_5k.arpa.gz
|
||||
gunzip $lmdir/lm_tg_5k.arpa.gz
|
||||
tail -n 4328839 $lmdir/lm_tg_5k.arpa | gzip -c -f > $lmdir/lm_tg_5k.arpa.gz
|
||||
rm $lmdir/lm_tg_5k.arpa
|
||||
|
||||
prune-lm --threshold=1e-7 $lmdir/lm_tg_5k.arpa.gz $lmdir/lm_tgpr_5k.arpa || exit 1;
|
||||
gzip -f $lmdir/lm_tgpr_5k.arpa || exit 1;
|
||||
|
||||
|
||||
if [ ! -f wsj0-train-spkrinfo.txt ] || [ `cat wsj0-train-spkrinfo.txt | wc -l` -ne 134 ]; then
|
||||
rm -f wsj0-train-spkrinfo.txt
|
||||
wget http://www.ldc.upenn.edu/Catalog/docs/LDC93S6A/wsj0-train-spkrinfo.txt \
|
||||
|| ( echo "Getting wsj0-train-spkrinfo.txt from backup location" && \
|
||||
wget --no-check-certificate https://sourceforge.net/projects/kaldi/files/wsj0-train-spkrinfo.txt );
|
||||
fi
|
||||
|
||||
if [ ! -f wsj0-train-spkrinfo.txt ]; then
|
||||
echo "Could not get the spkrinfo.txt file from LDC website (moved)?"
|
||||
echo "This is possibly omitted from the training disks; couldn't find it."
|
||||
echo "Everything else may have worked; we just may be missing gender info"
|
||||
echo "which is only needed for VTLN-related diagnostics anyway."
|
||||
exit 1
|
||||
fi
|
||||
# Note: wsj0-train-spkrinfo.txt doesn't seem to be on the disks but the
|
||||
# LDC put it on the web. Perhaps it was accidentally omitted from the
|
||||
# disks.
|
||||
|
||||
cat $CORPUS/wsj0/doc/spkrinfo.txt \
|
||||
./wsj0-train-spkrinfo.txt | \
|
||||
perl -ane 'tr/A-Z/a-z/; m/^;/ || print;' | \
|
||||
awk '{print $1, $2}' | grep -v -- -- | sort | uniq > spk2gender
|
||||
|
||||
|
||||
echo "Data preparation succeeded"
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
#!/bin/bash
|
||||
. path.sh
|
||||
data=$1
|
||||
old_ali_dir=$2
|
||||
mix_ali_dir=$3
|
||||
mkdir -p $mix_ali_dir
|
||||
|
||||
cp $old_ali_dir/{final.mdl,num_jobs,tree} $mix_ali_dir/
|
||||
|
||||
gunzip -c $old_ali_dir/ali.*.gz | gzip -c > $old_ali_dir/ali.gz
|
||||
|
||||
feats="ark,s,cs:copy-feats scp:$data/feats.scp ark:- |"
|
||||
copy-clean-ali "$feats" "ark:gunzip -c $old_ali_dir/ali.gz |" "ark:| gzip -c > $mix_ali_dir/ali.1.gz"
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# Copyright 2010-2011 Microsoft Corporation
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# 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, 12/1/12
|
||||
|
||||
# This program takes as its standard input an .ndx file from the WSJ corpus that looks
|
||||
# like this:
|
||||
#;; File: tr_s_wv1.ndx, updated 04/26/94
|
||||
#;;
|
||||
#;; Index for WSJ0 SI-short Sennheiser training data
|
||||
#;; Data is read WSJ sentences, Sennheiser mic.
|
||||
#;; Contains 84 speakers X (~100 utts per speaker MIT/SRI and ~50 utts
|
||||
#;; per speaker TI) = 7236 utts
|
||||
#;;
|
||||
#11_1_1:wsj0/si_tr_s/01i/01ic0201.wv1
|
||||
#11_1_1:wsj0/si_tr_s/01i/01ic0202.wv1
|
||||
#11_1_1:wsj0/si_tr_s/01i/01ic0203.wv1
|
||||
|
||||
# and as command-line argument it takes the names of the WSJ disk locations, e.g.:
|
||||
# /group/corpora/public/wsjcam0/data on DICE machines.
|
||||
# It outputs a list of absolute pathnames.
|
||||
|
||||
$wsj_dir = $ARGV[0];
|
||||
|
||||
while(<STDIN>){
|
||||
if(m/^;/){ next; } # Comment. Ignore it.
|
||||
else {
|
||||
m/^([0-9_]+):\s*(\S+)$/ || die "Could not parse line $_";
|
||||
$filename = $2; # as a subdirectory of the distributed disk.
|
||||
if ($filename !~ m/\.wv1$/) { $filename .= ".wv1"; }
|
||||
$filename = "$wsj_dir/$filename";
|
||||
if (-e $filename) {
|
||||
print "$filename\n";
|
||||
} else {
|
||||
print STDERR "File $filename found in the index but not on disk\n";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,187 @@
|
|||
#!/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 "\nUSAGE: %s <corpus-directory>\n\n" `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
|
||||
|
||||
# This version for SI-84
|
||||
cat $CORPUS/wsj0/doc/indices/train/tr_s_wv1.ndx \
|
||||
| $local/cstr_ndx2flist.pl $CORPUS | sort \
|
||||
| grep -v wsj0/si_tr_s/401 > train_si84.flist
|
||||
|
||||
# This version for SI-284
|
||||
cat $CORPUS/wsj1/doc/indices/si_tr_s.ndx \
|
||||
$CORPUS/wsj0/doc/indices/train/tr_s_wv1.ndx \
|
||||
| $local/cstr_ndx2flist.pl $CORPUS | sort \
|
||||
| grep -v wsj0/si_tr_s/401 > train_si284.flist
|
||||
|
||||
# Now for the test sets.
|
||||
# $CORPUS/wsj1/doc/indices/readme.doc
|
||||
# describes all the different test sets.
|
||||
# Note: each test-set seems to come in multiple versions depending
|
||||
# on different vocabulary sizes, verbalized vs. non-verbalized
|
||||
# pronunciations, etc. We use the largest vocab and non-verbalized
|
||||
# pronunciations.
|
||||
# The most normal one seems to be the "baseline 60k test set", which
|
||||
# is h1_p0.
|
||||
|
||||
# Nov'92 (333 utts)
|
||||
# These index files have a slightly different format;
|
||||
# have to add .wv1, which is done in cstr_ndx2flist.pl
|
||||
cat $CORPUS/wsj0/doc/indices/test/nvp/si_et_20.ndx | \
|
||||
$local/cstr_ndx2flist.pl $CORPUS | sort > test_eval92.flist
|
||||
|
||||
# Nov'92 (330 utts, 5k vocab)
|
||||
cat $CORPUS/wsj0/doc/indices/test/nvp/si_et_05.ndx | \
|
||||
$local/cstr_ndx2flist.pl $CORPUS | sort > test_eval92_5k.flist
|
||||
|
||||
# Nov'93: (213 utts)
|
||||
# Have to replace a wrong disk-id.
|
||||
cat $CORPUS/wsj1/doc/indices/wsj1/eval/h1_p0.ndx | \
|
||||
$local/cstr_ndx2flist.pl $CORPUS | sort > test_eval93.flist
|
||||
|
||||
# Nov'93: (215 utts, 5k)
|
||||
cat $CORPUS/wsj1/doc/indices/wsj1/eval/h2_p0.ndx | \
|
||||
$local/cstr_ndx2flist.pl $CORPUS | sort > test_eval93_5k.flist
|
||||
|
||||
# Dev-set for Nov'93 (503 utts)
|
||||
cat $CORPUS/wsj1/doc/indices/h1_p0.ndx | \
|
||||
$local/cstr_ndx2flist.pl $CORPUS | sort > test_dev93.flist
|
||||
|
||||
# Dev-set for Nov'93 (513 utts, 5k vocab)
|
||||
cat $CORPUS/wsj1/doc/indices/h2_p0.ndx | \
|
||||
$local/cstr_ndx2flist.pl $CORPUS | sort > test_dev93_5k.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 $CORPUS/???1/??_??_20 -print | grep -i ".wv1" | sort > dev_dt_20.flist
|
||||
find $CORPUS/???1/??_??_05 -print | grep -i ".wv1" | sort > dev_dt_05.flist
|
||||
|
||||
|
||||
# Finding the transcript files:
|
||||
find -L $CORPUS -iname '*.dot' > dot_files.flist
|
||||
|
||||
# Convert the transcripts into our format (no normalization yet)
|
||||
for x in train_si84 train_si284 test_eval92 test_eval93 test_dev93 test_eval92_5k test_eval93_5k test_dev93_5k dev_dt_05 dev_dt_20; do
|
||||
$local/flist2scp.pl $x.flist | sort > ${x}_sph.scp
|
||||
cat ${x}_sph.scp | awk '{print $1}' \
|
||||
| $local/find_transcripts.pl dot_files.flist > $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 train_si284 test_eval92 test_eval93 test_dev93 test_eval92_5k test_eval93_5k test_dev93_5k dev_dt_05 dev_dt_20; do
|
||||
cat $x.trans1 | $local/normalize_transcript.pl $noiseword \
|
||||
| sort > $x.txt || exit 1;
|
||||
done
|
||||
|
||||
# Create scp's with wav's. (the wv1 in the distribution is not really wav, it is sph.)
|
||||
for x in train_si84 train_si284 test_eval92 test_eval93 test_dev93 test_eval92_5k test_eval93_5k test_dev93_5k dev_dt_05 dev_dt_20; do
|
||||
awk '{printf("%s '$sph2pipe' -f wav %s |\n", $1, $2);}' < ${x}_sph.scp \
|
||||
> ${x}_wav.scp
|
||||
done
|
||||
|
||||
# Make the utt2spk and spk2utt files.
|
||||
for x in train_si84 train_si284 test_eval92 test_eval93 test_dev93 test_eval92_5k test_eval93_5k test_dev93_5k dev_dt_05 dev_dt_20; do
|
||||
cat ${x}_sph.scp | awk '{print $1}' \
|
||||
| perl -ane 'chop; m:^...:; print "$_ $&\n";' > $x.utt2spk
|
||||
cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
|
||||
done
|
||||
|
||||
#in case we want to limit lm's on most frequent words, copy lm training word frequency list
|
||||
cp $CORPUS/wsj1/doc/lng_modl/vocab/wfl_64.lst $lmdir
|
||||
chmod u+w $lmdir/*.lst # had weird permissions on source.
|
||||
|
||||
# The 20K vocab, open-vocabulary language model (i.e. the one with UNK), without
|
||||
# verbalized pronunciations. This is the most common test setup, I understand.
|
||||
|
||||
cp $CORPUS/wsj1/doc/lng_modl/base_lm/bcb20onp.z $lmdir/lm_bg.arpa.gz || exit 1;
|
||||
chmod u+w $lmdir/lm_bg.arpa.gz
|
||||
|
||||
# trigram would be:
|
||||
cat $CORPUS/wsj1/doc/lng_modl/base_lm/tcb20onp.z | \
|
||||
perl -e 'while(<>){ if(m/^\\data\\/){ print; last; } } while(<>){ print; }' \
|
||||
| gzip -c -f > $lmdir/lm_tg.arpa.gz || exit 1;
|
||||
|
||||
prune-lm --threshold=1e-7 $lmdir/lm_tg.arpa.gz $lmdir/lm_tgpr.arpa || exit 1;
|
||||
gzip -f $lmdir/lm_tgpr.arpa || exit 1;
|
||||
|
||||
# repeat for 5k language models
|
||||
cp $CORPUS/wsj1/doc/lng_modl/base_lm/bcb05onp.z $lmdir/lm_bg_5k.arpa.gz || exit 1;
|
||||
chmod u+w $lmdir/lm_bg_5k.arpa.gz
|
||||
|
||||
# trigram would be: !only closed vocabulary here!
|
||||
cp $CORPUS/wsj1/doc/lng_modl/base_lm/tcb05cnp.z $lmdir/lm_tg_5k.arpa.gz || exit 1;
|
||||
chmod u+w $lmdir/lm_tg_5k.arpa.gz
|
||||
gunzip $lmdir/lm_tg_5k.arpa.gz
|
||||
tail -n 4328839 $lmdir/lm_tg_5k.arpa | gzip -c -f > $lmdir/lm_tg_5k.arpa.gz
|
||||
rm $lmdir/lm_tg_5k.arpa
|
||||
|
||||
prune-lm --threshold=1e-7 $lmdir/lm_tg_5k.arpa.gz $lmdir/lm_tgpr_5k.arpa || exit 1;
|
||||
gzip -f $lmdir/lm_tgpr_5k.arpa || exit 1;
|
||||
|
||||
|
||||
if [ ! -f wsj0-train-spkrinfo.txt ] || [ `cat wsj0-train-spkrinfo.txt | wc -l` -ne 134 ]; then
|
||||
rm -f wsj0-train-spkrinfo.txt
|
||||
wget http://www.ldc.upenn.edu/Catalog/docs/LDC93S6A/wsj0-train-spkrinfo.txt \
|
||||
|| ( echo "Getting wsj0-train-spkrinfo.txt from backup location" && \
|
||||
wget --no-check-certificate https://sourceforge.net/projects/kaldi/files/wsj0-train-spkrinfo.txt );
|
||||
fi
|
||||
|
||||
if [ ! -f wsj0-train-spkrinfo.txt ]; then
|
||||
echo "Could not get the spkrinfo.txt file from LDC website (moved)?"
|
||||
echo "This is possibly omitted from the training disks; couldn't find it."
|
||||
echo "Everything else may have worked; we just may be missing gender info"
|
||||
echo "which is only needed for VTLN-related diagnostics anyway."
|
||||
exit 1
|
||||
fi
|
||||
# Note: wsj0-train-spkrinfo.txt doesn't seem to be on the disks but the
|
||||
# LDC put it on the web. Perhaps it was accidentally omitted from the
|
||||
# disks.
|
||||
|
||||
cat $CORPUS/wsj0/doc/spkrinfo.txt \
|
||||
$CORPUS/wsj1/doc/evl_spok/spkrinfo.txt \
|
||||
$CORPUS/wsj1/doc/dev_spok/spkrinfo.txt \
|
||||
$CORPUS/wsj1/doc/train/spkrinfo.txt \
|
||||
./wsj0-train-spkrinfo.txt | \
|
||||
perl -ane 'tr/A-Z/a-z/; m/^;/ || print;' | \
|
||||
awk '{print $1, $2}' | grep -v -- -- | sort | uniq > spk2gender
|
||||
|
||||
|
||||
echo "Data preparation succeeded"
|
||||
|
|
@ -0,0 +1,172 @@
|
|||
#!/bin/bash
|
||||
|
||||
# This script builds a larger word-list and dictionary
|
||||
# than used for the LMs supplied with the WSJ corpus.
|
||||
# It uses a couple of strategies to fill-in words in
|
||||
# the LM training data but not in CMUdict. One is
|
||||
# to generate special prons for possible acronyms, that
|
||||
# just consist of the constituent letters. The other
|
||||
# is designed to handle derivatives of known words
|
||||
# (e.g. deriving the pron of a plural from the pron of
|
||||
# the base-word), but in a more general, learned-from-data
|
||||
# way.
|
||||
# It makes use of scripts in local/dict/
|
||||
|
||||
if [ $# -ne 1 ]; then
|
||||
echo "Usage: local/cstr_wsj_train_lms.sh WSJ1_doc_dir"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
export PATH=$PATH:`pwd`/local/dict/
|
||||
srcdir=$1
|
||||
|
||||
if [ ! -d $srcdir/lng_modl ]; then
|
||||
echo "Expecting 'lng_modl' under WSJ doc directory '$srcdir'"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
mkdir -p data/local/dict_larger
|
||||
dir=data/local/dict_larger
|
||||
cp data/local/dict/* data/local/dict_larger # Various files describing phones etc.
|
||||
# are there; we just want to copy them as the phoneset is the same.
|
||||
rm data/local/dict_larger/lexicon.txt # we don't want this.
|
||||
mincount=2 # Minimum count of an OOV we will try to generate a pron for.
|
||||
|
||||
[ ! -f data/local/dict/cmudict/cmudict.0.7a ] && echo "CMU dict not in expected place" && exit 1;
|
||||
|
||||
# Remove comments from cmudict; print first field; remove
|
||||
# words like FOO(1) which are alternate prons: our dict format won't
|
||||
# include these markers.
|
||||
grep -v ';;;' data/local/dict/cmudict/cmudict.0.7a |
|
||||
perl -ane 's/^(\S+)\(\d+\)/$1/; print; ' | sort | uniq > $dir/dict.cmu
|
||||
|
||||
cat $dir/dict.cmu | awk '{print $1}' | sort | uniq > $dir/wordlist.cmu
|
||||
|
||||
echo "Getting training data [this should take at least a few seconds; if not, there's a problem]"
|
||||
|
||||
# Convert to uppercase, remove XML-like markings.
|
||||
# For words ending in "." that are not in CMUdict, we assume that these
|
||||
# are periods that somehow remained in the data during data preparation,
|
||||
# and we we replace the "." with "\n". Note: we found this by looking at
|
||||
# oov.counts below (before adding this rule).
|
||||
|
||||
touch $dir/cleaned.gz
|
||||
if [ `du -m $dir/cleaned.gz | cut -f 1` -eq 73 ]; then
|
||||
echo "Not getting cleaned data in $dir/cleaned.gz again [already exists]";
|
||||
else
|
||||
gunzip -c $srcdir/lng_modl/lm_train/np_data/{87,88,89}/*.z \
|
||||
| awk '/^</{next}{print toupper($0)}' | perl -e '
|
||||
open(F, "<$ARGV[0]")||die;
|
||||
while(<F>){ chop; $isword{$_} = 1; }
|
||||
while(<STDIN>) {
|
||||
@A = split(" ", $_);
|
||||
for ($n = 0; $n < @A; $n++) {
|
||||
$a = $A[$n];
|
||||
if (! $isword{$a} && $a =~ s/^([^\.]+)\.$/$1/) { # nonwords that end in "."
|
||||
# and have no other "." in them: treat as period.
|
||||
print "$a";
|
||||
if ($n+1 < @A) { print "\n"; }
|
||||
} else { print "$a "; }
|
||||
}
|
||||
print "\n";
|
||||
}
|
||||
' $dir/wordlist.cmu | gzip -c > $dir/cleaned.gz
|
||||
fi
|
||||
|
||||
# get unigram counts
|
||||
echo "Getting unigram counts"
|
||||
gunzip -c $dir/cleaned.gz | tr -s ' ' '\n' | \
|
||||
awk '{count[$1]++} END{for (w in count) { print count[w], w; }}' | sort -nr > $dir/unigrams
|
||||
|
||||
cat $dir/unigrams | awk -v dict=$dir/dict.cmu \
|
||||
'BEGIN{while(getline<dict) seen[$1]=1;} {if(!seen[$2]){print;}}' \
|
||||
> $dir/oov.counts
|
||||
|
||||
echo "Most frequent unseen unigrams are: "
|
||||
head $dir/oov.counts
|
||||
|
||||
# Prune away singleton counts, and remove things with numbers in
|
||||
# (which should have been normalized) and with no letters at all.
|
||||
|
||||
|
||||
cat $dir/oov.counts | awk -v thresh=$mincount '{if ($1 >= thresh) { print $2; }}' \
|
||||
| awk '/[0-9]/{next;} /[A-Z]/{print;}' > $dir/oovlist
|
||||
|
||||
# Automatic rule-finding...
|
||||
|
||||
# First make some prons for possible acronyms.
|
||||
# Note: we don't do this for things like U.K or U.N,
|
||||
# or A.B. (which doesn't exist anyway),
|
||||
# as we consider this normalization/spelling errors.
|
||||
|
||||
cat $dir/oovlist | local/dict/get_acronym_prons.pl $dir/dict.cmu > $dir/dict.acronyms
|
||||
|
||||
mkdir $dir/f $dir/b # forward, backward directions of rules...
|
||||
# forward is normal suffix
|
||||
# rules, backward is reversed (prefix rules). These
|
||||
# dirs contain stuff we create while making the rule-based
|
||||
# extensions to the dictionary.
|
||||
|
||||
# Remove ; and , from words, if they are present; these
|
||||
# might crash our scripts, as they are used as separators there.
|
||||
filter_dict.pl $dir/dict.cmu > $dir/f/dict
|
||||
cat $dir/oovlist | filter_dict.pl > $dir/f/oovs
|
||||
reverse_dict.pl $dir/f/dict > $dir/b/dict
|
||||
reverse_dict.pl $dir/f/oovs > $dir/b/oovs
|
||||
|
||||
# The next stage takes a few minutes.
|
||||
# Note: the forward stage takes longer, as English is
|
||||
# mostly a suffix-based language, and there are more rules
|
||||
# that it finds.
|
||||
for d in $dir/f $dir/b; do
|
||||
(
|
||||
cd $d
|
||||
cat dict | get_rules.pl 2>get_rules.log >rules
|
||||
get_rule_hierarchy.pl rules >hierarchy
|
||||
awk '{print $1}' dict | get_candidate_prons.pl rules dict | \
|
||||
limit_candidate_prons.pl hierarchy | \
|
||||
score_prons.pl dict | \
|
||||
count_rules.pl >rule.counts
|
||||
# the sort command below is just for convenience of reading.
|
||||
score_rules.pl <rule.counts | sort -t';' -k3,3 -n -r >rules.with_scores
|
||||
get_candidate_prons.pl rules.with_scores dict oovs | \
|
||||
limit_candidate_prons.pl hierarchy > oovs.candidates
|
||||
) &
|
||||
done
|
||||
wait
|
||||
|
||||
# Merge the candidates.
|
||||
reverse_candidates.pl $dir/b/oovs.candidates | cat - $dir/f/oovs.candidates | sort > $dir/oovs.candidates
|
||||
select_candidate_prons.pl <$dir/oovs.candidates | awk -F';' '{printf("%s %s\n", $1, $2);}' \
|
||||
> $dir/dict.oovs
|
||||
|
||||
cat $dir/dict.acronyms $dir/dict.oovs | sort | uniq > $dir/dict.oovs_merged
|
||||
|
||||
awk '{print $1}' $dir/dict.oovs_merged | uniq > $dir/oovlist.handled
|
||||
sort $dir/oovlist | diff - $dir/oovlist.handled | grep -v 'd' | sed 's:< ::' > $dir/oovlist.not_handled
|
||||
|
||||
|
||||
# add_counts.pl attaches to original counts to the list of handled/not-handled OOVs
|
||||
add_counts.pl $dir/oov.counts $dir/oovlist.handled | sort -nr > $dir/oovlist.handled.counts
|
||||
add_counts.pl $dir/oov.counts $dir/oovlist.not_handled | sort -nr > $dir/oovlist.not_handled.counts
|
||||
|
||||
echo "**Top OOVs we handled are:**";
|
||||
head $dir/oovlist.handled.counts
|
||||
echo "**Top OOVs we didn't handle are as follows (note: they are mostly misspellings):**";
|
||||
head $dir/oovlist.not_handled.counts
|
||||
|
||||
|
||||
echo "Count of OOVs we handled is `awk '{x+=$1} END{print x}' $dir/oovlist.handled.counts`"
|
||||
echo "Count of OOVs we couldn't handle is `awk '{x+=$1} END{print x}' $dir/oovlist.not_handled.counts`"
|
||||
echo "Count of OOVs we didn't handle due to low count is" \
|
||||
`awk -v thresh=$mincount '{if ($1 < thresh) x+=$1; } END{print x;}' $dir/oov.counts`
|
||||
# The two files created above are for humans to look at, as diagnostics.
|
||||
|
||||
cat <<EOF | cat - $dir/dict.cmu $dir/dict.oovs_merged | sort | uniq > $dir/lexicon.txt
|
||||
!SIL SIL
|
||||
<SPOKEN_NOISE> SPN
|
||||
<UNK> SPN
|
||||
<NOISE> NSN
|
||||
EOF
|
||||
|
||||
echo "Created $dir/lexicon.txt"
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
|
||||
# Add counts to an oovlist.
|
||||
# Reads in counts as output by uniq -c, and
|
||||
# an oovlist, and prints out the counts of the oovlist.
|
||||
|
||||
(@ARGV == 1 || @ARGV == 2) || die "Usage: add_counts.pl count_file [oovlist]\n";
|
||||
|
||||
$counts = shift @ARGV;
|
||||
|
||||
open(C, "<$counts") || die "Opening counts file $counts";
|
||||
|
||||
while(<C>) {
|
||||
@A = split(" ", $_);
|
||||
@A == 2 || die "Bad line in counts file: $_";
|
||||
($count, $word) = @A;
|
||||
$count =~ m:^\d+$: || die "Bad count $A[0]\n";
|
||||
$counts{$word} = $count;
|
||||
}
|
||||
|
||||
while(<>) {
|
||||
chop;
|
||||
$w = $_;
|
||||
$w =~ m:\S+: || die "Bad word $w";
|
||||
defined $counts{$w} || die "Word $w not present in counts file";
|
||||
print "\t$counts{$w}\t$w\n";
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# This program takes the output of score_prons.pl and collates
|
||||
# it for each (rule, destress) pair so that we get the
|
||||
# counts of right/partial/wrong for each pair.
|
||||
|
||||
# The input is a 7-tuple on each line, like:
|
||||
# word;pron;base-word;base-pron;rule-name;de-stress;right|partial|wrong
|
||||
#
|
||||
# The output format is a 5-tuple like:
|
||||
#
|
||||
# rule;destress;right-count;partial-count;wrong-count
|
||||
#
|
||||
|
||||
if (@ARGV != 0 && @ARGV != 1) {
|
||||
die "Usage: count_rules.pl < scored_candidate_prons > rule_counts";
|
||||
}
|
||||
|
||||
|
||||
while(<>) {
|
||||
chop;
|
||||
$line = $_;
|
||||
my ($word, $pron, $baseword, $basepron, $rulename, $destress, $score) = split(";", $line);
|
||||
|
||||
my $key = $rulename . ";" . $destress;
|
||||
|
||||
if (!defined $counts{$key}) {
|
||||
$counts{$key} = [ 0, 0, 0 ]; # new anonymous array.
|
||||
}
|
||||
$ref = $counts{$key};
|
||||
if ($score eq "right") {
|
||||
$$ref[0]++;
|
||||
} elsif ($score eq "partial") {
|
||||
$$ref[1]++;
|
||||
} elsif ($score eq "wrong") {
|
||||
$$ref[2]++;
|
||||
} else {
|
||||
die "Bad score $score\n";
|
||||
}
|
||||
}
|
||||
|
||||
while ( my ($key, $value) = each(%counts)) {
|
||||
print $key . ";" . join(";", @$value) . "\n";
|
||||
}
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
|
||||
# This program reads and writes either a dictionary or just a list
|
||||
# of words, and it removes any words containing ";" or "," as these
|
||||
# are used in these programs. It will warn about these.
|
||||
# It will die if the pronunciations have these symbols in.
|
||||
while(<>) {
|
||||
chop;
|
||||
@A = split(" ", $_);
|
||||
$word = shift @A;
|
||||
|
||||
if ($word =~ m:[;,]:) {
|
||||
print STDERR "Omitting line $_ since it has one of the banned characters ; or ,\n" ;
|
||||
} else {
|
||||
$_ =~ m:[;,]: && die "Phones cannot have ; or , in them.";
|
||||
print $_ . "\n";
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,95 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# Reads a dictionary, and prints out a list of words that seem to be pronounced
|
||||
# as acronyms (not including plurals of acronyms, just acronyms). Uses
|
||||
# the prons of the individual letters (A., B. and so on) to judge this.
|
||||
# Note: this is somewhat dependent on the convention used in CMUduct, that
|
||||
# the individual letters are spelled this way (e.g. "A.").
|
||||
|
||||
$max_length = 6; # Max length of words that might be
|
||||
# acronyms.
|
||||
|
||||
while(<>) { # Read the dict.
|
||||
chop;
|
||||
@A = split(" ", $_);
|
||||
$word = shift @A;
|
||||
$pron = join(" ", @A);
|
||||
if ($word =~ m/^([A-Z])\.$/ ) {
|
||||
chop $word; # Remove trailing "." to get just the letter
|
||||
$letter = $1;
|
||||
if (!defined $letter_prons{$letter} ) {
|
||||
$letter_prons{$letter} = [ ]; # new anonymous array
|
||||
}
|
||||
$arrayref = $letter_prons{$letter};
|
||||
push @$arrayref, $pron;
|
||||
} elsif( length($word) <= $max_length ) {
|
||||
$pronof{$word . "," . $pron} = 1;
|
||||
$isword{$word} = 1;
|
||||
#if (!defined $prons{$word} ) {
|
||||
# $prons{$word} = [ ];
|
||||
#}
|
||||
# push @{$prons{$word}}, $pron;
|
||||
}
|
||||
}
|
||||
|
||||
sub get_letter_prons;
|
||||
|
||||
foreach $word (keys %isword) {
|
||||
my @letter_prons = get_letter_prons($word);
|
||||
foreach $pron (@letter_prons) {
|
||||
if (defined $pronof{$word.",".$pron}) {
|
||||
print "$word $pron\n";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
sub get_letter_prons {
|
||||
@acronym = split("", shift); # The letters in the word.
|
||||
my @prons = ( "" );
|
||||
|
||||
while (@acronym > 0) {
|
||||
$l = shift @acronym;
|
||||
$n = 1; # num-repeats of letter $l.
|
||||
while (@acronym > 0 && $acronym[0] eq $l) {
|
||||
$n++;
|
||||
shift @acronym;
|
||||
}
|
||||
my $arrayref = $letter_prons{$l};
|
||||
my @prons_of_block = ();
|
||||
if ($n == 1) { # Just one repeat.
|
||||
foreach $lpron ( @$arrayref ) {
|
||||
push @prons_of_block, $lpron; # typically (always?) just one pron of a letter.
|
||||
}
|
||||
} elsif ($n == 2) { # Two repeats. Can be "double a" or "a a"
|
||||
foreach $lpron ( @$arrayref ) {
|
||||
push @prons_of_block, "D AH1 B AH0 L " . $lpron;
|
||||
push @prons_of_block, $lpron . $lpron;
|
||||
}
|
||||
} elsif ($n == 3) { # can be "triple a" or "a a a"
|
||||
foreach $lpron ( @$arrayref ) {
|
||||
push @prons_of_block, "T R IH1 P AH0 L " . $lpron;
|
||||
push @prons_of_block, $lpron . $lpron . $lpron;
|
||||
}
|
||||
} elsif ($n >= 4) { # let's say it can only be that letter repeated $n times..
|
||||
# not sure really.
|
||||
foreach $lpron ( @$arrayref ) {
|
||||
$nlpron = "";
|
||||
for ($m = 0; $m < $n; $m++) { $nlpron = $nlpron . $lpron; }
|
||||
push @prons_of_block, $nlpron;
|
||||
}
|
||||
}
|
||||
my @new_prons = ();
|
||||
foreach $pron (@prons) {
|
||||
foreach $pron_of_block(@prons_of_block) {
|
||||
if ($pron eq "") {
|
||||
push @new_prons, $pron_of_block;
|
||||
} else {
|
||||
push @new_prons, $pron . " " . $pron_of_block;
|
||||
}
|
||||
}
|
||||
}
|
||||
@prons = @new_prons;
|
||||
}
|
||||
return @prons;
|
||||
}
|
||||
|
|
@ -0,0 +1,123 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# Reads a dictionary (for prons of letters), and an OOV list,
|
||||
# and puts out candidate pronunciations of words in that list
|
||||
# that could plausibly be acronyms.
|
||||
# We judge that a word can plausibly be an acronym if it is
|
||||
# a sequence of just letters (no non-letter characters such
|
||||
# as "'"), or something like U.K.,
|
||||
# and the number of letters is four or less.
|
||||
#
|
||||
# If the text were not already pre-normalized, there would
|
||||
# be other hints such as capitalization.
|
||||
|
||||
# This program appends
|
||||
# the prons of the individual letters (A., B. and so on) to work out
|
||||
# the pron of the acronym.
|
||||
# Note: this is somewhat dependent on the convention used in CMUduct, that
|
||||
# the individual letters are spelled this way (e.g. "A."). [it seems
|
||||
# to also have the separated versions.
|
||||
|
||||
if (!(@ARGV == 1 || @ARGV == 2)) {
|
||||
print "Usage: get_acronym_prons.pl dict [oovlist]";
|
||||
}
|
||||
|
||||
$max_length = 4; # Max #letters in an acronym. (Longer
|
||||
# acronyms tend to have "pseudo-pronunciations", e.g. think about UNICEF.
|
||||
|
||||
$dict = shift @ARGV;
|
||||
open(D, "<$dict") || die "Opening dictionary $dict";
|
||||
|
||||
while(<D>) { # Read the dict, to get the prons of the letters.
|
||||
chop;
|
||||
@A = split(" ", $_);
|
||||
$word = shift @A;
|
||||
$pron = join(" ", @A);
|
||||
if ($word =~ m/^([A-Z])\.$/ ) {
|
||||
chop $word; # Remove trailing "." to get just the letter
|
||||
$letter = $1;
|
||||
if (!defined $letter_prons{$letter} ) {
|
||||
$letter_prons{$letter} = [ ]; # new anonymous array
|
||||
}
|
||||
$arrayref = $letter_prons{$letter};
|
||||
push @$arrayref, $pron;
|
||||
} elsif( length($word) <= $max_length ) {
|
||||
$pronof{$word . "," . $pron} = 1;
|
||||
$isword{$word} = 1;
|
||||
#if (!defined $prons{$word} ) {
|
||||
# $prons{$word} = [ ];
|
||||
#}
|
||||
# push @{$prons{$word}}, $pron;
|
||||
}
|
||||
}
|
||||
|
||||
sub get_letter_prons;
|
||||
|
||||
while(<>) { # Read OOVs.
|
||||
# For now, just do the simple cases without "." in
|
||||
# between... things with "." in the OOV list seem to
|
||||
# be mostly errors.
|
||||
chop;
|
||||
$word = $_;
|
||||
if ($word =~ m/^[A-Z]{1,5}$/) {
|
||||
foreach $pron ( get_letter_prons($word) ) { # E.g. UNPO
|
||||
print "$word $pron\n";
|
||||
}
|
||||
} elsif ($word =~ m:^(\w\.){1,4}\w\.?$:) { # E.g. U.K. Make the final "." optional.
|
||||
$letters = $word;
|
||||
$letters =~ s:\.::g;
|
||||
foreach $pron ( get_letter_prons($letters) ) {
|
||||
print "$word $pron\n";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sub get_letter_prons {
|
||||
@acronym = split("", shift); # The letters in the word.
|
||||
my @prons = ( "" );
|
||||
|
||||
while (@acronym > 0) {
|
||||
$l = shift @acronym;
|
||||
$n = 1; # num-repeats of letter $l.
|
||||
while (@acronym > 0 && $acronym[0] eq $l) {
|
||||
$n++;
|
||||
shift @acronym;
|
||||
}
|
||||
my $arrayref = $letter_prons{$l};
|
||||
my @prons_of_block = ();
|
||||
if ($n == 1) { # Just one repeat.
|
||||
foreach $lpron ( @$arrayref ) {
|
||||
push @prons_of_block, $lpron; # typically (always?) just one pron of a letter.
|
||||
}
|
||||
} elsif ($n == 2) { # Two repeats. Can be "double a" or "a a"
|
||||
foreach $lpron ( @$arrayref ) {
|
||||
push @prons_of_block, "D AH1 B AH0 L " . $lpron;
|
||||
push @prons_of_block, $lpron . " " . $lpron;
|
||||
}
|
||||
} elsif ($n == 3) { # can be "triple a" or "a a a"
|
||||
foreach $lpron ( @$arrayref ) {
|
||||
push @prons_of_block, "T R IH1 P AH0 L " . $lpron;
|
||||
push @prons_of_block, "$lpron $lpron $lpron";
|
||||
}
|
||||
} elsif ($n >= 4) { # let's say it can only be that letter repeated $n times..
|
||||
# not sure really.
|
||||
foreach $lpron ( @$arrayref ) {
|
||||
$nlpron = $lpron;
|
||||
for ($m = 1; $m < $n; $m++) { $nlpron = $nlpron . " " . $lpron; }
|
||||
push @prons_of_block, $nlpron;
|
||||
}
|
||||
}
|
||||
my @new_prons = ();
|
||||
foreach $pron (@prons) {
|
||||
foreach $pron_of_block(@prons_of_block) {
|
||||
if ($pron eq "") {
|
||||
push @new_prons, $pron_of_block;
|
||||
} else {
|
||||
push @new_prons, $pron . " " . $pron_of_block;
|
||||
}
|
||||
}
|
||||
}
|
||||
@prons = @new_prons;
|
||||
}
|
||||
return @prons;
|
||||
}
|
||||
|
|
@ -0,0 +1,187 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# This script takes three command-line arguments (typically files, or "-"):
|
||||
# the suffix rules (as output by get_rules.pl), the rule-hierarchy
|
||||
# (from get_rule_hierarchy.pl), and the words that we want prons to be
|
||||
# generated for (one per line).
|
||||
|
||||
# The output consists of candidate generated pronunciations for those words,
|
||||
# together with information about how we generated those pronunciations.
|
||||
# This does not do pruning of the candidates using the restriction
|
||||
# "you can't use a more general rule when a more specific one is applicable".
|
||||
# That is done by limit_candidate_prons.pl.
|
||||
|
||||
# Each line of the output consists of a 4-tuple, separated by ";", of the
|
||||
# form:
|
||||
# word;pron;base-word;base-pron;rule-name;destress[;rule-score]
|
||||
# [the last field is only present if you supplied rules with score information].
|
||||
# where:
|
||||
# - "word" is the input word that we queried for, e.g. WASTED
|
||||
# - "pron" is the generated pronunciation, e.g. "W EY1 S T AH0 D"
|
||||
# - rule-name is a 4-tuple separated by commas that describes the rule, e.g.
|
||||
# "STED,STING,D,NG",
|
||||
# - "base-word" is the base-word we're getting the pron from,
|
||||
# e.g. WASTING
|
||||
# - "base-pron" is the pron of the base-word, e.g. "W EY1 S T IH0 NG"
|
||||
# - "destress" is either "yes" or "no" and corresponds to whether we destressed the
|
||||
# base-word or not [de-stressing just corresponds to just taking any 2's down to 1's,
|
||||
# although we may extend this in future]...
|
||||
# - "rule-score" is a numeric score of the rule (this field is only present
|
||||
# if there was score information in your rules.
|
||||
|
||||
|
||||
(@ARGV == 2 || @ARGV == 3) || die "Usage: get_candidate_prons.pl rules base-dict [ words ]";
|
||||
|
||||
$min_prefix_len = 3; # this should probably match with get_rules.pl
|
||||
|
||||
$rules = shift @ARGV; # Note: rules may be with destress "yes/no" indicators or without...
|
||||
# if without, it's treated as if both "yes" and "no" are present.
|
||||
$dict = shift @ARGV;
|
||||
|
||||
open(R, "<$rules") || die "Opening rules file: $rules";
|
||||
|
||||
sub process_word;
|
||||
|
||||
while(<R>) {
|
||||
chop $_;
|
||||
my ($rule, $destress, $rule_score) = split(";", $_); # We may have "destress" markings (yes|no),
|
||||
# and scores, or we may have just rule, in which case
|
||||
# $destress and $rule_score will be undefined.
|
||||
|
||||
my @R = split(",", $rule, 4); # "my" means new instance of @R each
|
||||
# time we do this loop -> important because we'll be creating
|
||||
# a reference to @R below.
|
||||
# Note: the last arg to SPLIT tells it how many fields max to get.
|
||||
# This stops it from omitting empty trailing fields.
|
||||
@R == 4 || die "Bad rule $_";
|
||||
$suffix = $R[0]; # Suffix of word we want pron for.
|
||||
if (!defined $isrule{$rule}) {
|
||||
$isrule{$rule} = 1; # make sure we do this only once for each rule
|
||||
# (don't repeate for different stresses).
|
||||
if (!defined $suffix2rule{$suffix}) {
|
||||
# The syntax [ $x, $y, ... ] means a reference to a newly created array
|
||||
# containing $x, $y, etc. \@R creates an array reference to R.
|
||||
# so suffix2rule is a hash from suffix to ref to array of refs to
|
||||
# 4-dimensional arrays.
|
||||
$suffix2rule{$suffix} = [ \@R ];
|
||||
} else {
|
||||
# Below, the syntax @{$suffix2rule{$suffix}} dereferences the array
|
||||
# reference inside the hash; \@R pushes onto that array a new array
|
||||
# reference pointing to @R.
|
||||
push @{$suffix2rule{$suffix}}, \@R;
|
||||
}
|
||||
}
|
||||
if (!defined $rule_score) { $rule_score = -1; } # -1 means we don't have the score info.
|
||||
|
||||
# Now store information on which destress markings (yes|no) this rule
|
||||
# is valid for, and the associated scores (if supplied)
|
||||
# If just the rule is given (i.e. no destress marking specified),
|
||||
# assume valid for both.
|
||||
if (!defined $destress) { # treat as if both "yes" and "no" are valid.
|
||||
$rule_and_destress_to_rule_score{$rule.";yes"} = $rule_score;
|
||||
$rule_and_destress_to_rule_score{$rule.";no"} = $rule_score;
|
||||
} else {
|
||||
$rule_and_destress_to_rule_score{$rule.";".$destress} = $rule_score;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
open(D, "<$dict") || die "Opening base dictionary: $dict";
|
||||
while(<D>) {
|
||||
@A = split(" ", $_);
|
||||
$word = shift @A;
|
||||
$pron = join(" ", @A);
|
||||
if (!defined $word2prons{$word}) {
|
||||
$word2prons{$word} = [ $pron ]; # Ref to new anonymous array containing just "pron".
|
||||
} else {
|
||||
push @{$word2prons{$word}}, $pron; # Push $pron onto array referred to (@$ref derefs array).
|
||||
}
|
||||
}
|
||||
foreach $word (%word2prons) {
|
||||
# Set up the hash "prefixcount", which says how many times a char-sequence
|
||||
# is a prefix (not necessarily a strict prefix) of a word in the dict.
|
||||
$len = length($word);
|
||||
for ($l = 0; $l <= $len; $l++) {
|
||||
$prefixcount{substr($word, 0, $l)}++;
|
||||
}
|
||||
}
|
||||
|
||||
open(R, "<$rules") || die "Opening rules file: $rules";
|
||||
|
||||
|
||||
while(<>) {
|
||||
chop;
|
||||
m/^\S+$/ || die;
|
||||
process_word($_);
|
||||
}
|
||||
|
||||
sub process_word {
|
||||
my $word = shift @_;
|
||||
$len = length($word);
|
||||
# $owncount is used in evaluating whether a particular prefix is a prefix
|
||||
# of some other word in the dict... if a word itself may be in the dict
|
||||
# (usually because we're running this on the dict itself), we need to
|
||||
# correct for this.
|
||||
if (defined $word2prons{$word}) { $owncount = 1; } else { $owncount = 0; }
|
||||
|
||||
for ($prefix_len = $min_prefix_len; $prefix_len <= $len; $prefix_len++) {
|
||||
my $prefix = substr($word, 0, $prefix_len);
|
||||
my $suffix = substr($word, $prefix_len);
|
||||
if ($prefixcount{$prefix} - $owncount == 0) {
|
||||
# This prefix is not a prefix of any word in the dict, so no point
|
||||
# checking the rules below-- none of them can match.
|
||||
next;
|
||||
}
|
||||
$rules_array_ref = $suffix2rule{$suffix};
|
||||
if (defined $rules_array_ref) {
|
||||
foreach $R (@$rules_array_ref) { # @$rules_array_ref dereferences the array.
|
||||
# $R is a refernce to a 4-dimensional array, whose elements we access with
|
||||
# $$R[0], etc.
|
||||
my $base_suffix = $$R[1];
|
||||
my $base_word = $prefix . $base_suffix;
|
||||
my $base_prons_ref = $word2prons{$base_word};
|
||||
if (defined $base_prons_ref) {
|
||||
my $psuffix = $$R[2];
|
||||
my $base_psuffix = $$R[3];
|
||||
if ($base_psuffix ne "") {
|
||||
$base_psuffix = " " . $base_psuffix;
|
||||
# Include " ", the space between phones, to prevent
|
||||
# matching partial phones below.
|
||||
}
|
||||
my $base_psuffix_len = length($base_psuffix);
|
||||
foreach $base_pron (@$base_prons_ref) { # @$base_prons_ref derefs
|
||||
# that reference to an array.
|
||||
my $base_pron_prefix_len = length($base_pron) - $base_psuffix_len;
|
||||
# Note: these lengths are in characters, not phones.
|
||||
if ($base_pron_prefix_len >= 0 &&
|
||||
substr($base_pron, $base_pron_prefix_len) eq $base_psuffix) {
|
||||
# The suffix of the base_pron is what it should be.
|
||||
my $pron_prefix = substr($base_pron, 0, $base_pron_prefix_len);
|
||||
my $rule = join(",", @$R); # we'll output this..
|
||||
my $len = @R;
|
||||
for ($destress = 0; $destress <= 1; $destress++) { # Two versions
|
||||
# of each rule: with destressing and without.
|
||||
# pron is the generated pron.
|
||||
if ($destress) { $pron_prefix =~ s/2/1/g; }
|
||||
my $pron;
|
||||
if ($psuffix ne "") { $pron = $pron_prefix . " " . $psuffix; }
|
||||
else { $pron = $pron_prefix; }
|
||||
# Now print out the info about the generated pron.
|
||||
my $destress_mark = ($destress ? "yes" : "no");
|
||||
my $rule_score = $rule_and_destress_to_rule_score{$rule.";".$destress_mark};
|
||||
if (defined $rule_score) { # Means that the (rule,destress) combination was
|
||||
# seen [note: this if-statement may be pointless, as currently we don't
|
||||
# do any pruning of rules].
|
||||
my @output = ($word, $pron, $base_word, $base_pron, $rule, $destress_mark);
|
||||
if ($rule_score != -1) { push @output, $rule_score; } # If scores were supplied,
|
||||
# we also output the score info.
|
||||
print join(";", @output) . "\n";
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,73 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
#This reads in rules, of the form put out by get_rules.pl, e.g.:
|
||||
# ERT,,ER0 T,
|
||||
# MENT,ING,M AH0 N T,IH0 NG
|
||||
# S,TON,Z,T AH0 N
|
||||
# ,ER,IH0 NG,IH0 NG ER0
|
||||
# ,'S,M AH0 N,M AH0 N Z
|
||||
#TIONS,TIVE,SH AH0 N Z,T IH0 V
|
||||
|
||||
# and it works out a hierarchy that says which rules are sub-cases
|
||||
# of which rules: it outputs on each line a pair separated by ";", where
|
||||
# each member of the pair is a rule, first one is the specialization, the
|
||||
# second one being more general.
|
||||
# E.g.:
|
||||
# RED,RE,D,/ED,E,D,
|
||||
# RED,RE,D,/D,,D,
|
||||
# GING,GE,IH0 NG,/ING,I,IH0 NG,
|
||||
# TOR,TING,T ER0,T IH0 NG/OR,OR,T ER0,T ER0
|
||||
# ERED,ER,D,/RED,R,D,
|
||||
# ERED,ER,D,/ED,,D,
|
||||
|
||||
|
||||
|
||||
|
||||
while(<>) {
|
||||
chop;
|
||||
$rule = $_;
|
||||
$isrule{$rule} = 1;
|
||||
push @rules, $rule;
|
||||
}
|
||||
|
||||
foreach my $rule (@rules) {
|
||||
# Truncate the letters and phones in the rule, while we
|
||||
# can, to get more general rules; if the more general rule
|
||||
# exists, put out the pair.
|
||||
@A = split(",", $rule);
|
||||
@suffixa = split("", $A[0]);
|
||||
@suffixb = split("", $A[1]);
|
||||
@psuffixa = split(" ", $A[2]);
|
||||
@psuffixb = split(" ", $A[3]);
|
||||
for ($common_suffix_len = 0; $common_suffix_len < @suffixa && $common_suffix_len < @suffixb;) {
|
||||
if ($suffixa[$common_suffix_len] eq $suffixb[$common_suffix_len]) {
|
||||
$common_suffix_len++;
|
||||
} else {
|
||||
last;
|
||||
}
|
||||
}
|
||||
for ($common_psuffix_len = 0; $common_psuffix_len < @psuffixa && $common_psuffix_len < @psuffixb;) {
|
||||
if ($psuffixa[$common_psuffix_len] eq $psuffixb[$common_psuffix_len]) {
|
||||
$common_psuffix_len++;
|
||||
} else {
|
||||
last;
|
||||
}
|
||||
}
|
||||
# Get all combinations of pairs of integers <= (common_suffix_len, common_psuffix_len),
|
||||
# except (0,0), and print out this rule together with the corresponding rule (if it exists).
|
||||
for ($m = 0; $m <= $common_suffix_len; $m++) {
|
||||
$sa = join("", @suffixa[$m...$#suffixa]); # @x[a..b] is array slice notation.
|
||||
$sb = join("", @suffixb[$m...$#suffixb]);
|
||||
for ($n = 0; $n <= $common_psuffix_len; $n++) {
|
||||
if (!($m == 0 && $n == 0)) {
|
||||
$psa = join(" ", @psuffixa[$n...$#psuffixa]);
|
||||
$psb = join(" ", @psuffixb[$n...$#psuffixb]);
|
||||
$more_general_rule = join(",", ($sa, $sb, $psa, $psb));
|
||||
if (defined $isrule{$more_general_rule}) {
|
||||
print $rule . ";" . $more_general_rule . "\n";
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -0,0 +1,204 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# This program creates suggested suffix rules from a dictionary.
|
||||
# It outputs quadruples of the form:
|
||||
# suffix,base-suffix,psuffix,base-psuffix
|
||||
# where "suffix" is the suffix of the letters of a word, "base-suffix" is
|
||||
# the suffix of the letters of the base-word, "psuffix" is the suffix of the
|
||||
# pronunciation of the word (a space-separated list of phonemes), and
|
||||
# "base-psuffix" is the suffix of the pronunciation of the baseword.
|
||||
# As far as this program is concerned, there is no distinction between
|
||||
# "word" and "base-word". To simplify things slightly, what it does
|
||||
# is return all tuples (a,b,c,d) [with a != b] such that there are
|
||||
# at least $min_suffix_count instances in the dictionary of
|
||||
# a (word-prefix, pron-prefix) pair where there exists (word,pron)
|
||||
# pairs of the form
|
||||
# ( word-prefix . a, pron-prefix . c)
|
||||
# and
|
||||
# ( word-prefix . b, pron-prefix . d)
|
||||
# For example if (a,b,c,d) equals (USLY,US,S L IY0,S)
|
||||
# then this quadruple will be output as long as there at least
|
||||
# e.g. 30 instances of prefixes like (FAM, F EY1 M AH0)
|
||||
# where there exist (word, pron) pairs like:
|
||||
# FAMOUS, F EY1 M AH0 S
|
||||
# FAMOUSLY F EY1 M AH0 S L IY0
|
||||
#
|
||||
# There are some modifications to the picture above, for efficiency.
|
||||
# If $disallow_empty_suffix != 0, this program will not output 4-tuples where
|
||||
# the first element (the own-word suffix) is empty, as this would cause
|
||||
# efficiency problems in get_candidate_prons.pl. If
|
||||
# $ignore_prefix_stress != 0, this program will ignore stress markings
|
||||
# while evaluating whether prefixes are the same.
|
||||
# The minimum count for a quadruple to be output is $min_suffix_count
|
||||
# (e.g. 30).
|
||||
#
|
||||
# The function of this program is not to evaluate the accuracy of these rules;
|
||||
# it is mostly a pruning step, where we suggest rules that have large enough
|
||||
# counts to be suitable for our later procedure where we evaluate their
|
||||
# accuracy in predicting prons.
|
||||
|
||||
$disallow_empty_suffix = 1; # Disallow rules where the suffix of the "own-word" is
|
||||
# empty. This is for efficiency in later stages (e.g. get_candidate_prons.pl).
|
||||
$min_prefix_len = 3; # this must match with get_candidate_prons.pl
|
||||
$ignore_prefix_stress = 1; # or 0 to take account of stress in prefix.
|
||||
$min_suffix_count = 20;
|
||||
|
||||
# Takes in dictionary.
|
||||
|
||||
print STDERR "Reading dict\n";
|
||||
while(<>) {
|
||||
@A = split(" ", $_);
|
||||
my $word = shift @A;
|
||||
my $pron = join(" ", @A);
|
||||
if (!defined $prons{$word}) {
|
||||
$prons{$word} = $pron;
|
||||
push @words, $word;
|
||||
} else {
|
||||
$prons{$word} = $prons{$word} . ";" . $pron;
|
||||
}
|
||||
}
|
||||
|
||||
# Get common suffixes (e.g., count >100). Include empty suffix.
|
||||
|
||||
print STDERR "Getting common suffix counts.\n";
|
||||
{
|
||||
foreach $word (@words) {
|
||||
$len = length($word);
|
||||
for ($x = $min_prefix_len; $x <= $len; $x++) {
|
||||
$suffix_count{substr($word, $x)}++;
|
||||
}
|
||||
}
|
||||
|
||||
foreach $suffix (keys %suffix_count) {
|
||||
if ($suffix_count{$suffix} >= $min_suffix_count) {
|
||||
$newsuffix_count{$suffix} = $suffix_count{$suffix};
|
||||
}
|
||||
}
|
||||
%suffix_count = %newsuffix_count;
|
||||
undef %newsuffix_count;
|
||||
|
||||
foreach $suffix ( sort { $suffix_count{$b} <=> $suffix_count{$a} } keys %suffix_count ) {
|
||||
print STDERR "$suffix_count{$suffix} $suffix\n";
|
||||
}
|
||||
}
|
||||
|
||||
print STDERR "Getting common suffix pairs.\n";
|
||||
|
||||
{
|
||||
print STDERR " Getting map from prefix -> suffix-set.\n";
|
||||
|
||||
# Create map from prefix -> suffix-set.
|
||||
foreach $word (@words) {
|
||||
$len = length($word);
|
||||
for ($x = $min_prefix_len; $x <= $len; $x++) {
|
||||
$prefix = substr($word, 0, $x);
|
||||
$suffix = substr($word, $x);
|
||||
if (defined $suffix_count{$suffix}) { # Suffix is common...
|
||||
if (!defined $suffixes_of{$prefix}) {
|
||||
$suffixes_of{$prefix} = [ $suffix ]; # Create a reference to a new array with
|
||||
# one element.
|
||||
} else {
|
||||
push @{$suffixes_of{$prefix}}, $suffix; # Push $suffix onto array that the
|
||||
# hash member is a reference .
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
my %suffix_set_count;
|
||||
print STDERR " Getting map from suffix-set -> count.\n";
|
||||
while ( my ($key, $value) = each(%suffixes_of) ) {
|
||||
my @suffixes = sort ( @$value );
|
||||
$suffix_set_count{join(";", @suffixes)}++;
|
||||
}
|
||||
print STDERR " Getting counts for suffix pairs.\n";
|
||||
while ( my ($suffix_set, $count) = each (%suffix_set_count) ) {
|
||||
my @suffixes = split(";", $suffix_set);
|
||||
# Consider pairs to be ordered. This is more convenient
|
||||
# later on.
|
||||
foreach $suffix_a (@suffixes) {
|
||||
foreach $suffix_b (@suffixes) {
|
||||
if ($suffix_a ne $suffix_b) {
|
||||
$suffix_pair = $suffix_a . "," . $suffix_b;
|
||||
$suffix_pair_count{$suffix_pair} += $count;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# To save memory, only keep pairs above threshold in the hash.
|
||||
while ( my ($suffix_pair, $count) = each (%suffix_pair_count) ) {
|
||||
if ($count >= $min_suffix_count) {
|
||||
$new_hash{$suffix_pair} = $count;
|
||||
}
|
||||
}
|
||||
%suffix_pair_count = %new_hash;
|
||||
undef %new_hash;
|
||||
|
||||
# Print out the suffix pairs so the user can see.
|
||||
foreach $suffix_pair (
|
||||
sort { $suffix_pair_count{$b} <=> $suffix_pair_count{$a} } keys %suffix_pair_count ) {
|
||||
print STDERR "$suffix_pair_count{$suffix_pair} $suffix_pair\n";
|
||||
}
|
||||
}
|
||||
|
||||
print STDERR "Getting common suffix/suffix/psuffix/psuffix quadruples\n";
|
||||
|
||||
{
|
||||
while ( my ($prefix, $suffixes_ref) = each(%suffixes_of) ) {
|
||||
# Note: suffixes_ref is a reference to an array. We dereference with
|
||||
# @$suffixes_ref.
|
||||
# Consider each pair of suffixes (in each order).
|
||||
foreach my $suffix_a ( @$suffixes_ref ) {
|
||||
foreach my $suffix_b ( @$suffixes_ref ) {
|
||||
# could just used "defined" in next line, but this is for clarity.
|
||||
$suffix_pair = $suffix_a.",".$suffix_b;
|
||||
if ( $suffix_pair_count{$suffix_pair} >= $min_suffix_count ) {
|
||||
foreach $pron_a_str (split(";", $prons{$prefix.$suffix_a})) {
|
||||
@pron_a = split(" ", $pron_a_str);
|
||||
foreach $pron_b_str (split(";", $prons{$prefix.$suffix_b})) {
|
||||
@pron_b = split(" ", $pron_b_str);
|
||||
$len_a = @pron_a; # evaluating array as scalar automatically gives length.
|
||||
$len_b = @pron_b;
|
||||
for (my $pos = 0; $pos <= $len_a && $pos <= $len_b; $pos++) {
|
||||
# $pos is starting-pos of psuffix-pair.
|
||||
$psuffix_a = join(" ", @pron_a[$pos...$#pron_a]);
|
||||
$psuffix_b = join(" ", @pron_b[$pos...$#pron_b]);
|
||||
$quadruple = $suffix_pair . "," . $psuffix_a . "," . $psuffix_b;
|
||||
$quadruple_count{$quadruple}++;
|
||||
|
||||
my $pron_a_pos = $pron_a[$pos], $pron_b_pos = $pron_b[$pos];
|
||||
if ($ignore_prefix_stress) {
|
||||
$pron_a_pos =~ s/\d//; # e.g convert IH0 to IH. Only affects
|
||||
$pron_b_pos =~ s/\d//; # whether we exit the loop below.
|
||||
}
|
||||
if ($pron_a_pos ne $pron_b_pos) {
|
||||
# This is important: we don't consider a pron suffix-pair to be
|
||||
# valid unless the pron prefix is the same.
|
||||
last;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
# To save memory, only keep pairs above threshold in the hash.
|
||||
while ( my ($quadruple, $count) = each (%quadruple_count) ) {
|
||||
if ($count >= $min_suffix_count) {
|
||||
$new_hash{$quadruple} = $count;
|
||||
}
|
||||
}
|
||||
%quadruple_count = %new_hash;
|
||||
undef %new_hash;
|
||||
|
||||
# Print out the quadruples for diagnostics.
|
||||
foreach $quadruple (
|
||||
sort { $quadruple_count{$b} <=> $quadruple_count{$a} } keys %quadruple_count ) {
|
||||
print STDERR "$quadruple_count{$quadruple} $quadruple\n";
|
||||
}
|
||||
}
|
||||
# Now print out the quadruples; these are the output of this program.
|
||||
foreach $quadruple (keys %quadruple_count) {
|
||||
print $quadruple."\n";
|
||||
}
|
||||
|
|
@ -0,0 +1,103 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# This program enforces the rule that
|
||||
# if a "more specific" rule applies, we cannot use the more general rule.
|
||||
# It takes in tuples generated by get_candidate_prons (one per line, separated
|
||||
# by ";"), of the form:
|
||||
# word;pron;base-word;base-pron;rule-name;de-stress[;rule-score]
|
||||
# [note: we mean that the last element, the numeric score of the rule, is optional]
|
||||
# and it outputs a (generally shorter) list
|
||||
# of the same form.
|
||||
|
||||
|
||||
# For each word:
|
||||
# For each (base-word,base-pron):
|
||||
# Eliminate "more-general" rules as follows:
|
||||
# For each pair of rules applying to this (base-word, base-pron):
|
||||
# If pair is in more-general hash, disallow more general one.
|
||||
# Let the output be: for each (base-word, base-pron, rule):
|
||||
# for (destress-prefix) in [yes, no], do:
|
||||
# print out the word input, the rule-name, [destressed:yes|no], and the new pron.
|
||||
|
||||
|
||||
if (@ARGV != 1 && @ARGV != 2) {
|
||||
die "Usage: limit_candidate_prons.pl rule_hierarchy [candidate_prons] > limited_candidate_prons";
|
||||
}
|
||||
|
||||
$hierarchy = shift @ARGV;
|
||||
open(H, "<$hierarchy") || die "Opening rule hierarchy $hierarchy";
|
||||
|
||||
while(<H>) {
|
||||
chop;
|
||||
m:.+;.+: || die "Bad rule-hierarchy line $_";
|
||||
$hierarchy{$_} = 1; # Format is: if $rule1 is the string form of the more specific rule
|
||||
# and $rule21 is that string form of the more general rule, then $hierarchy{$rule1.";".$rule2}
|
||||
# is defined, else undefined.
|
||||
}
|
||||
|
||||
|
||||
sub process_word;
|
||||
|
||||
undef $cur_word;
|
||||
@cur_lines = ();
|
||||
|
||||
while(<>) {
|
||||
# input, output is:
|
||||
# word;pron;base-word;base-pron;rule-name;destress;score
|
||||
chop;
|
||||
m:^([^;]+);: || die "Unexpected input: $_";
|
||||
$word = $1;
|
||||
if (!defined $cur_word || $word eq $cur_word) {
|
||||
if (!defined $cur_word) { $cur_word = $word; }
|
||||
push @cur_lines, $_;
|
||||
} else {
|
||||
process_word(@cur_lines); # Process a series of suggested prons
|
||||
# for a particular word.
|
||||
$cur_word = $word;
|
||||
@cur_lines = ( $_ );
|
||||
}
|
||||
}
|
||||
process_word(@cur_lines);
|
||||
|
||||
sub process_word {
|
||||
my %pair2rule_list; # hash from $baseword.";".$baseword to ref
|
||||
# to array of [ line1, line2, ... ].
|
||||
my @cur_lines = @_;
|
||||
foreach my $line (@cur_lines) {
|
||||
my ($word, $pron, $baseword, $basepron, $rulename, $destress, $rule_score) = split(";", $line);
|
||||
my $key = $baseword.";".$basepron;
|
||||
if (defined $pair2rule_list{$key}) {
|
||||
push @{$pair2rule_list{$key}}, $line; # @{...} derefs the array pointed to
|
||||
# by the array ref inside {}.
|
||||
} else {
|
||||
$pair2rule_list{$key} = [ $line ]; # [ $x ] is new anonymous array with 1 elem ($x)
|
||||
}
|
||||
}
|
||||
while ( my ($key, $value) = each(%pair2rule_list) ) {
|
||||
my @lines = @$value; # array of lines that are for this (baseword,basepron).
|
||||
my @stress, @rules; # Arrays of stress markers and rule names, indexed by
|
||||
# same index that indexes @lines.
|
||||
for (my $n = 0; $n < @lines; $n++) {
|
||||
my $line = $lines[$n];
|
||||
my ($word, $pron, $baseword, $basepron, $rulename, $destress, $rule_score) = split(";", $line);
|
||||
$stress[$n] = $destress;
|
||||
$rules[$n] = $rulename;
|
||||
}
|
||||
for (my $m = 0; $m < @lines; $m++) {
|
||||
my $ok = 1; # if stays 1, this line is OK.
|
||||
for (my $n = 0; $n < @lines; $n++) {
|
||||
if ($m != $n && $stress[$m] eq $stress[$n]) {
|
||||
if (defined $hierarchy{$rules[$n].";".$rules[$m]}) {
|
||||
# Note: this "hierarchy" variable is defined if $rules[$n] is a more
|
||||
# specific instances of $rules[$m], thus invalidating $rules[$m].
|
||||
$ok = 0;
|
||||
last; # no point iterating further.
|
||||
}
|
||||
}
|
||||
}
|
||||
if ($ok != 0) {
|
||||
print $lines[$m] . "\n";
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,50 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# This takes the output of e.g. get_candidate_prons.pl or limit_candidate_prons.pl,
|
||||
# which is 7-tuples, one per line, of the form:
|
||||
|
||||
# word;pron;base-word;base-pron;rule-name;de-stress;rule-score
|
||||
# (where rule-score is somtimes listed as optional, but this
|
||||
# program does expect it, since we don't anticipate it being used
|
||||
# without it).
|
||||
# This program assumes that all the words and prons and rules have
|
||||
# come from a reversed dictionary (reverse_dict.pl) where the order
|
||||
# of the characters in the words, and the phones in the prons, have
|
||||
# been reversed, and it un-reverses them. That it, the characters
|
||||
# in "word" and "base-word", and the phones in "pron" and "base-pron"
|
||||
# are reversed; and the rule ("rule-name") is parsed as a 4-tuple,
|
||||
# like:
|
||||
# suffix,base-suffix,psuffix,base-psuffix
|
||||
# so this program reverses the characters in "suffix" and "base-suffix"
|
||||
# and the phones (separated by spaces) in "psuffix" and "base-psuffix".
|
||||
|
||||
sub reverse_str {
|
||||
$str = shift;
|
||||
return join("", reverse(split("", $str)));
|
||||
}
|
||||
sub reverse_pron {
|
||||
$str = shift;
|
||||
return join(" ", reverse(split(" ", $str)));
|
||||
}
|
||||
|
||||
while(<>){
|
||||
chop;
|
||||
@A = split(";", $_);
|
||||
@A == 7 || die "Bad input line $_: found $len fields, expected 7.";
|
||||
|
||||
($word,$pron,$baseword,$basepron,$rule,$destress,$score) = @A;
|
||||
$word = reverse_str($word);
|
||||
$pron = reverse_pron($pron);
|
||||
$baseword = reverse_str($baseword);
|
||||
$basepron = reverse_pron($basepron);
|
||||
@R = split(",", $rule, 4);
|
||||
@R == 4 || die "Bad rule $rule";
|
||||
|
||||
$R[0] = reverse_str($R[0]); # suffix.
|
||||
$R[1] = reverse_str($R[1]); # base-suffix.
|
||||
$R[2] = reverse_pron($R[2]); # pron.
|
||||
$R[3] = reverse_pron($R[3]); # base-pron.
|
||||
$rule = join(",", @R);
|
||||
@A = ($word,$pron,$baseword,$basepron,$rule,$destress,$score);
|
||||
print join(";", @A) . "\n";
|
||||
}
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# Used in conjunction with get_rules.pl
|
||||
# example input line: XANTHE Z AE1 N DH
|
||||
# example output line: EHTNAX DH N AE1 Z
|
||||
|
||||
while(<>){
|
||||
@A = split(" ", $_);
|
||||
$word = shift @A;
|
||||
$word = join("", reverse(split("", $word))); # Reverse letters of word.
|
||||
@A = reverse(@A); # Reverse phones in pron.
|
||||
unshift @A, $word;
|
||||
print join(" ", @A) . "\n";
|
||||
}
|
||||
|
|
@ -0,0 +1,50 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# This program takes candidate prons from "get_candidate_prons.pl" or
|
||||
# "limit_candidate_prons.pl", and a reference dictionary covering those words,
|
||||
# and outputs the same format but with scoring information added (so we go from
|
||||
# 6 to 7 fields). The scoring information says, for each generated pron,
|
||||
# whether we have a match, a partial match, or no match, to some word in the
|
||||
# dictionary. A partial match means it's correct except for stress.
|
||||
|
||||
# The input is a 6-tuple on each line, like:
|
||||
# word;pron;base-word;base-pron;rule-name;de-stress
|
||||
#
|
||||
# The output is the same except with one more field, the score,
|
||||
# which may be "right", "wrong", "partial".
|
||||
|
||||
if (@ARGV != 1 && @ARGV != 2) {
|
||||
die "Usage: score_prons.pl reference_dict [candidate_prons] > scored_candidate_prons";
|
||||
}
|
||||
|
||||
$dict = shift @ARGV;
|
||||
open(D, "<$dict") || die "Opening dictionary $dict";
|
||||
|
||||
while(<D>) { # Set up some hashes that tell us when
|
||||
# a (word,pron) pair is correct (and the same for
|
||||
# prons with stress information removed).
|
||||
chop;
|
||||
@A = split(" ", $_);
|
||||
$word = shift @A;
|
||||
$pron = join(" ", @A);
|
||||
$pron_nostress = $pron;
|
||||
$pron_nostress =~ s:\d::g;
|
||||
$word_and_pron{$word.";".$pron} = 1;
|
||||
$word_and_pron_nostress{$word.";".$pron_nostress} = 1;
|
||||
}
|
||||
|
||||
while(<>) {
|
||||
chop;
|
||||
$line = $_;
|
||||
my ($word, $pron, $baseword, $basepron, $rulename, $destress) = split(";", $line);
|
||||
$pron_nostress = $pron;
|
||||
$pron_nostress =~ s:\d::g;
|
||||
if (defined $word_and_pron{$word.";".$pron}) {
|
||||
$score = "right";
|
||||
} elsif (defined $word_and_pron_nostress{$word.";".$pron_nostress}) {
|
||||
$score = "partial";
|
||||
} else {
|
||||
$score = "wrong";
|
||||
}
|
||||
print $line.";".$score."\n";
|
||||
}
|
||||
|
|
@ -0,0 +1,52 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# This program takes the output of count_rules.pl, which is tuples
|
||||
# of the form
|
||||
#
|
||||
# rule;destress;right-count;partial-count;wrong-count
|
||||
#
|
||||
# and outputs lines of the form
|
||||
#
|
||||
# rule;de-stress;score
|
||||
#
|
||||
# where the score, between 0 and 1 (1 better), is
|
||||
# equal to:
|
||||
#
|
||||
# It forms a score between 0 and 1, of the form:
|
||||
# ((#correct) + $partial_score * (#partial)) / (#correct + #partial + #wrong + $ballast)
|
||||
#
|
||||
# where $partial_score (e.g. 0.8) is the score we assign to a "partial" match,
|
||||
# and $ballast is a small number, e.g. 1, that is treated like "extra" wrong scores, to penalize
|
||||
# rules with few observations.
|
||||
#
|
||||
# It outputs all rules that at are at least the
|
||||
|
||||
$ballast = 1;
|
||||
$partial_score = 0.8;
|
||||
$destress_penalty = 1.0e-05; # Give destressed rules a small
|
||||
# penalty vs. their no-destress counterparts, so if we
|
||||
# have to choose arbitrarily we won't destress (seems safer)>
|
||||
|
||||
for ($n = 1; $n <= 4; $n++) {
|
||||
if ($ARGV[0] eq "--ballast") {
|
||||
shift @ARGV;
|
||||
$ballast = shift @ARGV;
|
||||
}
|
||||
if ($ARGV[0] eq "--partial-score") {
|
||||
shift @ARGV;
|
||||
$partial_score = shift @ARGV;
|
||||
($partial_score >= 0.0 && $partial_score <= 1.0) || die "Invalid partial_score: $partial_score";
|
||||
}
|
||||
}
|
||||
|
||||
(@ARGV == 0 || @ARGV == 1) || die "Usage: score_rules.pl [--ballast ballast-count] [--partial-score partial-score] [input from count_rules.pl]";
|
||||
|
||||
while(<>) {
|
||||
@A = split(";", $_);
|
||||
@A == 5 || die "Bad input line; $_";
|
||||
($rule,$destress,$right_count,$partial_count,$wrong_count) = @A;
|
||||
$rule_score = ($right_count + $partial_score*$partial_count) /
|
||||
($right_count+$partial_count+$wrong_count+$ballast);
|
||||
if ($destress eq "yes") { $rule_score -= $destress_penalty; }
|
||||
print join(";", $rule, $destress, sprintf("%.5f", $rule_score)) . "\n";
|
||||
}
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
#!/usr/bin/perl
|
||||
|
||||
# This takes the output of e.g. get_candidate_prons.pl or limit_candidate_prons.pl
|
||||
# or reverse_candidates.pl, which is 7-tuples, one per line, of the form:
|
||||
#
|
||||
# word;pron;base-word;base-pron;rule-name;de-stress;rule-score
|
||||
#
|
||||
# and selects the most likely prons for the words based on rule
|
||||
# score. It outputs in the same format as the input (thus, it is
|
||||
# similar to limit_candidates.pl in its input and output format,
|
||||
# except it has a different way of selecting the prons to put out).
|
||||
#
|
||||
# This script will select the $max_prons best pronunciations for
|
||||
# each candidate word, subject to the constraint that no pron should
|
||||
# have a rule score worse than $min_rule_score.
|
||||
# It first merges the candidates by, if there are multiple candidates
|
||||
# generating the same pron, selecting the candidate that had the
|
||||
# best associated score. It then sorts the prons on score and
|
||||
# selects the n best prons (but doesn't print out candidates with
|
||||
# score beneath the threshold).
|
||||
|
||||
|
||||
$max_prons = 4;
|
||||
$min_rule_score = 0.35;
|
||||
|
||||
|
||||
for ($n = 1; $n <= 3; $n++) {
|
||||
if ($ARGV[0] eq "--max-prons") {
|
||||
shift @ARGV;
|
||||
$max_prons = shift @ARGV;
|
||||
}
|
||||
if ($ARGV[0] eq "--min-rule-score") {
|
||||
shift @ARGV;
|
||||
$min_rule_score = shift @ARGV;
|
||||
}
|
||||
}
|
||||
|
||||
if (@ARGV != 0 && @ARGV != 1) {
|
||||
die "Usage: select_candidates_prons.pl [candidate_prons] > selected_candidate_prons";
|
||||
}
|
||||
|
||||
sub process_word;
|
||||
|
||||
undef $cur_word;
|
||||
@cur_lines = ();
|
||||
|
||||
while(<>) {
|
||||
# input, output is:
|
||||
# word;pron;base-word;base-pron;rule-name;destress;score
|
||||
chop;
|
||||
m:^([^;]+);: || die "Unexpected input: $_";
|
||||
$word = $1;
|
||||
if (!defined $cur_word || $word eq $cur_word) {
|
||||
if (!defined $cur_word) { $cur_word = $word; }
|
||||
push @cur_lines, $_;
|
||||
} else {
|
||||
process_word(@cur_lines); # Process a series of suggested prons
|
||||
# for a particular word.
|
||||
$cur_word = $word;
|
||||
@cur_lines = ( $_ );
|
||||
}
|
||||
}
|
||||
process_word(@cur_lines);
|
||||
|
||||
|
||||
sub process_word {
|
||||
my %pron2rule_score; # hash from generated pron to rule score for that pron.
|
||||
my %pron2line; # hash from generated pron to best line for that pron.
|
||||
my @cur_lines = @_;
|
||||
foreach my $line (@cur_lines) {
|
||||
my ($word, $pron, $baseword, $basepron, $rulename, $destress, $rule_score) = split(";", $line);
|
||||
if (!defined $pron2rule_score{$pron} ||
|
||||
$rule_score > $pron2rule_score{$pron}) {
|
||||
$pron2rule_score{$pron} = $rule_score;
|
||||
$pron2line{$pron} = $line;
|
||||
}
|
||||
}
|
||||
my @prons = sort { $pron2rule_score{$b} <=> $pron2rule_score{$a} } keys %pron2rule_score;
|
||||
for (my $n = 0; $n < @prons && $n < $max_prons &&
|
||||
$pron2rule_score{$prons[$n]} >= $min_rule_score; $n++) {
|
||||
print $pron2line{$prons[$n]} . "\n";
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -0,0 +1,65 @@
|
|||
#!/usr/bin/perl
|
||||
# Copyright 2010-2011 Microsoft Corporation
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
|
||||
# This program takes on its standard input a list of utterance
|
||||
# id's, one for each line. (e.g. 4k0c030a is a an utterance id).
|
||||
# It takes as
|
||||
# Extracts from the dot files the transcripts for a given
|
||||
# dataset (represented by a file list).
|
||||
#
|
||||
|
||||
@ARGV == 1 || die "find_transcripts.pl dot_files_flist < utterance_ids > transcripts";
|
||||
$dot_flist = shift @ARGV;
|
||||
|
||||
open(L, "<$dot_flist") || die "Opening file list of dot files: $dot_flist\n";
|
||||
while(<L>){
|
||||
chop;
|
||||
m:\S+/(\w{6})00.dot: || die "Bad line in dot file list: $_";
|
||||
$spk = $1;
|
||||
$spk2dot{$spk} = $_;
|
||||
}
|
||||
|
||||
|
||||
|
||||
while(<STDIN>){
|
||||
chop;
|
||||
$uttid_orig = $_;
|
||||
$uttid = substr $uttid_orig, 0, 8;
|
||||
$uttid =~ m:(\w{6})\w\w: || die "Bad utterance id $_";
|
||||
$spk = $1;
|
||||
if($spk ne $curspk) {
|
||||
%utt2trans = { }; # Don't keep all the transcripts in memory...
|
||||
$curspk = $spk;
|
||||
$dotfile = $spk2dot{$spk};
|
||||
defined $dotfile || die "No dot file for speaker $spk\n";
|
||||
open(F, "<$dotfile") || die "Error opening dot file $dotfile\n";
|
||||
while(<F>) {
|
||||
$_ =~ m:(.+)\((\w{8})\)\s*$: || die "Bad line $_ in dot file $dotfile (line $.)\n";
|
||||
$trans = $1;
|
||||
$utt = $2;
|
||||
$utt2trans{$utt} = $trans;
|
||||
}
|
||||
}
|
||||
if(!defined $utt2trans{$uttid}) {
|
||||
print STDERR "No transcript for utterance $uttid (current dot file is $dotfile)\n";
|
||||
} else {
|
||||
print "$uttid_orig $utt2trans{$uttid}\n";
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
#!/usr/bin/perl
|
||||
# Copyright 2010-2011 Microsoft Corporation
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
|
||||
# This program takes on its standard input a list of utterance
|
||||
# id's, one for each line. (e.g. 4k0c030a is a an utterance id).
|
||||
# It takes as
|
||||
# Extracts from the dot files the transcripts for a given
|
||||
# dataset (represented by a file list).
|
||||
#
|
||||
|
||||
@ARGV == 1 || die "find_transcripts.pl dot_files_flist < utterance_ids > transcripts";
|
||||
$dot_flist = shift @ARGV;
|
||||
|
||||
open(L, "<$dot_flist") || die "Opening file list of dot files: $dot_flist\n";
|
||||
while(<L>){
|
||||
chop;
|
||||
m:\S+/(\w{6})00.dot: || die "Bad line in dot file list: $_";
|
||||
$spk = $1;
|
||||
$spk2dot{$spk} = $_;
|
||||
}
|
||||
|
||||
|
||||
|
||||
while(<STDIN>){
|
||||
chop;
|
||||
$uttid = $_;
|
||||
$uttid =~ m:(\w{6})\w\w: || die "Bad utterance id $_";
|
||||
$spk = $1;
|
||||
if($spk ne $curspk) {
|
||||
%utt2trans = { }; # Don't keep all the transcripts in memory...
|
||||
$curspk = $spk;
|
||||
$dotfile = $spk2dot{$spk};
|
||||
defined $dotfile || die "No dot file for speaker $spk\n";
|
||||
open(F, "<$dotfile") || die "Error opening dot file $dotfile\n";
|
||||
while(<F>) {
|
||||
$_ =~ m:(.+)\((\w{8})\)\s*$: || die "Bad line $_ in dot file $dotfile (line $.)\n";
|
||||
$trans = $1;
|
||||
$utt = $2;
|
||||
$utt2trans{$utt} = $trans;
|
||||
}
|
||||
}
|
||||
if(!defined $utt2trans{$uttid}) {
|
||||
print STDERR "No transcript for utterance $uttid (current dot file is $dotfile)\n";
|
||||
} else {
|
||||
print "$uttid $utt2trans{$uttid}\n";
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
#!/usr/bin/perl
|
||||
# Copyright 2010-2011 Microsoft Corporation
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
# takes in a file list with lines like
|
||||
# /mnt/matylda2/data/WSJ1/13-16.1/wsj1/si_dt_20/4k0/4k0c030a.wv1
|
||||
# and outputs an scp in kaldi format with lines like
|
||||
# 4k0c030a /mnt/matylda2/data/WSJ1/13-16.1/wsj1/si_dt_20/4k0/4k0c030a.wv1
|
||||
# (the first thing is the utterance-id, which is the same as the basename of the file.
|
||||
|
||||
|
||||
while(<>){
|
||||
m:^\S+/(\w+)\.[wW][vV]1$: || die "Bad line $_";
|
||||
$id = $1;
|
||||
$id =~ tr/A-Z/a-z/; # Necessary because of weirdness on disk 13-16.1 (uppercase filenames)
|
||||
print "$id $_";
|
||||
}
|
||||
|
||||
|
|
@ -0,0 +1,110 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Guoguo Chen)
|
||||
# Apache 2.0.
|
||||
|
||||
|
||||
if [ $# -ne 2 ]; then
|
||||
echo "Usage: local/generate_example_kws.sh <data-dir> <kws-data-dir>"
|
||||
echo " e.g.: local/generate_example_kws.sh data/test_eval92/ <data/kws>"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
datadir=$1;
|
||||
kwsdatadir=$2;
|
||||
text=$datadir/text;
|
||||
|
||||
mkdir -p $kwsdatadir;
|
||||
|
||||
# Generate keywords; we generate 20 unigram keywords with at least 20 counts,
|
||||
# 20 bigram keywords with at least 10 counts and 10 trigram keywords with at
|
||||
# least 5 counts.
|
||||
cat $text | perl -e '
|
||||
%unigram = ();
|
||||
%bigram = ();
|
||||
%trigram = ();
|
||||
while(<>) {
|
||||
chomp;
|
||||
@col=split(" ", $_);
|
||||
shift @col;
|
||||
for($i = 0; $i < @col; $i++) {
|
||||
# unigram case
|
||||
if (!defined($unigram{$col[$i]})) {
|
||||
$unigram{$col[$i]} = 0;
|
||||
}
|
||||
$unigram{$col[$i]}++;
|
||||
|
||||
# bigram case
|
||||
if ($i < @col-1) {
|
||||
$word = $col[$i] . " " . $col[$i+1];
|
||||
if (!defined($bigram{$word})) {
|
||||
$bigram{$word} = 0;
|
||||
}
|
||||
$bigram{$word}++;
|
||||
}
|
||||
|
||||
# trigram case
|
||||
if ($i < @col-2) {
|
||||
$word = $col[$i] . " " . $col[$i+1] . " " . $col[$i+2];
|
||||
if (!defined($trigram{$word})) {
|
||||
$trigram{$word} = 0;
|
||||
}
|
||||
$trigram{$word}++;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
$max_count = 100;
|
||||
$total = 20;
|
||||
$current = 0;
|
||||
$min_count = 20;
|
||||
while ($current < $total && $min_count <= $max_count) {
|
||||
foreach $x (keys %unigram) {
|
||||
if ($unigram{$x} == $min_count) {
|
||||
print "$x\n";
|
||||
$unigram{$x} = 0;
|
||||
$current++;
|
||||
}
|
||||
if ($current == $total) {
|
||||
last;
|
||||
}
|
||||
}
|
||||
$min_count++;
|
||||
}
|
||||
|
||||
$total = 20;
|
||||
$current = 0;
|
||||
$min_count = 4;
|
||||
while ($current < $total && $min_count <= $max_count) {
|
||||
foreach $x (keys %bigram) {
|
||||
if ($bigram{$x} == $min_count) {
|
||||
print "$x\n";
|
||||
$bigram{$x} = 0;
|
||||
$current++;
|
||||
}
|
||||
if ($current == $total) {
|
||||
last;
|
||||
}
|
||||
}
|
||||
$min_count++;
|
||||
}
|
||||
|
||||
$total = 10;
|
||||
$current = 0;
|
||||
$min_count = 3;
|
||||
while ($current < $total && $min_count <= $max_count) {
|
||||
foreach $x (keys %trigram) {
|
||||
if ($trigram{$x} == $min_count) {
|
||||
print "$x\n";
|
||||
$trigram{$x} = 0;
|
||||
$current++;
|
||||
}
|
||||
if ($current == $total) {
|
||||
last;
|
||||
}
|
||||
}
|
||||
$min_count++;
|
||||
}
|
||||
' > $kwsdatadir/raw_keywords.txt
|
||||
|
||||
echo "Keywords generation succeeded"
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Guoguo Chen)
|
||||
# Apache 2.0.
|
||||
|
||||
|
||||
if [ $# -ne 3 ]; then
|
||||
echo "Usage: local/kws_data_prep.sh <lang-dir> <data-dir> <kws-data-dir>"
|
||||
echo " e.g.: local/kws_data_prep.sh data/lang_test_bd_tgpr/ data/test_eval92/ data/kws/"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
langdir=$1;
|
||||
datadir=$2;
|
||||
kwsdatadir=$3;
|
||||
|
||||
mkdir -p $kwsdatadir;
|
||||
|
||||
# Create keyword id for each keyword
|
||||
cat $kwsdatadir/raw_keywords.txt | perl -e '
|
||||
$idx=1;
|
||||
while(<>) {
|
||||
chomp;
|
||||
printf "WSJ-%04d $_\n", $idx;
|
||||
$idx++;
|
||||
}' > $kwsdatadir/keywords.txt
|
||||
|
||||
# Map the keywords to integers; note that we remove the keywords that
|
||||
# are not in our $langdir/words.txt, as we won't find them anyway...
|
||||
cat $kwsdatadir/keywords.txt | \
|
||||
sym2int.pl --map-oov 0 -f 2- $langdir/words.txt | \
|
||||
grep -v " 0 " | grep -v " 0$" > $kwsdatadir/keywords.int
|
||||
|
||||
# Compile keywords into FSTs
|
||||
transcripts-to-fsts ark:$kwsdatadir/keywords.int ark:$kwsdatadir/keywords.fsts
|
||||
|
||||
# Create utterance id for each utterance; Note that by "utterance" here I mean
|
||||
# the keys that will appear in the lattice archive. You may have to modify here
|
||||
cat $datadir/wav.scp | \
|
||||
awk '{print $1}' | \
|
||||
sort | uniq | perl -e '
|
||||
$idx=1;
|
||||
while(<>) {
|
||||
chomp;
|
||||
print "$_ $idx\n";
|
||||
$idx++;
|
||||
}' > $kwsdatadir/utter_id
|
||||
|
||||
# Map utterance to the names that will appear in the rttm file. You have
|
||||
# to modify the commands below accoring to your rttm file. In the WSJ case
|
||||
# since each file is an utterance, we assume that the actual file names will
|
||||
# be the "names" in the rttm, so the utterance names map to themselves.
|
||||
cat $datadir/wav.scp | \
|
||||
awk '{print $1}' | \
|
||||
sort | uniq | perl -e '
|
||||
while(<>) {
|
||||
chomp;
|
||||
print "$_ $_\n";
|
||||
}' > $kwsdatadir/utter_map;
|
||||
echo "Kws data preparation succeeded"
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
#!/usr/bin/perl
|
||||
# Copyright 2010-2011 Microsoft Corporation
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
# This program takes as its standard input an .ndx file from the WSJ corpus that looks
|
||||
# like this:
|
||||
#;; File: tr_s_wv1.ndx, updated 04/26/94
|
||||
#;;
|
||||
#;; Index for WSJ0 SI-short Sennheiser training data
|
||||
#;; Data is read WSJ sentences, Sennheiser mic.
|
||||
#;; Contains 84 speakers X (~100 utts per speaker MIT/SRI and ~50 utts
|
||||
#;; per speaker TI) = 7236 utts
|
||||
#;;
|
||||
#11_1_1:wsj0/si_tr_s/01i/01ic0201.wv1
|
||||
#11_1_1:wsj0/si_tr_s/01i/01ic0202.wv1
|
||||
#11_1_1:wsj0/si_tr_s/01i/01ic0203.wv1
|
||||
|
||||
#and as command-line arguments it takes the names of the WSJ disk locations, e.g.:
|
||||
#/mnt/matylda2/data/WSJ0/11-1.1 /mnt/matylda2/data/WSJ0/11-10.1 ... etc.
|
||||
# It outputs a list of absolute pathnames (it does this by replacing e.g. 11_1_1 with
|
||||
# /mnt/matylda2/data/WSJ0/11-1.1.
|
||||
# It also does a slight fix because one of the WSJ disks (WSJ1/13-16.1) was distributed with
|
||||
# uppercase rather than lower case filenames.
|
||||
|
||||
foreach $fn (@ARGV) {
|
||||
$fn =~ m:.+/([0-9\.\-]+)/?$: || die "Bad command-line argument $fn\n";
|
||||
$disk_id=$1;
|
||||
$disk_id =~ tr/-\./__/; # replace - and . with - so 11-10.1 becomes 11_10_1
|
||||
$fn =~ s:/$::; # Remove final slash, just in case it is present.
|
||||
$disk2fn{$disk_id} = $fn;
|
||||
}
|
||||
|
||||
while(<STDIN>){
|
||||
if(m/^;/){ next; } # Comment. Ignore it.
|
||||
else {
|
||||
m/^([0-9_]+):\s*(\S+)$/ || die "Could not parse line $_";
|
||||
$disk=$1;
|
||||
if(!defined $disk2fn{$disk}) {
|
||||
die "Disk id $disk not found";
|
||||
}
|
||||
$filename = $2; # as a subdirectory of the distributed disk.
|
||||
if($disk eq "13_16_1" && `hostname` =~ m/fit.vutbr.cz/) {
|
||||
# The disk 13-16.1 has been uppercased for some reason, on the
|
||||
# BUT system. This is a fix specifically for that case.
|
||||
$filename =~ tr/a-z/A-Z/; # This disk contains all uppercase filenames. Why?
|
||||
}
|
||||
print "$disk2fn{$disk}/$filename\n";
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,69 @@
|
|||
#!/bin/bash
|
||||
|
||||
|
||||
stage=0
|
||||
train_stage=-100
|
||||
# This trains only unadapted (just cepstral mean normalized) features,
|
||||
# and uses various combinations of VTLN warping factor and time-warping
|
||||
# factor to artificially expand the amount of data.
|
||||
|
||||
. cmd.sh
|
||||
|
||||
. utils/parse_options.sh # to parse the --stage option, if given
|
||||
|
||||
[ $# != 0 ] && echo "Usage: local/run_4b.sh [--stage <stage> --train-stage <train-stage>]" && exit 1;
|
||||
|
||||
set -e
|
||||
|
||||
if [ $stage -le 0 ]; then
|
||||
# Create the training data.
|
||||
featdir=`pwd`/mfcc/nnet5b; mkdir -p $featdir
|
||||
fbank_conf=conf/fbank_40.conf
|
||||
echo "--num-mel-bins=40" > $fbank_conf
|
||||
steps/nnet2/get_perturbed_feats.sh --cmd "$train_cmd" \
|
||||
$fbank_conf $featdir exp/perturbed_fbanks_si284 data/train_si284 data/train_si284_perturbed_fbank &
|
||||
steps/nnet2/get_perturbed_feats.sh --cmd "$train_cmd" --feature-type mfcc \
|
||||
conf/mfcc.conf $featdir exp/perturbed_mfcc_si284 data/train_si284 data/train_si284_perturbed_mfcc &
|
||||
wait
|
||||
fi
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
|
||||
data/train_si284_perturbed_mfcc data/lang exp/tri4b exp/tri4b_ali_si284_perturbed_mfcc
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
steps/nnet2/train_block.sh --stage "$train_stage" \
|
||||
--cleanup false \
|
||||
--initial-learning-rate 0.01 --final-learning-rate 0.001 \
|
||||
--num-epochs 10 --num-epochs-extra 5 \
|
||||
--cmd "$decode_cmd" \
|
||||
--hidden-layer-dim 1536 \
|
||||
--num-block-layers 3 --num-normal-layers 3 \
|
||||
data/train_si284_perturbed_fbank data/lang exp/tri4b_ali_si284_perturbed_mfcc exp/nnet5b || exit 1
|
||||
fi
|
||||
|
||||
if [ $stage -le 3 ]; then # create testing fbank data.
|
||||
featdir=`pwd`/mfcc
|
||||
fbank_conf=conf/fbank_40.conf
|
||||
for x in test_eval92 test_eval93 test_dev93; do
|
||||
cp -rT data/$x data/${x}_fbank
|
||||
rm -r ${x}_fbank/split* || true
|
||||
steps/make_fbank.sh --fbank-config "$fbank_conf" --nj 8 \
|
||||
--cmd "$train_cmd" data/${x}_fbank exp/make_fbank/$x $featdir || exit 1;
|
||||
steps/compute_cmvn_stats.sh data/${x}_fbank exp/make_fbank/$x $featdir || exit 1;
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 4 ]; then
|
||||
steps/nnet2/decode.sh --cmd "$decode_cmd" --nj 10 \
|
||||
exp/tri4b/graph_bd_tgpr data/test_dev93_fbank exp/nnet5b/decode_bd_tgpr_dev93
|
||||
|
||||
steps/nnet2/decode.sh --cmd "$decode_cmd" --nj 8 \
|
||||
exp/tri4b/graph_bd_tgpr data/test_eval92_fbank exp/nnet5b/decode_bd_tgpr_eval92
|
||||
fi
|
||||
|
||||
|
||||
|
||||
exit 0;
|
||||
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
#!/bin/bash
|
||||
|
||||
# This is neural net training on top of adapted 40-dimensional features.
|
||||
#
|
||||
|
||||
. ./cmd.sh
|
||||
|
||||
(
|
||||
steps/nnet2/train_tanh.sh \
|
||||
--mix-up 8000 \
|
||||
--initial-learning-rate 0.01 --final-learning-rate 0.001 \
|
||||
--num-hidden-layers 4 --hidden-layer-dim 1024 \
|
||||
--cmd "$decode_cmd" \
|
||||
data/train_si284 data/lang exp/tri4b_ali_si284 exp/nnet5c || exit 1
|
||||
|
||||
steps/decode_nnet_cpu.sh --cmd "$decode_cmd" --nj 10 \
|
||||
--transform-dir exp/tri4b/decode_bd_tgpr_dev93 \
|
||||
exp/tri4b/graph_bd_tgpr data/test_dev93 exp/nnet5c/decode_bd_tgpr_dev93
|
||||
|
||||
steps/decode_nnet_cpu.sh --cmd "$decode_cmd" --nj 8 \
|
||||
--transform-dir exp/tri4b/decode_bd_tgpr_eval92 \
|
||||
exp/tri4b/graph_bd_tgpr data/test_eval92 exp/nnet5c/decode_bd_tgpr_eval92
|
||||
)
|
||||
|
||||
|
|
@ -0,0 +1,119 @@
|
|||
#!/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 "\nUSAGE: %s <corpus-directory>\n\n" `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_noisy.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_noisy.flist
|
||||
find $1/si_dt_05 -name '*.wav' | sort -u > dev_dt_05_noisy.flist
|
||||
|
||||
find $1/si_et_20 -name '*.wav' | sort -u > test_eval92_noisy.flist
|
||||
find $1/si_et_05 -name '*.wav' | sort -u > test_eval92_5k_noisy.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_noisy dev_dt_05_noisy dev_dt_20_noisy test_eval92_noisy test_eval92_5k_noisy; 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 | perl -e '
|
||||
while(<STDIN>) {
|
||||
@A=split(" ", $_);
|
||||
@B=split("/", $_);
|
||||
$abs_path_len=@B;
|
||||
$condition=$B[$abs_path_len-5];
|
||||
if ($condition eq "9dB") {$key_suffix=2;}
|
||||
elsif ($condition eq "6dB") {$key_suffix=3;}
|
||||
elsif ($condition eq "3dB") {$key_suffix=4;}
|
||||
elsif ($condition eq "0dB") {$key_suffix=5;}
|
||||
elsif ($condition eq "m3dB") {$key_suffix=6;}
|
||||
elsif ($condition eq "m6dB") {$key_suffix=7;}
|
||||
else {print STDERR "error condition $condition";}
|
||||
print $A[0].$key_suffix." ".$A[1]."\n";
|
||||
}
|
||||
' | sort -k1 > ${x}_wav.scp
|
||||
cat ${x}_wav.scp | awk '{print $1}' \
|
||||
| $local/find_noisy_transcripts.pl dot_files.flist > ${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_noisy dev_dt_05_noisy dev_dt_20_noisy test_eval92_noisy test_eval92_5k_noisy; 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 |\n", $1, $2);}' < ${x}_sph.scp \
|
||||
# > ${x}_wav.scp
|
||||
#done
|
||||
|
||||
# Make the utt2spk and spk2utt files.
|
||||
for x in train_si84_noisy dev_dt_05_noisy dev_dt_20_noisy test_eval92_noisy test_eval92_5k_noisy; do
|
||||
cat ${x}_wav.scp | awk '{print $1}' \
|
||||
| perl -ane 'chop; m:^...:; print "$_ $&\n";' > $x.utt2spk
|
||||
cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
|
||||
done
|
||||
|
||||
echo "Data preparation succeeded"
|
||||
|
|
@ -0,0 +1,59 @@
|
|||
#!/usr/bin/perl
|
||||
# Copyright 2010-2011 Microsoft Corporation
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
# This takes data from the standard input that's unnormalized transcripts in the format
|
||||
# 4k2c0308 Of course there isn\'t any guarantee the company will keep its hot hand [misc_noise]
|
||||
# 4k2c030a [loud_breath] And new hardware such as the set of personal computers I\. B\. M\. introduced last week can lead to unexpected changes in the software business [door_slam]
|
||||
# and outputs normalized transcripts.
|
||||
# c.f. /mnt/matylda2/data/WSJ0/11-10.1/wsj0/transcrp/doc/dot_spec.doc
|
||||
|
||||
@ARGV == 1 || die "usage: normalize_transcript.pl noise_word < transcript > transcript2";
|
||||
$noise_word = shift @ARGV;
|
||||
|
||||
while(<STDIN>) {
|
||||
$_ =~ m:^(\S+) (.+): || die "bad line $_";
|
||||
$utt = $1;
|
||||
$trans = $2;
|
||||
print "$utt";
|
||||
foreach $w (split (" ",$trans)) {
|
||||
$w =~ tr:a-z:A-Z:; # Upcase everything to match the CMU dictionary. .
|
||||
$w =~ s:\\::g; # Remove backslashes. We don't need the quoting.
|
||||
$w =~ s:^\%PERCENT$:PERCENT:; # Normalization for Nov'93 test transcripts.
|
||||
$w =~ s:^\.POINT$:POINT:; # Normalization for Nov'93 test transcripts.
|
||||
if($w =~ m:^\[\<\w+\]$: || # E.g. [<door_slam], this means a door slammed in the preceding word. Delete.
|
||||
$w =~ m:^\[\w+\>\]$: || # E.g. [door_slam>], this means a door slammed in the next word. Delete.
|
||||
$w =~ m:\[\w+/\]$: || # E.g. [phone_ring/], which indicates the start of this phenomenon.
|
||||
$w =~ m:\[\/\w+]$: || # E.g. [/phone_ring], which indicates the end of this phenomenon.
|
||||
$w eq "~" || # This is used to indicate truncation of an utterance. Not a word.
|
||||
$w eq ".") { # "." is used to indicate a pause. Silence is optional anyway so not much
|
||||
# point including this in the transcript.
|
||||
next; # we won't print this word.
|
||||
} elsif($w =~ m:\[\w+\]:) { # Other noises, e.g. [loud_breath].
|
||||
print " $noise_word";
|
||||
} elsif($w =~ m:^\<([\w\']+)\>$:) {
|
||||
# e.g. replace <and> with and. (the <> means verbal deletion of a word).. but it's pronounced.
|
||||
print " $1";
|
||||
} elsif($w eq "--DASH") {
|
||||
print " -DASH"; # This is a common issue; the CMU dictionary has it as -DASH.
|
||||
# } elsif($w =~ m:(.+)\-DASH$:) { # E.g. INCORPORATED-DASH... seems the DASH gets combined with previous word
|
||||
# print " $1 -DASH";
|
||||
} else {
|
||||
print " $w";
|
||||
}
|
||||
}
|
||||
print "\n";
|
||||
}
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
#!/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 "\nUSAGE: %s <corpus-directory>\n\n" `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\n", $1, $2);}' > ${x}_wav.scp
|
||||
cat ${x}_tmp.trans1 | awk '{printf("%s1 ", $1); for(i=2;i<=NF;i++) printf("%s ", $i); printf("\n");}' > ${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 |\n", $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 $1}' \
|
||||
| perl -ane 'chop; m:^...:; print "$_ $&\n";' > $x.utt2spk
|
||||
cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
|
||||
done
|
||||
|
||||
echo "Data preparation succeeded"
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
#!/bin/bash
|
||||
|
||||
. cmd.sh
|
||||
|
||||
mfccdir=mfcc
|
||||
|
||||
# Make "per-utterance" versions of the test sets where the speaker
|
||||
# information corresponds to utterances-- to demonstrate adaptation on
|
||||
# short utterances, particularly for basis fMLLR
|
||||
for x in test_eval92 test_eval93 test_dev93 ; do
|
||||
y=${x}_utt
|
||||
rm -r data/$y
|
||||
cp -r data/$x data/$y
|
||||
cat data/$x/utt2spk | awk '{print $1, $1;}' > data/$y/utt2spk;
|
||||
cp data/$y/utt2spk data/$y/spk2utt;
|
||||
steps/compute_cmvn_stats.sh data/$y exp/make_mfcc/$y $mfccdir || exit 1;
|
||||
done
|
||||
|
||||
|
||||
# basis fMLLR experiments.
|
||||
# First a baseline: decode per-utterance with normal fMLLR.
|
||||
steps/decode_fmllr.sh --nj 10 --cmd "$decode_cmd" \
|
||||
exp/tri3b/graph_tgpr data/test_dev93_utt exp/tri3b/decode_tgpr_dev93_utt || exit 1;
|
||||
steps/decode_fmllr.sh --nj 8 --cmd "$decode_cmd" \
|
||||
exp/tri3b/graph_tgpr data/test_eval92_utt exp/tri3b/decode_tgpr_eval92_utt || exit 1;
|
||||
|
||||
# get the fMLLR basis.
|
||||
steps/get_fmllr_basis.sh --cmd "$train_cmd" data/train_si84 data/lang exp/tri3b
|
||||
|
||||
# decoding tri3b with basis fMLLR
|
||||
steps/decode_basis_fmllr.sh --nj 10 --cmd "$decode_cmd" \
|
||||
exp/tri3b/graph_tgpr data/test_dev93 exp/tri3b/decode_tgpr_dev93_basis || exit 1;
|
||||
steps/decode_basis_fmllr.sh --nj 8 --cmd "$decode_cmd" \
|
||||
exp/tri3b/graph_tgpr data/test_eval92 exp/tri3b/decode_tgpr_eval92_basis || exit 1;
|
||||
|
||||
# The same, per-utterance.
|
||||
steps/decode_basis_fmllr.sh --nj 10 --cmd "$decode_cmd" \
|
||||
exp/tri3b/graph_tgpr data/test_dev93_utt exp/tri3b/decode_tgpr_dev93_basis_utt || exit 1;
|
||||
steps/decode_basis_fmllr.sh --nj 8 --cmd "$decode_cmd" \
|
||||
exp/tri3b/graph_tgpr data/test_eval92_utt exp/tri3b/decode_tgpr_eval92_basis_utt || exit 1;
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,181 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012-2013 Brno University of Technology (Author: Karel Vesely)
|
||||
# Apache 2.0
|
||||
|
||||
# In this recipe we build DNN in four stages:
|
||||
# 1) Data preparations : the fMLLR features are stored to disk
|
||||
# 2) RBM pre-training : in this unsupervised stage we train stack of RBMs, a good starting point for Cross-entropy trainig
|
||||
# 3) Frame-level cross-entropy training : in this stage the objective is to classify frames correctly.
|
||||
# 4) Sequence-criterion training : in this stage the objective is to classify the whole sequence correctly,
|
||||
# the idea is similar to the 'Discriminative training' in context of GMM-HMMs.
|
||||
|
||||
|
||||
. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
|
||||
## This relates to the queue.
|
||||
|
||||
. ./path.sh ## Source the tools/utils (import the queue.pl)
|
||||
|
||||
|
||||
|
||||
#false && \
|
||||
{
|
||||
gmmdir=exp/tri4b
|
||||
|
||||
###
|
||||
### Generate the alignments of dev93
|
||||
### (held-out set for Cross-entropy training)
|
||||
###
|
||||
steps/align_fmllr.sh --nj 10 --cmd "$train_cmd" \
|
||||
data/test_dev93 data/lang $gmmdir exp/tri4b_ali_dev93 || exit 1
|
||||
|
||||
###
|
||||
### Store the fMLLR features, so we can train on them easily
|
||||
###
|
||||
|
||||
# train si284
|
||||
# generate the features
|
||||
dir=data-fmllr-tri4b/train_si284
|
||||
steps/make_fmllr_feats.sh --nj 20 --cmd "$train_cmd" \
|
||||
--transform-dir exp/tri4b_ali_si284 \
|
||||
$dir data/train_si284 $gmmdir $dir/_log $dir/_data || exit 1
|
||||
|
||||
# eval92
|
||||
dir=data-fmllr-tri4b/test_eval92
|
||||
steps/make_fmllr_feats.sh --nj 8 --cmd "$train_cmd" \
|
||||
--transform-dir exp/tri4b/decode_tgpr_eval92 \
|
||||
$dir data/test_eval92 $gmmdir $dir/_log $dir/_data || exit 1
|
||||
|
||||
# dev93 (unsupervised fMLLR)
|
||||
# held-out set of Cross-entropy training
|
||||
dir=data-fmllr-tri4b/test_dev93
|
||||
steps/make_fmllr_feats.sh --nj 10 --cmd "$train_cmd" \
|
||||
--transform-dir exp/tri4b/decode_tgpr_dev93 \
|
||||
$dir data/test_dev93 $gmmdir $dir/_log $dir/_data || exit 1
|
||||
}
|
||||
|
||||
|
||||
|
||||
###
|
||||
### Now we can pre-train stack of RBMs
|
||||
###
|
||||
#false && \
|
||||
{ # Pre-train the DBN
|
||||
dir=exp/tri4b_pretrain-dbn
|
||||
(tail --pid=$$ -F $dir/_pretrain_dbn.log 2>/dev/null)&
|
||||
$cuda_cmd $dir/_pretrain_dbn.log \
|
||||
steps/pretrain_dbn.sh --rbm-iter 3 data-fmllr-tri4b/train_si284 $dir
|
||||
}
|
||||
|
||||
|
||||
|
||||
###
|
||||
### Now we train the DNN optimizing cross-entropy.
|
||||
### This will take quite some time.
|
||||
###
|
||||
|
||||
#false && \
|
||||
{ # Train the MLP
|
||||
dir=exp/tri4b_pretrain-dbn_dnn
|
||||
ali=exp/tri4b_ali
|
||||
feature_transform=exp/tri4b_pretrain-dbn/final.feature_transform
|
||||
dbn=exp/tri4b_pretrain-dbn/6.dbn
|
||||
(tail --pid=$$ -F $dir/_train_nnet.log 2>/dev/null)&
|
||||
$cuda_cmd $dir/_train_nnet.log \
|
||||
steps/train_nnet.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 \
|
||||
data-fmllr-tri4b/train_si284 data-fmllr-tri4b/test_dev93 data/lang ${ali}_si284 ${ali}_dev93 $dir || exit 1;
|
||||
# decode with 'big-dictionary' (reuse HCLG graph)
|
||||
steps/decode_nnet.sh --nj 10 --cmd "$decode_cmd" --acwt 0.10 --config conf/decode_dnn.config \
|
||||
exp/tri4b/graph_bd_tgpr data-fmllr-tri4b/test_dev93 $dir/decode_bd_tgpr_dev93 || exit 1;
|
||||
steps/decode_nnet.sh --nj 8 --cmd "$decode_cmd" --acwt 0.10 --config conf/decode_dnn.config \
|
||||
exp/tri4b/graph_bd_tgpr data-fmllr-tri4b/test_eval92 $dir/decode_bd_tgpr_eval92 || exit 1;
|
||||
}
|
||||
|
||||
|
||||
|
||||
###
|
||||
### Finally we train using sMBR criterion.
|
||||
### We do Stochastic-GD with per-utterance updates.
|
||||
###
|
||||
### To get faster convergence, we will re-generate
|
||||
### the lattices after 1st epoch of sMBR.
|
||||
###
|
||||
|
||||
dir=exp/tri4b_pretrain-dbn_dnn_smbr
|
||||
srcdir=exp/tri4b_pretrain-dbn_dnn
|
||||
acwt=0.10
|
||||
|
||||
# First we need to generate lattices and alignments:
|
||||
#false && \
|
||||
{
|
||||
steps/align_nnet.sh --nj 100 --cmd "$train_cmd" \
|
||||
data-fmllr-tri4b/train_si284 data/lang $srcdir ${srcdir}_ali_si284 || exit 1;
|
||||
steps/make_denlats_nnet.sh --nj 100 --cmd "$decode_cmd" \
|
||||
--config conf/decode_dnn.config --acwt $acwt \
|
||||
data-fmllr-tri4b/train_si284 data/lang $srcdir ${srcdir}_denlats_si284 || exit 1;
|
||||
}
|
||||
# Now we re-train the hybrid by single iteration of sMBR
|
||||
#false && \
|
||||
{
|
||||
steps/train_nnet_mpe.sh --cmd "$cuda_cmd" --num-iters 1 --acwt $acwt --do-smbr true \
|
||||
data-fmllr-tri4b/train_si284 data/lang $srcdir \
|
||||
${srcdir}_ali_si284 ${srcdir}_denlats_si284 $dir || exit 1
|
||||
}
|
||||
# Decode
|
||||
#false && \
|
||||
{
|
||||
for ITER in 1; do
|
||||
# decode dev93 with big dict graph_bd_tgpr
|
||||
steps/decode_nnet.sh --nj 10 --cmd "$decode_cmd" --config conf/decode_dnn.config \
|
||||
--nnet $dir/${ITER}.nnet --acwt $acwt \
|
||||
exp/tri4b/graph_bd_tgpr data-fmllr-tri4b/test_dev93 $dir/decode_dev93_bd_tgpr_it${ITER} || exit 1
|
||||
# decode eval92 with big dict graph_bd_tgpr
|
||||
steps/decode_nnet.sh --nj 8 --cmd "$decode_cmd" --config conf/decode_dnn.config \
|
||||
--nnet $dir/${ITER}.nnet --acwt $acwt \
|
||||
exp/tri4b/graph_bd_tgpr data-fmllr-tri4b/test_eval92 $dir/decode_eval92_bd_tgpr_it${ITER} || exit 1
|
||||
done
|
||||
}
|
||||
|
||||
|
||||
###
|
||||
### Re-generate lattices and run several more iterations of sMBR
|
||||
###
|
||||
|
||||
dir=exp/tri4b_pretrain-dbn_dnn_smbr_iter1-lats
|
||||
srcdir=exp/tri4b_pretrain-dbn_dnn_smbr
|
||||
acwt=0.10
|
||||
|
||||
# First we need to generate lattices and alignments:
|
||||
#false && \
|
||||
{
|
||||
steps/align_nnet.sh --nj 100 --cmd "$train_cmd" \
|
||||
data-fmllr-tri4b/train_si284 data/lang $srcdir ${srcdir}_ali_si284 || exit 1;
|
||||
steps/make_denlats_nnet.sh --nj 100 --cmd "$decode_cmd" \
|
||||
--config conf/decode_dnn.config --acwt $acwt \
|
||||
data-fmllr-tri4b/train_si284 data/lang $srcdir ${srcdir}_denlats_si284 || exit 1;
|
||||
}
|
||||
# Now we re-train the hybrid by several iterations of sMBR
|
||||
#false && \
|
||||
{
|
||||
steps/train_nnet_mpe.sh --cmd "$cuda_cmd" --num-iters 4 --acwt $acwt --do-smbr true \
|
||||
data-fmllr-tri4b/train_si284 data/lang $srcdir \
|
||||
${srcdir}_ali_si284 ${srcdir}_denlats_si284 $dir
|
||||
}
|
||||
# Decode
|
||||
#false && \
|
||||
{
|
||||
for ITER in 1 2 3 4; do
|
||||
# decode dev93 with big dict graph_bd_tgpr
|
||||
steps/decode_nnet.sh --nj 10 --cmd "$decode_cmd" --config conf/decode_dnn.config \
|
||||
--nnet $dir/${ITER}.nnet --acwt $acwt \
|
||||
exp/tri4b/graph_bd_tgpr data-fmllr-tri4b/test_dev93 $dir/decode_dev93_bd_tgpr_it${ITER} || exit 1
|
||||
# decode eval92 with big dict graph_bd_tgpr
|
||||
steps/decode_nnet.sh --nj 8 --cmd "$decode_cmd" --config conf/decode_dnn.config \
|
||||
--nnet $dir/${ITER}.nnet --acwt $acwt \
|
||||
exp/tri4b/graph_bd_tgpr data-fmllr-tri4b/test_eval92 $dir/decode_eval92_bd_tgpr_it${ITER} || exit 1
|
||||
done
|
||||
}
|
||||
|
||||
|
||||
# Getting results [see RESULTS file]
|
||||
# for x in exp/*/decode*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done
|
||||
|
|
@ -0,0 +1,41 @@
|
|||
#prepare reverse lexicon and language model for backwards decoding
|
||||
utils/prepare_lang.sh --reverse true data/local/dict "<SPOKEN_NOISE>" data/local/lang_tmp.reverse data/lang.reverse || exit 1;
|
||||
utils/reverse_lm.sh data/local/nist_lm/lm_bg_5k.arpa.gz data/lang.reverse data/lang_test_bg_5k.reverse || exit 1;
|
||||
utils/reverse_lm_test.sh data/lang_test_bg_5k data/lang_test_bg_5k.reverse || exit 1;
|
||||
|
||||
# normal forward decoding
|
||||
utils/mkgraph.sh data/lang_test_bg_5k exp/tri2a exp/tri2a/graph_bg5k
|
||||
steps/decode_fwdbwd.sh --beam 10.0 --latbeam 4.0 --nj 8 --cmd "$decode_cmd" \
|
||||
exp/tri2a/graph_bg5k data/test_eval92 exp/tri2a/decode_eval92_bg5k_10 || exit 1;
|
||||
|
||||
# backward decoding
|
||||
utils/mkgraph.sh --reverse data/lang_test_bg_5k.reverse exp/tri2a exp/tri2a/graph_bg5k_r
|
||||
steps/decode_fwdbwd.sh --beam 10.0 --latbeam 4.0 --reverse true --nj 8 --cmd "$decode_cmd" \
|
||||
exp/tri2a/graph_bg5k_r data/test_eval92 exp/tri2a/decode_eval92_bg5k_reverse10 || exit 1;
|
||||
|
||||
# pingpong decoding
|
||||
steps/decode_fwdbwd.sh --beam 10.0 --max-beam 20.0 --reverse true --nj 8 --cmd "$decode_cmd" \
|
||||
--first_pass exp/tri2a/decode_eval92_bg5k_10 exp/tri2a/graph_bg5k_r data/test_eval92 exp/tri2a/decode_eval92_bg5k_pingpong10 || exit 1;
|
||||
steps/decode_fwdbwd.sh --beam 10.0 --max-beam 20.0 --nj 8 --cmd "$decode_cmd" \
|
||||
--first_pass exp/tri2a/decode_eval92_bg5k_reverse10 exp/tri2a/graph_bg5k data/test_eval92 exp/tri2a/decode_eval92_bg5k_pongping10 || exit 1;
|
||||
|
||||
# same for bigger language models (on machine with 8GB RAM, you can run the whole decoding in 3-4 min without SGE)
|
||||
utils/prepare_lang.sh --reverse true data/local/dict_larger "<SPOKEN_NOISE>" data/local/lang_larger.reverse data/lang_bd.reverse || exit;
|
||||
utils/reverse_lm.sh --lexicon data/local/dict_larger/lexicon.txt data/local/local_lm/3gram-mincount/lm_pr6.0.gz data/lang_bd.reverse data/lang_test_bd_tgpr.reverse || exit 1;
|
||||
utils/reverse_lm_test.sh data/lang_test_bd_tgpr data/lang_test_bd_tgpr.reverse || exit 1;
|
||||
|
||||
utils/mkgraph.sh data/lang_test_bd_tgpr exp/tri2a exp/tri2a/graph_bd_tgpr
|
||||
steps/decode_fwdbwd.sh --beam 10.0 --latbeam 4.0 --nj 4 --cmd run.pl \
|
||||
exp/tri2a/graph_bd_tgpr data/test_eval92 exp/tri2a/decode_eval92_bdtgpr4_10 || exit 1;
|
||||
|
||||
utils/mkgraph.sh --reverse data/lang_test_bd_tgpr.reverse exp/tri2a exp/tri2a/graph_bd_tgpr_r
|
||||
steps/decode_fwdbwd.sh --beam 10.0 --latbeam 4.0 --reverse true --nj 4 --cmd run.pl \
|
||||
exp/tri2a/graph_bd_tgpr_r data/test_eval92 exp/tri2a/decode_eval92_bdtgpr4_reverse10 || exit 1;
|
||||
|
||||
steps/decode_fwdbwd.sh --beam 10.0 --max-beam 20.0 --reverse true --nj 4 --cmd run.pl \
|
||||
--first_pass exp/tri2a/decode_eval92_bdtgpr4_10 exp/tri2a/graph_bd_tgpr_r data/test_eval92 \
|
||||
exp/tri2a/decode_eval92_bdtgpr4_pingpong10 || exit 1;
|
||||
|
||||
steps/decode_fwdbwd.sh --beam 10.0 --max-beam 20.0 --nj 4 --cmd run.pl \
|
||||
--first_pass exp/tri2a/decode_eval92_bdtgpr4_reverse10 exp/tri2a/graph_bd_tgpr data/test_eval92 \
|
||||
exp/tri2a/decode_eval92_bdtgpr4_pongping10 || exit 1;
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
#!/bin/bash
|
||||
|
||||
. ./cmd.sh
|
||||
|
||||
# Train and test MMI (and boosted MMI) on tri2b system.
|
||||
steps/make_denlats.sh --sub-split 20 --nj 10 --cmd "$train_cmd" \
|
||||
data/train_si84 data/lang exp/tri2b exp/tri2b_denlats_si84 || exit 1;
|
||||
|
||||
# train the basic MMI system.
|
||||
steps/train_mmi.sh --cmd "$train_cmd" \
|
||||
data/train_si84 data/lang exp/tri2b_ali_si84 \
|
||||
exp/tri2b_denlats_si84 exp/tri2b_mmi || exit 1;
|
||||
for iter in 3 4; do
|
||||
steps/decode_si.sh --nj 10 --cmd "$decode_cmd" --iter $iter \
|
||||
exp/tri2b/graph_tgpr data/test_dev93 exp/tri2b_mmi/decode_tgpr_dev93_it$iter &
|
||||
steps/decode_si.sh --nj 8 --cmd "$decode_cmd" --iter $iter \
|
||||
exp/tri2b/graph_tgpr data/test_eval92 exp/tri2b_mmi/decode_tgpr_eval92_it$iter &
|
||||
done
|
||||
|
||||
# MMI with 0.1 boosting factor.
|
||||
steps/train_mmi.sh --cmd "$train_cmd" --boost 0.1 \
|
||||
data/train_si84 data/lang exp/tri2b_ali_si84 exp/tri2b_denlats_si84 \
|
||||
exp/tri2b_mmi_b0.1 || exit 1;
|
||||
|
||||
for iter in 3 4; do
|
||||
steps/decode_si.sh --nj 10 --cmd "$decode_cmd" --iter $iter \
|
||||
exp/tri2b/graph_tgpr data/test_dev93 exp/tri2b_mmi_b0.1/decode_tgpr_dev93_it$iter &
|
||||
steps/decode_si.sh --nj 8 --cmd "$decode_cmd" --iter $iter \
|
||||
exp/tri2b/graph_tgpr data/test_eval92 exp/tri2b_mmi_b0.1/decode_tgpr_eval92_it$iter &
|
||||
done
|
||||
|
||||
|
||||
# Train a UBM with 400 components, for fMMI.
|
||||
steps/train_diag_ubm.sh --silence-weight 0.5 --nj 10 --cmd "$train_cmd" \
|
||||
400 data/train_si84 data/lang exp/tri2b_ali_si84 exp/dubm2b
|
||||
|
||||
steps/train_mmi_fmmi.sh --boost 0.1 --cmd "$train_cmd" \
|
||||
data/train_si84 data/lang exp/tri2b_ali_si84 exp/dubm2b exp/tri2b_denlats_si84 \
|
||||
exp/tri2b_fmmi_b0.1
|
||||
|
||||
for iter in `seq 3 8`; do
|
||||
steps/decode_fmmi.sh --nj 10 --cmd "$decode_cmd" --iter $iter \
|
||||
exp/tri2b/graph_tgpr data/test_dev93 exp/tri2b_fmmi_b0.1/decode_tgpr_dev93_it$iter &
|
||||
done
|
||||
|
||||
steps/train_mmi_fmmi.sh --learning-rate 0.005 --boost 0.1 --cmd "$train_cmd" \
|
||||
data/train_si84 data/lang exp/tri2b_ali_si84 exp/dubm2b exp/tri2b_denlats_si84 \
|
||||
exp/tri2b_fmmi_b0.1_lr0.005 || exit 1;
|
||||
for iter in `seq 3 8`; do
|
||||
steps/decode_fmmi.sh --nj 10 --cmd "$decode_cmd" --iter $iter \
|
||||
exp/tri2b/graph_tgpr data/test_dev93 exp/tri2b_fmmi_b0.1_lr0.005/decode_tgpr_dev93_it$iter &
|
||||
done
|
||||
|
||||
steps/train_mmi_fmmi_indirect.sh --boost 0.1 --cmd "$train_cmd" \
|
||||
data/train_si84 data/lang exp/tri2b_ali_si84 exp/dubm2b exp/tri2b_denlats_si84 \
|
||||
exp/tri2b_fmmi_indirect_b0.1
|
||||
for iter in `seq 3 8`; do
|
||||
steps/decode_fmmi.sh --nj 10 --cmd "$decode_cmd" --iter $iter \
|
||||
exp/tri2b/graph_tgpr data/test_dev93 exp/tri2b_fmmi_indirect_b0.1/decode_tgpr_dev93_it$iter &
|
||||
done
|
||||
|
|
@ -0,0 +1,50 @@
|
|||
#!/bin/bash
|
||||
. ./cmd.sh
|
||||
|
||||
steps/make_denlats.sh --nj 30 --sub-split 30 --cmd "$train_cmd" \
|
||||
--transform-dir exp/tri4b_ali_si284 \
|
||||
data/train_si284 data/lang exp/tri4b exp/tri4b_denlats_si284 || exit 1;
|
||||
|
||||
steps/train_mmi.sh --cmd "$train_cmd" --boost 0.1 \
|
||||
data/train_si284 data/lang exp/tri4b_ali_si284 exp/tri4b_denlats_si284 \
|
||||
exp/tri4b_mmi_b0.1 || exit 1;
|
||||
|
||||
steps/decode.sh --nj 10 --cmd "$decode_cmd" --transform-dir exp/tri3b/decode_tgpr_dev93 \
|
||||
exp/tri4b/graph_tgpr data/test_dev93 exp/tri4b_mmi_b0.1/decode_tgpr_dev93
|
||||
|
||||
#first, train UBM for fMMI experiments.
|
||||
steps/train_diag_ubm.sh --silence-weight 0.5 --nj 30 --cmd "$train_cmd" \
|
||||
600 data/train_si284 data/lang exp/tri4b_ali_si284 exp/dubm4b
|
||||
|
||||
# Next, fMMI+MMI.
|
||||
steps/train_mmi_fmmi.sh \
|
||||
--boost 0.1 --cmd "$train_cmd" data/train_si284 data/lang exp/tri4b_ali_si284 exp/dubm4b exp/tri4b_denlats_si284 \
|
||||
exp/tri4b_fmmi_a || exit 1;
|
||||
|
||||
for iter in 3 4 5 6 7 8; do
|
||||
steps/decode_fmmi.sh --nj 10 --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri3b/decode_tgpr_dev93 exp/tri4b/graph_tgpr data/test_dev93 \
|
||||
exp/tri4b_fmmi_a/decode_tgpr_dev93_it$iter &
|
||||
done
|
||||
# decode the last iter with the bd model.
|
||||
for iter in 8; do
|
||||
steps/decode_fmmi.sh --nj 10 --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri3b/decode_bd_tgpr_dev93 exp/tri4b/graph_bd_tgpr data/test_dev93 \
|
||||
exp/tri4b_fmmi_a/decode_bd_tgpr_dev93_it$iter &
|
||||
steps/decode_fmmi.sh --nj 8 --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri3b/decode_bd_tgpr_eval92 exp/tri4b/graph_bd_tgpr data/test_eval92 \
|
||||
exp/tri4b_fmmi_a/decode_tgpr_eval92_it$iter &
|
||||
done
|
||||
|
||||
|
||||
# fMMI + mmi with indirect differential.
|
||||
steps/train_mmi_fmmi_indirect.sh \
|
||||
--boost 0.1 --cmd "$train_cmd" data/train_si284 data/lang exp/tri4b_ali_si284 exp/dubm4b exp/tri4b_denlats_si284 \
|
||||
exp/tri4b_fmmi_indirect || exit 1;
|
||||
|
||||
for iter in 3 4 5 6 7 8; do
|
||||
steps/decode_fmmi.sh --nj 10 --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri3b/decode_tgpr_dev93 exp/tri4b/graph_tgpr data/test_dev93 \
|
||||
exp/tri4b_fmmi_indirect/decode_tgpr_dev93_it$iter &
|
||||
done
|
||||
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
#!/bin/bash
|
||||
|
||||
. ./cmd.sh
|
||||
|
||||
|
||||
# ...
|
||||
|
||||
local/nnet2/run_5c.sh
|
||||
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
#!/bin/bash
|
||||
|
||||
|
||||
steps/align_raw_fmllr.sh --nj 10 --cmd "$train_cmd" --use-graphs true \
|
||||
data/train_si84 data/lang exp/tri2b exp/tri2b_ali_si84_raw
|
||||
|
||||
steps/train_raw_sat.sh --cmd "$train_cmd" \
|
||||
2500 15000 data/train_si84 data/lang exp/tri2b_ali_si84_raw exp/tri3c || exit 1;
|
||||
|
||||
|
||||
mfccdir=mfcc
|
||||
for x in test_eval92 test_eval93 test_dev93 ; do
|
||||
y=${x}_utt
|
||||
cp -rT data/$x data/$y
|
||||
cat data/$x/utt2spk | awk '{print $1, $1;}' > data/$y/utt2spk;
|
||||
cp data/$y/utt2spk data/$y/spk2utt;
|
||||
steps/compute_cmvn_stats.sh data/$y exp/make_mfcc/$y $mfccdir || exit 1;
|
||||
done
|
||||
|
||||
(
|
||||
utils/mkgraph.sh data/lang_test_tgpr exp/tri3c exp/tri3c/graph_tgpr || exit 1;
|
||||
steps/decode_raw_fmllr.sh --nj 10 --cmd "$decode_cmd" \
|
||||
exp/tri3c/graph_tgpr data/test_dev93 exp/tri3c/decode_tgpr_dev93 || exit 1;
|
||||
steps/decode_raw_fmllr.sh --nj 8 --cmd "$decode_cmd" \
|
||||
exp/tri3c/graph_tgpr data/test_eval92 exp/tri3c/decode_tgpr_eval92 || exit 1;
|
||||
|
||||
steps/decode_raw_fmllr.sh --nj 30 --cmd "$decode_cmd" \
|
||||
exp/tri3c/graph_tgpr data/test_dev93_utt exp/tri3c/decode_tgpr_dev93_utt || exit 1;
|
||||
steps/decode_raw_fmllr.sh --nj 30 --cmd "$decode_cmd" \
|
||||
exp/tri3c/graph_tgpr data/test_eval92_utt exp/tri3c/decode_tgpr_eval92_utt || exit 1;
|
||||
|
||||
steps/decode_raw_fmllr.sh --use-normal-fmllr true --nj 10 --cmd "$decode_cmd" \
|
||||
exp/tri3c/graph_tgpr data/test_dev93 exp/tri3c/decode_tgpr_dev93_2fmllr || exit 1;
|
||||
steps/decode_raw_fmllr.sh --use-normal-fmllr true --nj 8 --cmd "$decode_cmd" \
|
||||
exp/tri3c/graph_tgpr data/test_eval92 exp/tri3c/decode_tgpr_eval92_2fmllr || exit 1;
|
||||
)&
|
||||
|
||||
(
|
||||
utils/mkgraph.sh data/lang_test_bd_tgpr exp/tri3c exp/tri3c/graph_bd_tgpr || exit 1;
|
||||
|
||||
steps/decode_raw_fmllr.sh --cmd "$decode_cmd" --nj 8 exp/tri3c/graph_bd_tgpr \
|
||||
data/test_eval92 exp/tri3c/decode_bd_tgpr_eval92
|
||||
steps/decode_raw_fmllr.sh --cmd "$decode_cmd" --nj 10 exp/tri3c/graph_bd_tgpr \
|
||||
data/test_dev93 exp/tri3c/decode_bd_tgpr_dev93
|
||||
)&
|
||||
|
||||
steps/align_fmllr.sh --nj 20 --cmd "$train_cmd" \
|
||||
data/train_si284 data/lang exp/tri3c exp/tri3c_ali_si284 || exit 1;
|
||||
|
||||
|
||||
steps/train_raw_sat.sh --cmd "$train_cmd" \
|
||||
4200 40000 data/train_si284 data/lang exp/tri3c_ali_si284 exp/tri4d || exit 1;
|
||||
(
|
||||
utils/mkgraph.sh data/lang_test_tgpr exp/tri4d exp/tri4d/graph_tgpr || exit 1;
|
||||
steps/decode_raw_fmllr.sh --nj 10 --cmd "$decode_cmd" \
|
||||
exp/tri4d/graph_tgpr data/test_dev93 exp/tri4d/decode_tgpr_dev93 || exit 1;
|
||||
steps/decode_raw_fmllr.sh --nj 8 --cmd "$decode_cmd" \
|
||||
exp/tri4d/graph_tgpr data/test_eval92 exp/tri4d/decode_tgpr_eval92 || exit 1;
|
||||
) &
|
||||
|
||||
|
||||
wait
|
||||
|
||||
|
||||
#for x in exp/tri3{b,c}/decode_tgpr*; do grep WER $x/wer_* | utils/best_wer.sh ; done
|
||||
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
#!/bin/bash
|
||||
|
||||
for test in dev93 eval92; do
|
||||
|
||||
steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_bd_tgpr data/lang_test_bd_fg \
|
||||
data/test_${test} exp/sgmm5b_mmi_b0.1/decode_bd_tgpr_${test}_it4 exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4 || exit 1;
|
||||
|
||||
|
||||
# Note: for N-best-list generation, choosing the acoustic scale (12) that gave
|
||||
# the best WER on this test set. Ideally we should do this on a dev set.
|
||||
|
||||
# This step interpolates a small RNNLM (with weight 0.25) with the 4-gram LM.
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 100 --cmd "$decode_cmd" --inv-acwt 12 \
|
||||
0.25 data/lang_test_bd_fg data/local/rnnlm.h30.voc10k data/test_${test} \
|
||||
exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4 exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4_rnnlm30_0.25 \
|
||||
|| exit 1;
|
||||
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 100 --cmd "$decode_cmd" --inv-acwt 12 \
|
||||
0.5 data/lang_test_bd_fg data/local/rnnlm.h100.voc20k data/test_${test} \
|
||||
exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4 exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4_rnnlm100_0.5 \
|
||||
|| exit 1;
|
||||
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 100 --cmd "$decode_cmd" --inv-acwt 12 \
|
||||
0.5 data/lang_test_bd_fg data/local/rnnlm.h200.voc30k data/test_${test} \
|
||||
exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4 exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4_rnnlm200_0.5 \
|
||||
|| exit 1;
|
||||
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 100 --cmd "$decode_cmd" --inv-acwt 12 \
|
||||
0.5 data/lang_test_bd_fg data/local/rnnlm.h300.voc40k data/test_${test} \
|
||||
exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4 exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4_rnnlm300_0.5 \
|
||||
|| exit 1;
|
||||
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 100 --cmd "$decode_cmd" --inv-acwt 12 \
|
||||
0.75 data/lang_test_bd_fg data/local/rnnlm.h300.voc40k data/test_${test} \
|
||||
exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4 exp/sgmm5b_mmi_b0.1/decode_bd_fg_${test}_it4_rnnlm300_0.75 \
|
||||
|| exit 1;
|
||||
done
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
#!/bin/bash
|
||||
|
||||
. cmd.sh
|
||||
|
||||
# This step interpolates a small RNNLM (with weight 0.25) with the 4-gram LM.
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 100 --cmd "$decode_cmd" --inv-acwt 17 \
|
||||
0.25 data/lang_test_bd_fg data/local/rnnlm.h30.voc10k data/test_eval92 \
|
||||
exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm30_0.25 \
|
||||
|| exit 1;
|
||||
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 100 --cmd "$decode_cmd" --inv-acwt 17 \
|
||||
0.5 data/lang_test_bd_fg data/local/rnnlm.h100.voc20k data/test_eval92 \
|
||||
exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm100_0.5 \
|
||||
|| exit 1;
|
||||
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 100 --cmd "$decode_cmd" --inv-acwt 17 \
|
||||
0.5 data/lang_test_bd_fg data/local/rnnlm.h200.voc30k data/test_eval92 \
|
||||
exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm200_0.5 \
|
||||
|| exit 1;
|
||||
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 100 --cmd "$decode_cmd" --inv-acwt 17 \
|
||||
0.5 data/lang_test_bd_fg data/local/rnnlm.h300.voc40k data/test_eval92 \
|
||||
exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm300_0.5 \
|
||||
|| exit 1;
|
||||
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 1000 --cmd "$decode_cmd" --inv-acwt 17 \
|
||||
0.5 data/lang_test_bd_fg data/local/rnnlm.h300.voc40k data/test_eval92 \
|
||||
exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm300_0.5_N1000
|
||||
|
||||
dir=exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm300_0.75_N1000
|
||||
rm -rf $dir
|
||||
cp -r exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm300_0.5_N1000 $dir
|
||||
steps/rnnlmrescore.sh \
|
||||
--stage 7 --N 1000 --cmd "$decode_cmd" --inv-acwt 17 \
|
||||
0.75 data/lang_test_bd_fg data/local/rnnlm.h300.voc40k data/test_eval92 \
|
||||
exp/tri3b/decode_bd_tgpr_eval92_fg $dir
|
||||
|
||||
dir=exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm300_0.75
|
||||
rm -rf $dir
|
||||
cp -r exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm300_0.5 $dir
|
||||
steps/rnnlmrescore.sh \
|
||||
--stage 7 --N 100 --cmd "$decode_cmd" --inv-acwt 17 \
|
||||
0.75 data/lang_test_bd_fg data/local/rnnlm.h300.voc40k data/test_eval92 \
|
||||
exp/tri3b/decode_bd_tgpr_eval92_fg $dir
|
||||
|
||||
dir=exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm300_0.25
|
||||
rm -rf $dir
|
||||
cp -r exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm300_0.5 $dir
|
||||
steps/rnnlmrescore.sh \
|
||||
--stage 7 --N 100 --cmd "$decode_cmd" --inv-acwt 17 \
|
||||
0.25 data/lang_test_bd_fg data/local/rnnlm.h300.voc40k data/test_eval92 \
|
||||
exp/tri3b/decode_bd_tgpr_eval92_fg $dir
|
||||
|
||||
steps/rnnlmrescore.sh \
|
||||
--N 10 --cmd "$decode_cmd" --inv-acwt 17 \
|
||||
0.5 data/lang_test_bd_fg data/local/rnnlm.h300.voc40k data/test_eval92 \
|
||||
exp/tri3b/decode_bd_tgpr_eval92_fg exp/tri3b/decode_bd_tgpr_eval92_fg_rnnlm300_0.5_N10 \
|
||||
|| exit 1;
|
||||
|
||||
|
|
@ -0,0 +1,113 @@
|
|||
#!/bin/bash
|
||||
|
||||
# This script is invoked from ../run.sh
|
||||
# It contains some SGMM-related scripts that I am breaking out of the main run.sh for clarity.
|
||||
|
||||
. cmd.sh
|
||||
|
||||
# SGMM system on si84 data [sgmm5a]. Note: the system we aligned from used the si284 data for
|
||||
# training, but this shouldn't have much effect.
|
||||
|
||||
(
|
||||
steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
|
||||
data/train_si84 data/lang exp/tri4b exp/tri4b_ali_si84 || exit 1;
|
||||
|
||||
steps/train_ubm.sh --cmd "$train_cmd" \
|
||||
400 data/train_si84 data/lang exp/tri4b_ali_si84 exp/ubm5a || exit 1;
|
||||
|
||||
steps/train_sgmm.sh --cmd "$train_cmd" \
|
||||
3500 10000 data/train_si84 data/lang exp/tri4b_ali_si84 \
|
||||
exp/ubm5b/final.ubm exp/sgmm5a || exit 1;
|
||||
|
||||
(
|
||||
utils/mkgraph.sh data/lang_test_tgpr exp/sgmm5a exp/sgmm5a/graph_tgpr
|
||||
steps/decode_sgmm.sh --nj 10 --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_tgpr_dev93 \
|
||||
exp/sgmm5a/graph_tgpr data/test_dev93 exp/sgmm5a/decode_tgpr_dev93
|
||||
) &
|
||||
|
||||
steps/align_sgmm.sh --nj 30 --cmd "$train_cmd" --transform-dir exp/tri4b_ali_si84 \
|
||||
--use-graphs true --use-gselect true data/train_si84 data/lang exp/sgmm5a exp/sgmm5a_ali_si84 || exit 1;
|
||||
steps/make_denlats_sgmm.sh --nj 30 --sub-split 30 --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si84 \
|
||||
data/train_si84 data/lang exp/sgmm5a_ali_si84 exp/sgmm5a_denlats_si84
|
||||
|
||||
steps/train_mmi_sgmm.sh --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si84 --boost 0.1 \
|
||||
data/train_si84 data/lang exp/sgmm5a_ali_si84 exp/sgmm5a_denlats_si84 exp/sgmm5a_mmi_b0.1
|
||||
|
||||
for iter in 1 2 3 4; do
|
||||
steps/decode_sgmm_rescore.sh --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri4b/decode_tgpr_dev93 data/lang_test_tgpr data/test_dev93 exp/sgmm5a/decode_tgpr_dev93 \
|
||||
exp/sgmm5a_mmi_b0.1/decode_tgpr_dev93_it$iter &
|
||||
done
|
||||
|
||||
steps/train_mmi_sgmm.sh --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si84 --boost 0.1 \
|
||||
--update-opts "--cov-min-value=0.9" data/train_si84 data/lang exp/sgmm5a_ali_si84 exp/sgmm5a_denlats_si84 exp/sgmm5a_mmi_b0.1_m0.9
|
||||
|
||||
for iter in 1 2 3 4; do
|
||||
steps/decode_sgmm_rescore.sh --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri4b/decode_tgpr_dev93 data/lang_test_tgpr data/test_dev93 exp/sgmm5a/decode_tgpr_dev93 \
|
||||
exp/sgmm5a_mmi_b0.1_m0.9/decode_tgpr_dev93_it$iter &
|
||||
done
|
||||
|
||||
) &
|
||||
|
||||
|
||||
(
|
||||
# The next commands are the same thing on all the si284 data.
|
||||
|
||||
# SGMM system on the si284 data [sgmm5b]
|
||||
steps/train_ubm.sh --cmd "$train_cmd" \
|
||||
600 data/train_si284 data/lang exp/tri4b_ali_si284 exp/ubm5b || exit 1;
|
||||
|
||||
steps/train_sgmm.sh --cmd "$train_cmd" \
|
||||
5500 25000 data/train_si284 data/lang exp/tri4b_ali_si284 \
|
||||
exp/ubm5b/final.ubm exp/sgmm5b || exit 1;
|
||||
|
||||
(
|
||||
utils/mkgraph.sh data/lang_test_tgpr exp/sgmm5b exp/sgmm5b/graph_tgpr
|
||||
steps/decode_sgmm.sh --nj 10 --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_tgpr_dev93 \
|
||||
exp/sgmm5b/graph_tgpr data/test_dev93 exp/sgmm5b/decode_tgpr_dev93
|
||||
steps/decode_sgmm.sh --nj 8 --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_tgpr_eval92 \
|
||||
exp/sgmm5b/graph_tgpr data/test_eval92 exp/sgmm5b/decode_tgpr_eval92
|
||||
|
||||
utils/mkgraph.sh data/lang_test_bd_tgpr exp/sgmm5b exp/sgmm5b/graph_bd_tgpr || exit 1;
|
||||
steps/decode_sgmm.sh --nj 10 --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_bd_tgpr_dev93 \
|
||||
exp/sgmm5b/graph_bd_tgpr data/test_dev93 exp/sgmm5b/decode_bd_tgpr_dev93
|
||||
steps/decode_sgmm.sh --nj 8 --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_bd_tgpr_eval92 \
|
||||
exp/sgmm5b/graph_bd_tgpr data/test_eval92 exp/sgmm5b/decode_bd_tgpr_eval92
|
||||
) &
|
||||
|
||||
steps/align_sgmm.sh --nj 30 --cmd "$train_cmd" --transform-dir exp/tri4b_ali_si284 \
|
||||
--use-graphs true --use-gselect true data/train_si284 data/lang exp/sgmm5b exp/sgmm5b_ali_si284
|
||||
|
||||
steps/make_denlats_sgmm.sh --nj 30 --sub-split 30 --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si284 \
|
||||
data/train_si284 data/lang exp/sgmm5b_ali_si284 exp/sgmm5b_denlats_si284
|
||||
|
||||
steps/train_mmi_sgmm.sh --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si284 --boost 0.1 \
|
||||
data/train_si284 data/lang exp/sgmm5b_ali_si284 exp/sgmm5b_denlats_si284 exp/sgmm5b_mmi_b0.1
|
||||
|
||||
for iter in 1 2 3 4; do
|
||||
for test in dev93 eval92; do
|
||||
steps/decode_sgmm_rescore.sh --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri4b/decode_tgpr_${test} data/lang_test_tgpr data/test_${test} exp/sgmm5b/decode_tgpr_${test} \
|
||||
exp/sgmm5b_mmi_b0.1/decode_tgpr_${test}_it$iter &
|
||||
|
||||
steps/decode_sgmm_rescore.sh --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri4b/decode_bd_tgpr_${test} data/lang_test_bd_tgpr data/test_${test} exp/sgmm5b/decode_bd_tgpr_${test} \
|
||||
exp/sgmm5b_mmi_b0.1/decode_bd_tgpr_${test}_it$iter &
|
||||
done
|
||||
done
|
||||
) &
|
||||
|
||||
|
||||
|
||||
# Train quinphone SGMM system.
|
||||
|
||||
steps/train_sgmm.sh --cmd "$train_cmd" \
|
||||
--context-opts "--context-width=5 --central-position=2" \
|
||||
5500 25000 data/train_si284 data/lang exp/tri4b_ali_si284 \
|
||||
exp/ubm5b/final.ubm exp/sgmm5c || exit 1;
|
||||
|
||||
# Decode from lattices in exp/sgmm5a/decode_tgpr_dev93.
|
||||
steps/decode_sgmm_fromlats.sh --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_tgpr_dev93 \
|
||||
data/test_dev93 data/lang_test_tgpr exp/sgmm5a/decode_tgpr_dev93 exp/sgmm5c/decode_tgpr_dev93
|
||||
|
||||
|
|
@ -0,0 +1,148 @@
|
|||
#!/bin/bash
|
||||
|
||||
# This script is invoked from ../run.sh
|
||||
# It contains some SGMM-related scripts that I am breaking out of the main run.sh for clarity.
|
||||
|
||||
. cmd.sh
|
||||
|
||||
# Note: you might want to try to give the option --spk-dep-weights=false to train_sgmm2.sh;
|
||||
# this takes out the "symmetric SGMM" part which is not always helpful.
|
||||
|
||||
# SGMM system on si84 data [sgmm5a]. Note: the system we aligned from used the si284 data for
|
||||
# training, but this shouldn't have much effect.
|
||||
|
||||
(
|
||||
steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
|
||||
data/train_si84 data/lang exp/tri4b exp/tri4b_ali_si84 || exit 1;
|
||||
|
||||
steps/train_ubm.sh --cmd "$train_cmd" \
|
||||
400 data/train_si84 data/lang exp/tri4b_ali_si84 exp/ubm5a || exit 1;
|
||||
|
||||
steps/train_sgmm2.sh --cmd "$train_cmd" \
|
||||
7000 9000 data/train_si84 data/lang exp/tri4b_ali_si84 \
|
||||
exp/ubm5a/final.ubm exp/sgmm2_5a || exit 1;
|
||||
|
||||
(
|
||||
utils/mkgraph.sh data/lang_test_tgpr exp/sgmm2_5a exp/sgmm2_5a/graph_tgpr
|
||||
steps/decode_sgmm2.sh --nj 10 --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_tgpr_dev93 \
|
||||
exp/sgmm2_5a/graph_tgpr data/test_dev93 exp/sgmm2_5a/decode_tgpr_dev93
|
||||
) &
|
||||
|
||||
steps/align_sgmm2.sh --nj 30 --cmd "$train_cmd" --transform-dir exp/tri4b_ali_si84 \
|
||||
--use-graphs true --use-gselect true data/train_si84 data/lang exp/sgmm2_5a exp/sgmm2_5a_ali_si84 || exit 1;
|
||||
steps/make_denlats_sgmm2.sh --nj 30 --sub-split 30 --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si84 \
|
||||
data/train_si84 data/lang exp/sgmm2_5a_ali_si84 exp/sgmm2_5a_denlats_si84
|
||||
|
||||
steps/train_mmi_sgmm2.sh --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si84 --boost 0.1 \
|
||||
data/train_si84 data/lang exp/sgmm2_5a_ali_si84 exp/sgmm2_5a_denlats_si84 exp/sgmm2_5a_mmi_b0.1
|
||||
|
||||
for iter in 1 2 3 4; do
|
||||
steps/decode_sgmm2_rescore.sh --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri4b/decode_tgpr_dev93 data/lang_test_tgpr data/test_dev93 exp/sgmm2_5a/decode_tgpr_dev93 \
|
||||
exp/sgmm2_5a_mmi_b0.1/decode_tgpr_dev93_it$iter &
|
||||
done
|
||||
|
||||
steps/train_mmi_sgmm2.sh --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si84 --boost 0.1 \
|
||||
--update-opts "--cov-min-value=0.9" data/train_si84 data/lang exp/sgmm2_5a_ali_si84 exp/sgmm2_5a_denlats_si84 exp/sgmm2_5a_mmi_b0.1_m0.9
|
||||
|
||||
for iter in 1 2 3 4; do
|
||||
steps/decode_sgmm2_rescore.sh --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri4b/decode_tgpr_dev93 data/lang_test_tgpr data/test_dev93 exp/sgmm2_5a/decode_tgpr_dev93 \
|
||||
exp/sgmm2_5a_mmi_b0.1_m0.9/decode_tgpr_dev93_it$iter &
|
||||
done
|
||||
|
||||
) &
|
||||
|
||||
|
||||
(
|
||||
# The next commands are the same thing on all the si284 data.
|
||||
|
||||
# SGMM system on the si284 data [sgmm5b]
|
||||
steps/train_ubm.sh --cmd "$train_cmd" \
|
||||
600 data/train_si284 data/lang exp/tri4b_ali_si284 exp/ubm5b || exit 1;
|
||||
|
||||
steps/train_sgmm2.sh --cmd "$train_cmd" \
|
||||
11000 25000 data/train_si284 data/lang exp/tri4b_ali_si284 \
|
||||
exp/ubm5b/final.ubm exp/sgmm2_5b || exit 1;
|
||||
|
||||
(
|
||||
utils/mkgraph.sh data/lang_test_tgpr exp/sgmm2_5b exp/sgmm2_5b/graph_tgpr
|
||||
steps/decode_sgmm2.sh --nj 10 --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_tgpr_dev93 \
|
||||
exp/sgmm2_5b/graph_tgpr data/test_dev93 exp/sgmm2_5b/decode_tgpr_dev93
|
||||
steps/decode_sgmm2.sh --nj 8 --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_tgpr_eval92 \
|
||||
exp/sgmm2_5b/graph_tgpr data/test_eval92 exp/sgmm2_5b/decode_tgpr_eval92
|
||||
|
||||
utils/mkgraph.sh data/lang_test_bd_tgpr exp/sgmm2_5b exp/sgmm2_5b/graph_bd_tgpr || exit 1;
|
||||
steps/decode_sgmm2.sh --nj 10 --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_bd_tgpr_dev93 \
|
||||
exp/sgmm2_5b/graph_bd_tgpr data/test_dev93 exp/sgmm2_5b/decode_bd_tgpr_dev93
|
||||
steps/decode_sgmm2.sh --nj 8 --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_bd_tgpr_eval92 \
|
||||
exp/sgmm2_5b/graph_bd_tgpr data/test_eval92 exp/sgmm2_5b/decode_bd_tgpr_eval92
|
||||
) &
|
||||
|
||||
|
||||
# This shows how you would build and test a quinphone SGMM2 system, but
|
||||
(
|
||||
steps/train_sgmm2.sh --cmd "$train_cmd" \
|
||||
--context-opts "--context-width=5 --central-position=2" \
|
||||
11000 25000 data/train_si284 data/lang exp/tri4b_ali_si284 \
|
||||
exp/ubm5b/final.ubm exp/sgmm2_5c || exit 1;
|
||||
# Decode from lattices in exp/sgmm2_5b
|
||||
steps/decode_sgmm2_fromlats.sh --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_tgpr_dev93 \
|
||||
data/test_dev93 data/lang_test_tgpr exp/sgmm2_5b/decode_tgpr_dev93 exp/sgmm2_5c/decode_tgpr_dev93
|
||||
steps/decode_sgmm2_fromlats.sh --cmd "$decode_cmd" --transform-dir exp/tri4b/decode_tgpr_eval92 \
|
||||
data/test_eval92 data/lang_test_tgpr exp/sgmm2_5b/decode_tgpr_eval92 exp/sgmm2_5c/decode_tgpr_eval92
|
||||
) &
|
||||
|
||||
|
||||
steps/align_sgmm2.sh --nj 30 --cmd "$train_cmd" --transform-dir exp/tri4b_ali_si284 \
|
||||
--use-graphs true --use-gselect true data/train_si284 data/lang exp/sgmm2_5b exp/sgmm2_5b_ali_si284
|
||||
|
||||
steps/make_denlats_sgmm2.sh --nj 30 --sub-split 30 --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si284 \
|
||||
data/train_si284 data/lang exp/sgmm2_5b_ali_si284 exp/sgmm2_5b_denlats_si284
|
||||
|
||||
steps/train_mmi_sgmm2.sh --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si284 --boost 0.1 \
|
||||
data/train_si284 data/lang exp/sgmm2_5b_ali_si284 exp/sgmm2_5b_denlats_si284 exp/sgmm2_5b_mmi_b0.1
|
||||
|
||||
for iter in 1 2 3 4; do
|
||||
for test in eval92; do # dev93
|
||||
steps/decode_sgmm2_rescore.sh --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri4b/decode_bd_tgpr_${test} data/lang_test_bd_fg data/test_${test} exp/sgmm2_5b/decode_bd_tgpr_${test} \
|
||||
exp/sgmm2_5b_mmi_b0.1/decode_bd_tgpr_${test}_it$iter &
|
||||
done
|
||||
done
|
||||
|
||||
steps/train_mmi_sgmm2.sh --cmd "$decode_cmd" --transform-dir exp/tri4b_ali_si284 --boost 0.1 \
|
||||
--zero-if-disjoint true data/train_si284 data/lang exp/sgmm2_5b_ali_si284 exp/sgmm2_5b_denlats_si284 exp/sgmm2_5b_mmi_b0.1_z
|
||||
|
||||
for iter in 1 2 3 4; do
|
||||
for test in eval92 dev93; do
|
||||
steps/decode_sgmm2_rescore.sh --cmd "$decode_cmd" --iter $iter \
|
||||
--transform-dir exp/tri4b/decode_bd_tgpr_${test} data/lang_test_bd_fg data/test_${test} exp/sgmm2_5b/decode_bd_tgpr_${test} \
|
||||
exp/sgmm2_5b_mmi_b0.1_z/decode_bd_tgpr_${test}_it$iter &
|
||||
done
|
||||
done
|
||||
|
||||
) &
|
||||
|
||||
wait
|
||||
|
||||
# Examples of combining some of the best decodings: SGMM+MMI with
|
||||
# MMI+fMMI on a conventional system.
|
||||
|
||||
local/score_combine.sh data/test_eval92 \
|
||||
data/lang_test_bd_tgpr \
|
||||
exp/tri4b_fmmi_a/decode_tgpr_eval92_it8 \
|
||||
exp/sgmm2_5b_mmi_b0.1/decode_bd_tgpr_eval92_it3 \
|
||||
exp/combine_tri4b_fmmi_a_sgmm2_5b_mmi_b0.1/decode_bd_tgpr_eval92_it8_3
|
||||
|
||||
|
||||
# %WER 4.43 [ 250 / 5643, 41 ins, 12 del, 197 sub ] exp/tri4b_fmmi_a/decode_tgpr_eval92_it8/wer_11
|
||||
# %WER 3.85 [ 217 / 5643, 35 ins, 11 del, 171 sub ] exp/sgmm2_5b_mmi_b0.1/decode_bd_tgpr_eval92_it3/wer_10
|
||||
# combined to:
|
||||
# %WER 3.76 [ 212 / 5643, 32 ins, 12 del, 168 sub ] exp/combine_tri4b_fmmi_a_sgmm2_5b_mmi_b0.1/decode_bd_tgpr_eval92_it8_3/wer_12
|
||||
|
||||
# Checking MBR decode of baseline:
|
||||
cp -r -T exp/sgmm2_5b_mmi_b0.1/decode_bd_tgpr_eval92_it3{,.mbr}
|
||||
local/score_mbr.sh data/test_eval92 data/lang_test_bd_tgpr exp/sgmm2_5b_mmi_b0.1/decode_bd_tgpr_eval92_it3.mbr
|
||||
# MBR decoding did not seem to help (baseline was 3.85). I think this is normal at such low WERs.
|
||||
%WER 3.86 [ 218 / 5643, 35 ins, 11 del, 172 sub ] exp/sgmm2_5b_mmi_b0.1/decode_bd_tgpr_eval92_it3.mbr/wer_10
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh
|
||||
|
||||
# begin configuration section.
|
||||
cmd=run.pl
|
||||
stage=0
|
||||
decode_mbr=true
|
||||
reverse=false
|
||||
word_ins_penalty=0.0
|
||||
min_lmwt=5
|
||||
max_lmwt=20
|
||||
#end configuration section.
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 3 ]; then
|
||||
echo "Usage: local/score.sh [--cmd (run.pl|queue.pl...)] <data-dir> <lang-dir|graph-dir> <decode-dir>"
|
||||
echo " Options:"
|
||||
echo " --cmd (run.pl|queue.pl...) # specify how to run the sub-processes."
|
||||
echo " --stage (0|1|2) # start scoring script from part-way through."
|
||||
echo " --decode_mbr (true/false) # maximum bayes risk decoding (confusion network)."
|
||||
echo " --min_lmwt <int> # minumum LM-weight for lattice rescoring "
|
||||
echo " --max_lmwt <int> # maximum LM-weight for lattice rescoring "
|
||||
echo " --reverse (true/false) # score with time reversed features "
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang_or_graph=$2
|
||||
dir=$3
|
||||
|
||||
symtab=$lang_or_graph/words.txt
|
||||
|
||||
for f in $symtab $dir/lat.1.gz $data/text; do
|
||||
[ ! -f $f ] && echo "score.sh: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
mkdir -p $dir/scoring/log
|
||||
|
||||
cat $data/text | sed 's:<NOISE>::g' | sed 's:<SPOKEN_NOISE>::g' > $dir/scoring/test_filt.txt
|
||||
|
||||
$cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/best_path.LMWT.log \
|
||||
lattice-scale --inv-acoustic-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \
|
||||
lattice-add-penalty --word-ins-penalty=$word_ins_penalty ark:- ark:- \| \
|
||||
lattice-best-path --word-symbol-table=$symtab \
|
||||
ark:- ark,t:$dir/scoring/LMWT.tra || exit 1;
|
||||
|
||||
if $reverse; then
|
||||
for lmwt in `seq $min_lmwt $max_lmwt`; do
|
||||
mv $dir/scoring/$lmwt.tra $dir/scoring/$lmwt.tra.orig
|
||||
awk '{ printf("%s ",$1); for(i=NF; i>1; i--){ printf("%s ",$i); } printf("\n"); }' \
|
||||
<$dir/scoring/$lmwt.tra.orig >$dir/scoring/$lmwt.tra
|
||||
done
|
||||
fi
|
||||
|
||||
# Note: the double level of quoting for the sed command
|
||||
$cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/score.LMWT.log \
|
||||
cat $dir/scoring/LMWT.tra \| \
|
||||
utils/int2sym.pl -f 2- $symtab \| sed 's:\<UNK\>::g' \| \
|
||||
compute-wer --text --mode=present \
|
||||
ark:$dir/scoring/test_filt.txt ark,p:- ">&" $dir/wer_LMWT || exit 1;
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,95 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2013 Arnab Ghoshal
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
# Script for system combination using minimum Bayes risk decoding.
|
||||
# This calls lattice-combine to create a union of lattices that have been
|
||||
# normalized by removing the total forward cost from them. The resulting lattice
|
||||
# is used as input to lattice-mbr-decode. This should not be put in steps/ or
|
||||
# utils/ since the scores on the combined lattice must not be scaled.
|
||||
|
||||
# begin configuration section.
|
||||
cmd=run.pl
|
||||
min_lmwt=9
|
||||
max_lmwt=20
|
||||
lat_weights=
|
||||
#end configuration section.
|
||||
|
||||
help_message="Usage: "$(basename $0)" [options] <data-dir> <graph-dir|lang-dir> <decode-dir1> <decode-dir2> [decode-dir3 ... ] <out-dir>
|
||||
Options:
|
||||
--cmd (run.pl|queue.pl...) # specify how to run the sub-processes.
|
||||
--min-lmwt INT # minumum LM-weight for lattice rescoring
|
||||
--max-lmwt INT # maximum LM-weight for lattice rescoring
|
||||
--lat-weights STR # colon-separated string of lattice weights
|
||||
";
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -lt 5 ]; then
|
||||
printf "$help_message\n";
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
graphdir=$2
|
||||
odir=${@: -1} # last argument to the script
|
||||
shift 2;
|
||||
decode_dirs=( $@ ) # read the remaining arguments into an array
|
||||
unset decode_dirs[${#decode_dirs[@]}-1] # 'pop' the last argument which is odir
|
||||
num_sys=${#decode_dirs[@]} # number of systems to combine
|
||||
|
||||
symtab=$graphdir/words.txt
|
||||
[ ! -f $symtab ] && echo "$0: missing word symbol table '$symtab'" && exit 1;
|
||||
[ ! -f $data/text ] && echo "$0: missing reference '$data/text'" && exit 1;
|
||||
|
||||
|
||||
mkdir -p $odir/log
|
||||
|
||||
for i in `seq 0 $[num_sys-1]`; do
|
||||
model=${decode_dirs[$i]}/../final.mdl # model one level up from decode dir
|
||||
for f in $model ${decode_dirs[$i]}/lat.1.gz ; do
|
||||
[ ! -f $f ] && echo "$0: expecting file $f to exist" && exit 1;
|
||||
done
|
||||
lats[$i]="\"ark:gunzip -c ${decode_dirs[$i]}/lat.*.gz |\""
|
||||
done
|
||||
|
||||
mkdir -p $odir/scoring/log
|
||||
|
||||
cat $data/text | sed 's:<NOISE>::g' | sed 's:<SPOKEN_NOISE>::g' \
|
||||
> $odir/scoring/test_filt.txt
|
||||
|
||||
if [ -z "$lat_weights" ]; then
|
||||
$cmd LMWT=$min_lmwt:$max_lmwt $odir/log/combine_lats.LMWT.log \
|
||||
lattice-combine --inv-acoustic-scale=LMWT ${lats[@]} ark:- \| \
|
||||
lattice-mbr-decode --word-symbol-table=$symtab ark:- \
|
||||
ark,t:$odir/scoring/LMWT.tra || exit 1;
|
||||
else
|
||||
$cmd LMWT=$min_lmwt:$max_lmwt $odir/log/combine_lats.LMWT.log \
|
||||
lattice-combine --inv-acoustic-scale=LMWT --lat-weights=$lat_weights \
|
||||
${lats[@]} ark:- \| \
|
||||
lattice-mbr-decode --word-symbol-table=$symtab ark:- \
|
||||
ark,t:$odir/scoring/LMWT.tra || exit 1;
|
||||
fi
|
||||
|
||||
$cmd LMWT=$min_lmwt:$max_lmwt $odir/scoring/log/score.LMWT.log \
|
||||
cat $odir/scoring/LMWT.tra \| \
|
||||
utils/int2sym.pl -f 2- $symtab \| sed 's:\<UNK\>::g' \| \
|
||||
compute-wer --text --mode=present \
|
||||
ark:$odir/scoring/test_filt.txt ark,p:- ">&" $odir/wer_LMWT || exit 1;
|
||||
|
||||
exit 0
|
||||
|
|
@ -0,0 +1,58 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Script for minimum bayes risk decoding.
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh;
|
||||
|
||||
# begin configuration section.
|
||||
cmd=run.pl
|
||||
min_lmwt=9
|
||||
max_lmwt=20
|
||||
#end configuration section.
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 3 ]; then
|
||||
echo "Usage: local/score_mbr.sh [--cmd (run.pl|queue.pl...)] <data-dir> <lang-dir|graph-dir> <decode-dir>"
|
||||
echo " Options:"
|
||||
echo " --cmd (run.pl|queue.pl...) # specify how to run the sub-processes."
|
||||
echo " --min_lmwt <int> # minumum LM-weight for lattice rescoring "
|
||||
echo " --max_lmwt <int> # maximum LM-weight for lattice rescoring "
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang_or_graph=$2
|
||||
dir=$3
|
||||
|
||||
symtab=$lang_or_graph/words.txt
|
||||
|
||||
for f in $symtab $dir/lat.1.gz $data/text; do
|
||||
[ ! -f $f ] && echo "score_mbr.sh: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
mkdir -p $dir/scoring/log
|
||||
|
||||
cat $data/text | sed 's:<NOISE>::g' | sed 's:<SPOKEN_NOISE>::g' > $dir/scoring/test_filt.txt
|
||||
|
||||
# We submit the jobs separately, not as an array, because it's hard
|
||||
# to get the inverse of the LM scales.
|
||||
rm $dir/.error 2>/dev/null
|
||||
for inv_acwt in `seq $min_lmwt $max_lmwt`; do
|
||||
acwt=`perl -e "print (1.0/$inv_acwt);"`
|
||||
$cmd $dir/scoring/rescore_mbr.${inv_acwt}.log \
|
||||
lattice-mbr-decode --acoustic-scale=$acwt --word-symbol-table=$symtab \
|
||||
"ark:gunzip -c $dir/lat.*.gz|" ark,t:$dir/scoring/${inv_acwt}.tra \
|
||||
|| touch $dir/.error &
|
||||
done
|
||||
wait;
|
||||
[ -f $dir/.error ] && echo "score_mbr.sh: errror getting MBR outout.";
|
||||
|
||||
|
||||
$cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/score.LMWT.log \
|
||||
cat $dir/scoring/LMWT.tra \| \
|
||||
utils/int2sym.pl -f 2- $symtab \| sed 's:\<UNK\>::g' \| \
|
||||
compute-wer --text --mode=present \
|
||||
ark:$dir/scoring/test_filt.txt ark,p:- ">" $dir/wer_LMWT || exit 1;
|
||||
|
||||
|
|
@ -0,0 +1,201 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2009-2012 Microsoft Corporation Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0.
|
||||
|
||||
|
||||
if [ $# -le 3 ]; then
|
||||
echo "Arguments should be a list of WSJ directories, see ../run.sh for example."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
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
|
||||
|
||||
# Make directory of links to the WSJ disks such as 11-13.1. This relies on the command
|
||||
# line arguments being absolute pathnames.
|
||||
rm -r links/ 2>/dev/null
|
||||
mkdir links/
|
||||
ln -s $* links
|
||||
|
||||
# Do some basic checks that we have what we expected.
|
||||
if [ ! -d links/11-13.1 -o ! -d links/13-34.1 -o ! -d links/11-2.1 ]; then
|
||||
echo "wsj_data_prep.sh: Spot check of command line arguments failed"
|
||||
echo "Command line arguments must be absolute pathnames to WSJ directories"
|
||||
echo "with names like 11-13.1."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
# This version for SI-84
|
||||
|
||||
cat links/11-13.1/wsj0/doc/indices/train/tr_s_wv1.ndx | \
|
||||
$local/ndx2flist.pl $* | sort | \
|
||||
grep -v -i 11-2.1/wsj0/si_tr_s/401 > train_si84.flist
|
||||
|
||||
nl=`cat train_si84.flist | wc -l`
|
||||
[ "$nl" -eq 7138 ] || echo "Warning: expected 7138 lines in train_si84.flist, got $nl"
|
||||
|
||||
# This version for SI-284
|
||||
cat links/13-34.1/wsj1/doc/indices/si_tr_s.ndx \
|
||||
links/11-13.1/wsj0/doc/indices/train/tr_s_wv1.ndx | \
|
||||
$local/ndx2flist.pl $* | sort | \
|
||||
grep -v -i 11-2.1/wsj0/si_tr_s/401 > train_si284.flist
|
||||
|
||||
nl=`cat train_si284.flist | wc -l`
|
||||
[ "$nl" -eq 37416 ] || echo "Warning: expected 37416 lines in train_si284.flist, got $nl"
|
||||
|
||||
# Now for the test sets.
|
||||
# links/13-34.1/wsj1/doc/indices/readme.doc
|
||||
# describes all the different test sets.
|
||||
# Note: each test-set seems to come in multiple versions depending
|
||||
# on different vocabulary sizes, verbalized vs. non-verbalized
|
||||
# pronunciations, etc. We use the largest vocab and non-verbalized
|
||||
# pronunciations.
|
||||
# The most normal one seems to be the "baseline 60k test set", which
|
||||
# is h1_p0.
|
||||
|
||||
# Nov'92 (333 utts)
|
||||
# These index files have a slightly different format;
|
||||
# have to add .wv1
|
||||
cat links/11-13.1/wsj0/doc/indices/test/nvp/si_et_20.ndx | \
|
||||
$local/ndx2flist.pl $* | awk '{printf("%s.wv1\n", $1)}' | \
|
||||
sort > test_eval92.flist
|
||||
|
||||
# Nov'92 (330 utts, 5k vocab)
|
||||
cat links/11-13.1/wsj0/doc/indices/test/nvp/si_et_05.ndx | \
|
||||
$local/ndx2flist.pl $* | awk '{printf("%s.wv1\n", $1)}' | \
|
||||
sort > test_eval92_5k.flist
|
||||
|
||||
# Nov'93: (213 utts)
|
||||
# Have to replace a wrong disk-id.
|
||||
cat links/13-32.1/wsj1/doc/indices/wsj1/eval/h1_p0.ndx | \
|
||||
sed s/13_32_1/13_33_1/ | \
|
||||
$local/ndx2flist.pl $* | sort > test_eval93.flist
|
||||
|
||||
# Nov'93: (213 utts, 5k)
|
||||
cat links/13-32.1/wsj1/doc/indices/wsj1/eval/h2_p0.ndx | \
|
||||
sed s/13_32_1/13_33_1/ | \
|
||||
$local/ndx2flist.pl $* | sort > test_eval93_5k.flist
|
||||
|
||||
# Dev-set for Nov'93 (503 utts)
|
||||
cat links/13-34.1/wsj1/doc/indices/h1_p0.ndx | \
|
||||
$local/ndx2flist.pl $* | sort > test_dev93.flist
|
||||
|
||||
# Dev-set for Nov'93 (513 utts, 5k vocab)
|
||||
cat links/13-34.1/wsj1/doc/indices/h2_p0.ndx | \
|
||||
$local/ndx2flist.pl $* | sort > test_dev93_5k.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 `readlink links/13-16.1`/???1/??_??_20 -print | grep -i ".wv1" | sort > dev_dt_20.flist
|
||||
find `readlink links/13-16.1`/???1/??_??_05 -print | grep -i ".wv1" | sort > dev_dt_05.flist
|
||||
|
||||
|
||||
# Finding the transcript files:
|
||||
for x in $*; do find -L $x -iname '*.dot'; done > dot_files.flist
|
||||
|
||||
# Convert the transcripts into our format (no normalization yet)
|
||||
for x in train_si84 train_si284 test_eval92 test_eval93 test_dev93 test_eval92_5k test_eval93_5k test_dev93_5k dev_dt_05 dev_dt_20; do
|
||||
$local/flist2scp.pl $x.flist | sort > ${x}_sph.scp
|
||||
cat ${x}_sph.scp | awk '{print $1}' | $local/find_transcripts.pl dot_files.flist > $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 train_si284 test_eval92 test_eval93 test_dev93 test_eval92_5k test_eval93_5k test_dev93_5k dev_dt_05 dev_dt_20; do
|
||||
cat $x.trans1 | $local/normalize_transcript.pl $noiseword | sort > $x.txt || exit 1;
|
||||
done
|
||||
|
||||
# Create scp's with wav's. (the wv1 in the distribution is not really wav, it is sph.)
|
||||
for x in train_si84 train_si284 test_eval92 test_eval93 test_dev93 test_eval92_5k test_eval93_5k test_dev93_5k dev_dt_05 dev_dt_20; do
|
||||
awk '{printf("%s '$sph2pipe' -f wav %s |\n", $1, $2);}' < ${x}_sph.scp > ${x}_wav.scp
|
||||
done
|
||||
|
||||
# Make the utt2spk and spk2utt files.
|
||||
for x in train_si84 train_si284 test_eval92 test_eval93 test_dev93 test_eval92_5k test_eval93_5k test_dev93_5k dev_dt_05 dev_dt_20; do
|
||||
cat ${x}_sph.scp | awk '{print $1}' | perl -ane 'chop; m:^...:; print "$_ $&\n";' > $x.utt2spk
|
||||
cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
|
||||
done
|
||||
|
||||
|
||||
#in case we want to limit lm's on most frequent words, copy lm training word frequency list
|
||||
cp links/13-32.1/wsj1/doc/lng_modl/vocab/wfl_64.lst $lmdir
|
||||
chmod u+w $lmdir/*.lst # had weird permissions on source.
|
||||
|
||||
# The 20K vocab, open-vocabulary language model (i.e. the one with UNK), without
|
||||
# verbalized pronunciations. This is the most common test setup, I understand.
|
||||
|
||||
cp links/13-32.1/wsj1/doc/lng_modl/base_lm/bcb20onp.z $lmdir/lm_bg.arpa.gz || exit 1;
|
||||
chmod u+w $lmdir/lm_bg.arpa.gz
|
||||
|
||||
# trigram would be:
|
||||
cat links/13-32.1/wsj1/doc/lng_modl/base_lm/tcb20onp.z | \
|
||||
perl -e 'while(<>){ if(m/^\\data\\/){ print; last; } } while(<>){ print; }' | \
|
||||
gzip -c -f > $lmdir/lm_tg.arpa.gz || exit 1;
|
||||
|
||||
prune-lm --threshold=1e-7 $lmdir/lm_tg.arpa.gz $lmdir/lm_tgpr.arpa || exit 1;
|
||||
gzip -f $lmdir/lm_tgpr.arpa || exit 1;
|
||||
|
||||
# repeat for 5k language models
|
||||
cp links/13-32.1/wsj1/doc/lng_modl/base_lm/bcb05onp.z $lmdir/lm_bg_5k.arpa.gz || exit 1;
|
||||
chmod u+w $lmdir/lm_bg_5k.arpa.gz
|
||||
|
||||
# trigram would be: !only closed vocabulary here!
|
||||
cp links/13-32.1/wsj1/doc/lng_modl/base_lm/tcb05cnp.z $lmdir/lm_tg_5k.arpa.gz || exit 1;
|
||||
chmod u+w $lmdir/lm_tg_5k.arpa.gz
|
||||
gunzip $lmdir/lm_tg_5k.arpa.gz
|
||||
tail -n 4328839 $lmdir/lm_tg_5k.arpa | gzip -c -f > $lmdir/lm_tg_5k.arpa.gz
|
||||
rm $lmdir/lm_tg_5k.arpa
|
||||
|
||||
prune-lm --threshold=1e-7 $lmdir/lm_tg_5k.arpa.gz $lmdir/lm_tgpr_5k.arpa || exit 1;
|
||||
gzip -f $lmdir/lm_tgpr_5k.arpa || exit 1;
|
||||
|
||||
|
||||
if [ ! -f wsj0-train-spkrinfo.txt ] || [ `cat wsj0-train-spkrinfo.txt | wc -l` -ne 134 ]; then
|
||||
rm wsj0-train-spkrinfo.txt
|
||||
! wget http://www.ldc.upenn.edu/Catalog/docs/LDC93S6A/wsj0-train-spkrinfo.txt && \
|
||||
echo "Getting wsj0-train-spkrinfo.txt from backup location" && \
|
||||
wget --no-check-certificate https://sourceforge.net/projects/kaldi/files/wsj0-train-spkrinfo.txt
|
||||
fi
|
||||
|
||||
if [ ! -f wsj0-train-spkrinfo.txt ]; then
|
||||
echo "Could not get the spkrinfo.txt file from LDC website (moved)?"
|
||||
echo "This is possibly omitted from the training disks; couldn't find it."
|
||||
echo "Everything else may have worked; we just may be missing gender info"
|
||||
echo "which is only needed for VTLN-related diagnostics anyway."
|
||||
exit 1
|
||||
fi
|
||||
# Note: wsj0-train-spkrinfo.txt doesn't seem to be on the disks but the
|
||||
# LDC put it on the web. Perhaps it was accidentally omitted from the
|
||||
# disks.
|
||||
|
||||
cat links/11-13.1/wsj0/doc/spkrinfo.txt \
|
||||
links/13-32.1/wsj1/doc/evl_spok/spkrinfo.txt \
|
||||
links/13-34.1/wsj1/doc/dev_spok/spkrinfo.txt \
|
||||
links/13-34.1/wsj1/doc/train/spkrinfo.txt \
|
||||
./wsj0-train-spkrinfo.txt | \
|
||||
perl -ane 'tr/A-Z/a-z/; m/^;/ || print;' | \
|
||||
awk '{print $1, $2}' | grep -v -- -- | sort | uniq > spk2gender
|
||||
|
||||
|
||||
echo "Data preparation succeeded"
|
||||
|
|
@ -0,0 +1,173 @@
|
|||
#!/bin/bash
|
||||
|
||||
# This script builds a larger word-list and dictionary
|
||||
# than used for the LMs supplied with the WSJ corpus.
|
||||
# It uses a couple of strategies to fill-in words in
|
||||
# the LM training data but not in CMUdict. One is
|
||||
# to generate special prons for possible acronyms, that
|
||||
# just consist of the constituent letters. The other
|
||||
# is designed to handle derivatives of known words
|
||||
# (e.g. deriving the pron of a plural from the pron of
|
||||
# the base-word), but in a more general, learned-from-data
|
||||
# way.
|
||||
# It makes use of scripts in local/dict/
|
||||
|
||||
if [ $# -ne 1 ]; then
|
||||
echo "Usage: local/wsj_train_lms.sh /foo/bar/WSJ/13-32.1/"
|
||||
exit 1
|
||||
fi
|
||||
if [ "`basename $1`" != 13-32.1 ]; then
|
||||
echo "Expecting the argument to this script to end in 13-32.1"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# e.g.
|
||||
#srcdir=/mnt/matylda2/data/WSJ1/13-32.1
|
||||
export PATH=$PATH:`pwd`/local/dict/
|
||||
srcdir=$1
|
||||
mkdir -p data/local/dict_larger
|
||||
dir=data/local/dict_larger
|
||||
cp data/local/dict/* data/local/dict_larger # Various files describing phones etc.
|
||||
# are there; we just want to copy them as the phoneset is the same.
|
||||
rm data/local/dict_larger/lexicon.txt # we don't want this.
|
||||
rm data/local/dict_larger/lexiconp.txt # we don't want this either.
|
||||
mincount=2 # Minimum count of an OOV we will try to generate a pron for.
|
||||
|
||||
[ ! -f data/local/dict/cmudict/cmudict.0.7a ] && echo "CMU dict not in expected place" && exit 1;
|
||||
|
||||
# Remove comments from cmudict; print first field; remove
|
||||
# words like FOO(1) which are alternate prons: our dict format won't
|
||||
# include these markers.
|
||||
grep -v ';;;' data/local/dict/cmudict/cmudict.0.7a |
|
||||
perl -ane 's/^(\S+)\(\d+\)/$1/; print; ' | sort | uniq > $dir/dict.cmu
|
||||
|
||||
cat $dir/dict.cmu | awk '{print $1}' | sort | uniq > $dir/wordlist.cmu
|
||||
|
||||
echo "Getting training data [this should take at least a few seconds; if not, there's a problem]"
|
||||
|
||||
# Convert to uppercase, remove XML-like markings.
|
||||
# For words ending in "." that are not in CMUdict, we assume that these
|
||||
# are periods that somehow remained in the data during data preparation,
|
||||
# and we we replace the "." with "\n". Note: we found this by looking at
|
||||
# oov.counts below (before adding this rule).
|
||||
|
||||
touch $dir/cleaned.gz
|
||||
if [ `du -m $dir/cleaned.gz | cut -f 1` -eq 73 ]; then
|
||||
echo "Not getting cleaned data in $dir/cleaned.gz again [already exists]";
|
||||
else
|
||||
gunzip -c $srcdir/wsj1/doc/lng_modl/lm_train/np_data/{87,88,89}/*.z \
|
||||
| awk '/^</{next}{print toupper($0)}' | perl -e '
|
||||
open(F, "<$ARGV[0]")||die;
|
||||
while(<F>){ chop; $isword{$_} = 1; }
|
||||
while(<STDIN>) {
|
||||
@A = split(" ", $_);
|
||||
for ($n = 0; $n < @A; $n++) {
|
||||
$a = $A[$n];
|
||||
if (! $isword{$a} && $a =~ s/^([^\.]+)\.$/$1/) { # nonwords that end in "."
|
||||
# and have no other "." in them: treat as period.
|
||||
print "$a";
|
||||
if ($n+1 < @A) { print "\n"; }
|
||||
} else { print "$a "; }
|
||||
}
|
||||
print "\n";
|
||||
}
|
||||
' $dir/wordlist.cmu | gzip -c > $dir/cleaned.gz
|
||||
fi
|
||||
|
||||
# get unigram counts
|
||||
echo "Getting unigram counts"
|
||||
gunzip -c $dir/cleaned.gz | tr -s ' ' '\n' | \
|
||||
awk '{count[$1]++} END{for (w in count) { print count[w], w; }}' | sort -nr > $dir/unigrams
|
||||
|
||||
cat $dir/unigrams | awk -v dict=$dir/dict.cmu \
|
||||
'BEGIN{while(getline<dict) seen[$1]=1;} {if(!seen[$2]){print;}}' \
|
||||
> $dir/oov.counts
|
||||
|
||||
echo "Most frequent unseen unigrams are: "
|
||||
head $dir/oov.counts
|
||||
|
||||
# Prune away singleton counts, and remove things with numbers in
|
||||
# (which should have been normalized) and with no letters at all.
|
||||
|
||||
|
||||
cat $dir/oov.counts | awk -v thresh=$mincount '{if ($1 >= thresh) { print $2; }}' \
|
||||
| awk '/[0-9]/{next;} /[A-Z]/{print;}' > $dir/oovlist
|
||||
|
||||
# Automatic rule-finding...
|
||||
|
||||
# First make some prons for possible acronyms.
|
||||
# Note: we don't do this for things like U.K or U.N,
|
||||
# or A.B. (which doesn't exist anyway),
|
||||
# as we consider this normalization/spelling errors.
|
||||
|
||||
cat $dir/oovlist | local/dict/get_acronym_prons.pl $dir/dict.cmu > $dir/dict.acronyms
|
||||
|
||||
mkdir $dir/f $dir/b # forward, backward directions of rules...
|
||||
# forward is normal suffix
|
||||
# rules, backward is reversed (prefix rules). These
|
||||
# dirs contain stuff we create while making the rule-based
|
||||
# extensions to the dictionary.
|
||||
|
||||
# Remove ; and , from words, if they are present; these
|
||||
# might crash our scripts, as they are used as separators there.
|
||||
filter_dict.pl $dir/dict.cmu > $dir/f/dict
|
||||
cat $dir/oovlist | filter_dict.pl > $dir/f/oovs
|
||||
reverse_dict.pl $dir/f/dict > $dir/b/dict
|
||||
reverse_dict.pl $dir/f/oovs > $dir/b/oovs
|
||||
|
||||
# The next stage takes a few minutes.
|
||||
# Note: the forward stage takes longer, as English is
|
||||
# mostly a suffix-based language, and there are more rules
|
||||
# that it finds.
|
||||
for d in $dir/f $dir/b; do
|
||||
(
|
||||
cd $d
|
||||
cat dict | get_rules.pl 2>get_rules.log >rules
|
||||
get_rule_hierarchy.pl rules >hierarchy
|
||||
awk '{print $1}' dict | get_candidate_prons.pl rules dict | \
|
||||
limit_candidate_prons.pl hierarchy | \
|
||||
score_prons.pl dict | \
|
||||
count_rules.pl >rule.counts
|
||||
# the sort command below is just for convenience of reading.
|
||||
score_rules.pl <rule.counts | sort -t';' -k3,3 -n -r >rules.with_scores
|
||||
get_candidate_prons.pl rules.with_scores dict oovs | \
|
||||
limit_candidate_prons.pl hierarchy > oovs.candidates
|
||||
) &
|
||||
done
|
||||
wait
|
||||
|
||||
# Merge the candidates.
|
||||
reverse_candidates.pl $dir/b/oovs.candidates | cat - $dir/f/oovs.candidates | sort > $dir/oovs.candidates
|
||||
select_candidate_prons.pl <$dir/oovs.candidates | awk -F';' '{printf("%s %s\n", $1, $2);}' \
|
||||
> $dir/dict.oovs
|
||||
|
||||
cat $dir/dict.acronyms $dir/dict.oovs | sort | uniq > $dir/dict.oovs_merged
|
||||
|
||||
awk '{print $1}' $dir/dict.oovs_merged | uniq > $dir/oovlist.handled
|
||||
sort $dir/oovlist | diff - $dir/oovlist.handled | grep -v 'd' | sed 's:< ::' > $dir/oovlist.not_handled
|
||||
|
||||
|
||||
# add_counts.pl attaches to original counts to the list of handled/not-handled OOVs
|
||||
add_counts.pl $dir/oov.counts $dir/oovlist.handled | sort -nr > $dir/oovlist.handled.counts
|
||||
add_counts.pl $dir/oov.counts $dir/oovlist.not_handled | sort -nr > $dir/oovlist.not_handled.counts
|
||||
|
||||
echo "**Top OOVs we handled are:**";
|
||||
head $dir/oovlist.handled.counts
|
||||
echo "**Top OOVs we didn't handle are as follows (note: they are mostly misspellings):**";
|
||||
head $dir/oovlist.not_handled.counts
|
||||
|
||||
|
||||
echo "Count of OOVs we handled is `awk '{x+=$1} END{print x}' $dir/oovlist.handled.counts`"
|
||||
echo "Count of OOVs we couldn't handle is `awk '{x+=$1} END{print x}' $dir/oovlist.not_handled.counts`"
|
||||
echo "Count of OOVs we didn't handle due to low count is" \
|
||||
`awk -v thresh=$mincount '{if ($1 < thresh) x+=$1; } END{print x;}' $dir/oov.counts`
|
||||
# The two files created above are for humans to look at, as diagnostics.
|
||||
|
||||
cat <<EOF | cat - $dir/dict.cmu $dir/dict.oovs_merged | sort | uniq > $dir/lexicon.txt
|
||||
!SIL SIL
|
||||
<SPOKEN_NOISE> SPN
|
||||
<UNK> SPN
|
||||
<NOISE> NSN
|
||||
EOF
|
||||
|
||||
echo "Created $dir/lexicon.txt"
|
||||
|
|
@ -0,0 +1,86 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Microsoft Corporation Johns Hopkins University (Author: Daniel Povey)
|
||||
# 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.
|
||||
|
||||
. ./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_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_test_${lm_suffix}
|
||||
mkdir -p $test
|
||||
for f in phones.txt words.txt phones.txt L.fst L_disambig.fst \
|
||||
phones/; do
|
||||
cp -r data/lang/$f $test
|
||||
done
|
||||
gunzip -c $lmdir/lm_${lm_suffix}.arpa.gz | \
|
||||
utils/find_arpa_oovs.pl $test/words.txt > $tmpdir/oovs_${lm_suffix}.txt
|
||||
|
||||
# grep -v '<s> <s>' because the LM seems to have some strange and useless
|
||||
# stuff in it with multiple <s>'s in the history. Encountered some other similar
|
||||
# things in a LM from Geoff. Removing all "illegal" combinations of <s> and </s>,
|
||||
# which are supposed to occur only at being/end of utt. These can cause
|
||||
# determinization failures of CLG [ends up being epsilon cycles].
|
||||
gunzip -c $lmdir/lm_${lm_suffix}.arpa.gz | \
|
||||
grep -v '<s> <s>' | \
|
||||
grep -v '</s> <s>' | \
|
||||
grep -v '</s> </s>' | \
|
||||
arpa2fst - | fstprint | \
|
||||
utils/remove_oovs.pl $tmpdir/oovs_${lm_suffix}.txt | \
|
||||
utils/eps2disambig.pl | utils/s2eps.pl | fstcompile --isymbols=$test/words.txt \
|
||||
--osymbols=$test/words.txt --keep_isymbols=false --keep_osymbols=false | \
|
||||
fstrmepsilon > $test/G.fst
|
||||
fstisstochastic $test/G.fst
|
||||
# The output is like:
|
||||
# 9.14233e-05 -0.259833
|
||||
# we do expect the first of these 2 numbers to be close to zero (the second is
|
||||
# nonzero because the backoff weights make the states sum to >1).
|
||||
# Because of the <s> fiasco for these particular LMs, the first number is not
|
||||
# as close to zero as it could be.
|
||||
|
||||
# Everything below is only for diagnostic.
|
||||
# Checking that G has no cycles with empty words on them (e.g. <s>, </s>);
|
||||
# this might cause determinization failure of CLG.
|
||||
# #0 is treated as an empty word.
|
||||
mkdir -p $tmpdir/g
|
||||
awk '{if(NF==1){ printf("0 0 %s %s\n", $1,$1); }} END{print "0 0 #0 #0"; print "0";}' \
|
||||
< "$lexicon" >$tmpdir/g/select_empty.fst.txt
|
||||
fstcompile --isymbols=$test/words.txt --osymbols=$test/words.txt $tmpdir/g/select_empty.fst.txt | \
|
||||
fstarcsort --sort_type=olabel | fstcompose - $test/G.fst > $tmpdir/g/empty_words.fst
|
||||
fstinfo $tmpdir/g/empty_words.fst | grep cyclic | grep -w 'y' &&
|
||||
echo "Language model has cycles with empty words" && exit 1
|
||||
rm -r $tmpdir/g
|
||||
done
|
||||
|
||||
echo "Succeeded in formatting data."
|
||||
rm -r $tmpdir
|
||||
|
|
@ -0,0 +1,52 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright Johns Hopkins University (Author: Daniel Povey) 2012
|
||||
|
||||
. ./path.sh
|
||||
|
||||
[ ! -d data/lang_bd ] && echo "Expect data/local/lang_bd to exist" && exit 1;
|
||||
|
||||
lm_srcdir_3g=data/local/local_lm/3gram-mincount
|
||||
lm_srcdir_4g=data/local/local_lm/4gram-mincount
|
||||
|
||||
[ ! -d "$lm_srcdir_3g" ] && echo "No such dir $lm_srcdir_3g" && exit 1;
|
||||
[ ! -d "$lm_srcdir_4g" ] && echo "No such dir $lm_srcdir_4g" && exit 1;
|
||||
|
||||
for d in data/lang_test_bd_{tg,tgpr,fg,fgpr}; do
|
||||
rm -r $d 2>/dev/null
|
||||
cp -r data/lang_bd $d
|
||||
done
|
||||
|
||||
lang=data/lang_bd
|
||||
|
||||
# Be careful: this time we dispense with the grep -v '<s> <s>' so this might
|
||||
# not work for LMs generated from all toolkits.
|
||||
gunzip -c $lm_srcdir_3g/lm_pr6.0.gz | \
|
||||
arpa2fst - | fstprint | \
|
||||
utils/eps2disambig.pl | utils/s2eps.pl | fstcompile --isymbols=$lang/words.txt \
|
||||
--osymbols=$lang/words.txt --keep_isymbols=false --keep_osymbols=false | \
|
||||
fstrmepsilon > data/lang_test_bd_tgpr/G.fst || exit 1;
|
||||
fstisstochastic data/lang_test_bd_tgpr/G.fst
|
||||
|
||||
gunzip -c $lm_srcdir_3g/lm_unpruned.gz | \
|
||||
arpa2fst - | fstprint | \
|
||||
utils/eps2disambig.pl | utils/s2eps.pl | fstcompile --isymbols=$lang/words.txt \
|
||||
--osymbols=$lang/words.txt --keep_isymbols=false --keep_osymbols=false | \
|
||||
fstrmepsilon > data/lang_test_bd_tg/G.fst || exit 1;
|
||||
fstisstochastic data/lang_test_bd_tg/G.fst
|
||||
|
||||
gunzip -c $lm_srcdir_4g/lm_unpruned.gz | \
|
||||
arpa2fst - | fstprint | \
|
||||
utils/eps2disambig.pl | utils/s2eps.pl | fstcompile --isymbols=$lang/words.txt \
|
||||
--osymbols=$lang/words.txt --keep_isymbols=false --keep_osymbols=false | \
|
||||
fstrmepsilon > data/lang_test_bd_fg/G.fst || exit 1;
|
||||
fstisstochastic data/lang_test_bd_fg/G.fst
|
||||
|
||||
gunzip -c $lm_srcdir_4g/lm_pr7.0.gz | \
|
||||
arpa2fst - | fstprint | \
|
||||
utils/eps2disambig.pl | utils/s2eps.pl | fstcompile --isymbols=$lang/words.txt \
|
||||
--osymbols=$lang/words.txt --keep_isymbols=false --keep_osymbols=false | \
|
||||
fstrmepsilon > data/lang_test_bd_fgpr/G.fst || exit 1;
|
||||
fstisstochastic data/lang_test_bd_fgpr/G.fst
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2010-2012 Microsoft Corporation Johns Hopkins University (Author: Daniel Povey)
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Call this script from one level above, e.g. from the s3/ directory. It puts
|
||||
# its output in data/local/.
|
||||
|
||||
# The parts of the output of this that will be needed are
|
||||
# [in data/local/dict/ ]
|
||||
# lexicon.txt
|
||||
# extra_questions.txt
|
||||
# nonsilence_phones.txt
|
||||
# optional_silence.txt
|
||||
# silence_phones.txt
|
||||
|
||||
# run this from ../
|
||||
dir=data/local/dict
|
||||
mkdir -p $dir
|
||||
|
||||
|
||||
# (1) Get the CMU dictionary
|
||||
svn co https://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict \
|
||||
$dir/cmudict || exit 1;
|
||||
|
||||
# can add -r 10966 for strict compatibility.
|
||||
|
||||
|
||||
#(2) Dictionary preparation:
|
||||
|
||||
|
||||
# Make phones symbol-table (adding in silence and verbal and non-verbal noises at this point).
|
||||
# We are adding suffixes _B, _E, _S for beginning, ending, and singleton phones.
|
||||
|
||||
# silence phones, one per line.
|
||||
(echo SIL; echo SPN; echo NSN) > $dir/silence_phones.txt
|
||||
echo SIL > $dir/optional_silence.txt
|
||||
|
||||
# nonsilence phones; on each line is a list of phones that correspond
|
||||
# really to the same base phone.
|
||||
cat $dir/cmudict/cmudict.0.7a.symbols | perl -ane 's:\r::; print;' | \
|
||||
perl -e 'while(<>){
|
||||
chop; m:^([^\d]+)(\d*)$: || die "Bad phone $_";
|
||||
$phones_of{$1} .= "$_ "; }
|
||||
foreach $list (values %phones_of) {print $list . "\n"; } ' \
|
||||
> $dir/nonsilence_phones.txt || exit 1;
|
||||
|
||||
# A few extra questions that will be added to those obtained by automatically clustering
|
||||
# the "real" phones. These ask about stress; there's also one for silence.
|
||||
cat $dir/silence_phones.txt| awk '{printf("%s ", $1);} END{printf "\n";}' > $dir/extra_questions.txt || exit 1;
|
||||
cat $dir/nonsilence_phones.txt | perl -e 'while(<>){ foreach $p (split(" ", $_)) {
|
||||
$p =~ m:^([^\d]+)(\d*)$: || die "Bad phone $_"; $q{$2} .= "$p "; } } foreach $l (values %q) {print "$l\n";}' \
|
||||
>> $dir/extra_questions.txt || exit 1;
|
||||
|
||||
grep -v ';;;' $dir/cmudict/cmudict.0.7a | \
|
||||
perl -ane 'if(!m:^;;;:){ s:(\S+)\(\d+\) :$1 :; print; }' \
|
||||
> $dir/lexicon1_raw_nosil.txt || exit 1;
|
||||
|
||||
# Add to cmudict the silences, noises etc.
|
||||
|
||||
(echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; echo '<NOISE> NSN'; ) | \
|
||||
cat - $dir/lexicon1_raw_nosil.txt > $dir/lexicon2_raw.txt || exit 1;
|
||||
|
||||
|
||||
# lexicon.txt is without the _B, _E, _S, _I markers.
|
||||
# This is the input to wsj_format_data.sh
|
||||
cp $dir/lexicon2_raw.txt $dir/lexicon.txt
|
||||
|
||||
|
||||
echo "Dictionary preparation succeeded"
|
||||
|
||||
|
|
@ -0,0 +1,202 @@
|
|||
#!/bin/bash
|
||||
|
||||
# This script trains LMs on the WSJ LM-training data.
|
||||
# It requires that you have already run wsj_extend_dict.sh,
|
||||
# to get the larger-size dictionary including all of CMUdict
|
||||
# plus any OOVs and possible acronyms that we could easily
|
||||
# derive pronunciations for.
|
||||
|
||||
# This script takes no command-line arguments
|
||||
|
||||
dir=data/local/local_lm
|
||||
srcdir=data/local/dict_larger
|
||||
mkdir -p $dir
|
||||
. ./path.sh || exit 1; # for KALDI_ROOT
|
||||
export PATH=$KALDI_ROOT/tools/kaldi_lm:$PATH
|
||||
( # First make sure the kaldi_lm toolkit is installed.
|
||||
cd $KALDI_ROOT/tools || exit 1;
|
||||
if [ -d kaldi_lm ]; then
|
||||
echo Not installing the kaldi_lm toolkit since it is already there.
|
||||
else
|
||||
echo Downloading and installing the kaldi_lm tools
|
||||
if [ ! -f kaldi_lm.tar.gz ]; then
|
||||
wget http://merlin.fit.vutbr.cz/kaldi/kaldi_lm.tar.gz || exit 1;
|
||||
fi
|
||||
tar -xvzf kaldi_lm.tar.gz || exit 1;
|
||||
cd kaldi_lm
|
||||
make || exit 1;
|
||||
echo Done making the kaldi_lm tools
|
||||
fi
|
||||
) || exit 1;
|
||||
|
||||
|
||||
|
||||
if [ ! -f $srcdir/cleaned.gz -o ! -f $srcdir/lexicon.txt ]; then
|
||||
echo "Expecting files $srcdir/cleaned.gz and $srcdir/lexicon.txt to exist";
|
||||
echo "You need to run local/wsj_extend_dict.sh before running this script."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
# Get a wordlist-- keep everything but silence, which should not appear in
|
||||
# the LM.
|
||||
awk '{print $1}' $srcdir/lexicon.txt | grep -v -w '!SIL' > $dir/wordlist.txt
|
||||
|
||||
# Get training data with OOV words (w.r.t. our current vocab) replaced with <UNK>.
|
||||
echo "Getting training data with OOV words replaced with <UNK> (train_nounk.gz)"
|
||||
gunzip -c $srcdir/cleaned.gz | awk -v w=$dir/wordlist.txt \
|
||||
'BEGIN{while((getline<w)>0) v[$1]=1;}
|
||||
{for (i=1;i<=NF;i++) if ($i in v) printf $i" ";else printf "<UNK> ";print ""}'|sed 's/ $//g' \
|
||||
| gzip -c > $dir/train_nounk.gz
|
||||
|
||||
# Get unigram counts (without bos/eos, but this doens't matter here, it's
|
||||
# only to get the word-map, which treats them specially & doesn't need their
|
||||
# counts).
|
||||
# Add a 1-count for each word in word-list by including that in the data,
|
||||
# so all words appear.
|
||||
gunzip -c $dir/train_nounk.gz | cat - $dir/wordlist.txt | \
|
||||
awk '{ for(x=1;x<=NF;x++) count[$x]++; } END{for(w in count){print count[w], w;}}' | \
|
||||
sort -nr > $dir/unigram.counts
|
||||
|
||||
# Get "mapped" words-- a character encoding of the words that makes the common words very short.
|
||||
cat $dir/unigram.counts | awk '{print $2}' | get_word_map.pl "<s>" "</s>" "<UNK>" > $dir/word_map
|
||||
|
||||
gunzip -c $dir/train_nounk.gz | awk -v wmap=$dir/word_map 'BEGIN{while((getline<wmap)>0)map[$1]=$2;}
|
||||
{ for(n=1;n<=NF;n++) { printf map[$n]; if(n<NF){ printf " "; } else { print ""; }}}' | gzip -c >$dir/train.gz
|
||||
|
||||
# To save disk space, remove the un-mapped training data. We could
|
||||
# easily generate it again if needed.
|
||||
rm $dir/train_nounk.gz
|
||||
|
||||
train_lm.sh --arpa --lmtype 3gram-mincount $dir
|
||||
#Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 141.444826
|
||||
# 7.8 million N-grams.
|
||||
|
||||
prune_lm.sh --arpa 6.0 $dir/3gram-mincount/
|
||||
# 1.45 million N-grams.
|
||||
# Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 165.394139
|
||||
|
||||
train_lm.sh --arpa --lmtype 4gram-mincount $dir
|
||||
#Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 126.734180
|
||||
# 10.3 million N-grams.
|
||||
|
||||
prune_lm.sh --arpa 7.0 $dir/4gram-mincount
|
||||
# 1.50 million N-grams
|
||||
# Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 155.663757
|
||||
|
||||
|
||||
exit 0
|
||||
|
||||
### Below here, this script is showing various commands that
|
||||
## were run during LM tuning.
|
||||
|
||||
train_lm.sh --arpa --lmtype 3gram-mincount $dir
|
||||
#Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 141.444826
|
||||
# 7.8 million N-grams.
|
||||
|
||||
prune_lm.sh --arpa 3.0 $dir/3gram-mincount/
|
||||
#Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 156.408740
|
||||
# 2.5 million N-grams.
|
||||
|
||||
prune_lm.sh --arpa 6.0 $dir/3gram-mincount/
|
||||
# 1.45 million N-grams.
|
||||
# Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 165.394139
|
||||
|
||||
train_lm.sh --arpa --lmtype 4gram-mincount $dir
|
||||
#Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 126.734180
|
||||
# 10.3 million N-grams.
|
||||
|
||||
prune_lm.sh --arpa 3.0 $dir/4gram-mincount
|
||||
#Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 143.206294
|
||||
# 2.6 million N-grams.
|
||||
|
||||
prune_lm.sh --arpa 4.0 $dir/4gram-mincount
|
||||
# Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 146.927717
|
||||
# 2.15 million N-grams.
|
||||
|
||||
prune_lm.sh --arpa 5.0 $dir/4gram-mincount
|
||||
# 1.86 million N-grams
|
||||
# Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 150.162023
|
||||
|
||||
prune_lm.sh --arpa 7.0 $dir/4gram-mincount
|
||||
# 1.50 million N-grams
|
||||
# Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 155.663757
|
||||
|
||||
train_lm.sh --arpa --lmtype 3gram $dir
|
||||
# Perplexity over 228518.000000 words (excluding 478.000000 OOVs) is 135.692866
|
||||
# 20.0 million N-grams
|
||||
|
||||
! which ngram-count \
|
||||
&& echo "SRILM tools not installed so not doing the comparison" && exit 1;
|
||||
|
||||
#################
|
||||
# You could finish the script here if you wanted.
|
||||
# Below is to show how to do baselines with SRILM.
|
||||
# You'd have to install the SRILM toolkit first.
|
||||
|
||||
heldout_sent=10000 # Don't change this if you want result to be comparable with
|
||||
# kaldi_lm results
|
||||
sdir=$dir/srilm # in case we want to use SRILM to double-check perplexities.
|
||||
mkdir -p $sdir
|
||||
gunzip -c $srcdir/cleaned.gz | head -$heldout_sent > $sdir/cleaned.heldout
|
||||
gunzip -c $srcdir/cleaned.gz | tail -n +$heldout_sent > $sdir/cleaned.train
|
||||
(echo "<s>"; echo "</s>" ) | cat - $dir/wordlist.txt > $sdir/wordlist.final.s
|
||||
|
||||
# 3-gram:
|
||||
ngram-count -text $sdir/cleaned.train -order 3 -limit-vocab -vocab $sdir/wordlist.final.s -unk \
|
||||
-map-unk "<UNK>" -kndiscount -interpolate -lm $sdir/srilm.o3g.kn.gz
|
||||
ngram -lm $sdir/srilm.o3g.kn.gz -ppl $sdir/cleaned.heldout # consider -debug 2
|
||||
#file data/local/local_lm/srilm/cleaned.heldout: 10000 sentences, 218996 words, 478 OOVs
|
||||
#0 zeroprobs, logprob= -491456 ppl= 141.457 ppl1= 177.437
|
||||
|
||||
# Trying 4-gram:
|
||||
ngram-count -text $sdir/cleaned.train -order 4 -limit-vocab -vocab $sdir/wordlist.final.s -unk \
|
||||
-map-unk "<UNK>" -kndiscount -interpolate -lm $sdir/srilm.o4g.kn.gz
|
||||
ngram -order 4 -lm $sdir/srilm.o4g.kn.gz -ppl $sdir/cleaned.heldout
|
||||
#file data/local/local_lm/srilm/cleaned.heldout: 10000 sentences, 218996 words, 478 OOVs
|
||||
#0 zeroprobs, logprob= -480939 ppl= 127.233 ppl1= 158.822
|
||||
|
||||
#3-gram with pruning:
|
||||
ngram-count -text $sdir/cleaned.train -order 3 -limit-vocab -vocab $sdir/wordlist.final.s -unk \
|
||||
-prune 0.0000001 -map-unk "<UNK>" -kndiscount -interpolate -lm $sdir/srilm.o3g.pr7.kn.gz
|
||||
ngram -lm $sdir/srilm.o3g.pr7.kn.gz -ppl $sdir/cleaned.heldout
|
||||
#file data/local/local_lm/srilm/cleaned.heldout: 10000 sentences, 218996 words, 478 OOVs
|
||||
#0 zeroprobs, logprob= -510828 ppl= 171.947 ppl1= 217.616
|
||||
# Around 2.25M N-grams.
|
||||
# Note: this is closest to the experiment done with "prune_lm.sh --arpa 3.0 $dir/3gram-mincount/"
|
||||
# above, which gave 2.5 million N-grams and a perplexity of 156.
|
||||
|
||||
# Note: all SRILM experiments above fully discount all singleton 3 and 4-grams.
|
||||
# You can use -gt3min=0 and -gt4min=0 to stop this (this will be comparable to
|
||||
# the kaldi_lm experiments above without "-mincount".
|
||||
|
||||
## From here is how to train with
|
||||
# IRSTLM. This is not really working at the moment.
|
||||
export IRSTLM=$KALDI_ROOT/tools/irstlm/
|
||||
|
||||
idir=$dir/irstlm
|
||||
mkdir $idir
|
||||
gunzip -c $srcdir/cleaned.gz | tail -n +$heldout_sent | $IRSTLM/scripts/add-start-end.sh | \
|
||||
gzip -c > $idir/train.gz
|
||||
|
||||
$IRSTLM/bin/dict -i=WSJ.cleaned.irstlm.txt -o=dico -f=y -sort=no
|
||||
cat dico | gawk 'BEGIN{while (getline<"vocab.20k.nooov") v[$1]=1; print "DICTIONARY 0 "length(v);}FNR>1{if ($1 in v)\
|
||||
{print $0;}}' > vocab.irstlm.20k
|
||||
|
||||
|
||||
$IRSTLM/bin/build-lm.sh -i "gunzip -c $idir/train.gz" -o $idir/lm_3gram.gz -p yes \
|
||||
-n 3 -s improved-kneser-ney -b yes
|
||||
# Testing perplexity with SRILM tools:
|
||||
ngram -lm $idir/lm_3gram.gz -ppl $sdir/cleaned.heldout
|
||||
#data/local/local_lm/irstlm/lm_3gram.gz: line 162049: warning: non-zero probability for <unk> in closed-vocabulary LM
|
||||
#file data/local/local_lm/srilm/cleaned.heldout: 10000 sentences, 218996 words, 0 OOVs
|
||||
#0 zeroprobs, logprob= -513670 ppl= 175.041 ppl1= 221.599
|
||||
|
||||
# Perplexity is very bad (should be ~141, since we used -p option,
|
||||
# not 175),
|
||||
# but adding -debug 3 to the command line shows that
|
||||
# the IRSTLM LM does not seem to sum to one properly, so it seems that
|
||||
# it produces an LM that isn't interpretable in the normal way as an ARPA
|
||||
# LM.
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,153 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (author: Daniel Povey) Tony Robinson
|
||||
|
||||
# This script trains LMs on the WSJ LM-training data.
|
||||
# It requires that you have already run wsj_extend_dict.sh,
|
||||
# to get the larger-size dictionary including all of CMUdict
|
||||
# plus any OOVs and possible acronyms that we could easily
|
||||
# derive pronunciations for.
|
||||
|
||||
# This script takes no command-line arguments but takes the --cmd option.
|
||||
|
||||
# Begin configuration section.
|
||||
rand_seed=0
|
||||
cmd=run.pl
|
||||
nwords=10000 # This is how many words we're putting in the vocab of the RNNLM.
|
||||
hidden=30
|
||||
class=200 # Num-classes... should be somewhat larger than sqrt of nwords.
|
||||
direct=1000 # Probably number of megabytes to allocate for hash-table for "direct" connections.
|
||||
rnnlm_ver=rnnlm-0.3e # version of RNNLM to use
|
||||
# End configuration section.
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh
|
||||
. utils/parse_options.sh
|
||||
|
||||
if [ $# != 1 ]; then
|
||||
echo "Usage: local/wsj_train_rnnlms.sh [options] <dest-dir>"
|
||||
echo "For options, see top of script file"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
dir=$1
|
||||
srcdir=data/local/dict_larger
|
||||
mkdir -p $dir
|
||||
|
||||
export PATH=$KALDI_ROOT/tools/$rnnlm_ver:$PATH
|
||||
|
||||
|
||||
( # First make sure the kaldi_lm toolkit is installed.
|
||||
# Note: this didn't work out of the box for me, I had to
|
||||
# change the g++ version to just "g++" (no cross-compilation
|
||||
# needed for me as I ran on a machine that had been setup
|
||||
# as 64 bit by default.
|
||||
cd $KALDI_ROOT/tools || exit 1;
|
||||
if [ -d $rnnlm_ver ]; then
|
||||
echo Not installing the rnnlm toolkit since it is already there.
|
||||
else
|
||||
echo Downloading and installing the rnnlm tools
|
||||
# http://www.fit.vutbr.cz/~imikolov/rnnlm/$rnnlm_ver.tgz
|
||||
if [ ! -f $rnnlm_ver.tgz ]; then
|
||||
wget http://www.fit.vutbr.cz/~imikolov/rnnlm/$rnnlm_ver.tgz || exit 1;
|
||||
fi
|
||||
mkdir $rnnlm_ver
|
||||
cd $rnnlm_ver
|
||||
tar -xvzf ../$rnnlm_ver.tgz || exit 1;
|
||||
make CC=g++ || exit 1;
|
||||
echo Done making the rnnlm tools
|
||||
fi
|
||||
) || exit 1;
|
||||
|
||||
|
||||
if [ ! -f $srcdir/cleaned.gz -o ! -f $srcdir/lexicon.txt ]; then
|
||||
echo "Expecting files $srcdir/cleaned.gz and $srcdir/wordlist.final to exist";
|
||||
echo "You need to run local/wsj_extend_dict.sh before running this script."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
cat $srcdir/lexicon.txt | awk '{print $1}' | grep -v -w '!SIL' > $dir/wordlist.all
|
||||
|
||||
# Get training data with OOV words (w.r.t. our current vocab) replaced with <UNK>.
|
||||
echo "Getting training data with OOV words replaced with <UNK> (train_nounk.gz)"
|
||||
gunzip -c $srcdir/cleaned.gz | awk -v w=$dir/wordlist.all \
|
||||
'BEGIN{while((getline<w)>0) v[$1]=1;}
|
||||
{for (i=1;i<=NF;i++) if ($i in v) printf $i" ";else printf "<UNK> ";print ""}'|sed 's/ $//g' \
|
||||
| gzip -c > $dir/all.gz
|
||||
|
||||
echo "Splitting data into train and validation sets."
|
||||
heldout_sent=10000
|
||||
gunzip -c $dir/all.gz | head -n $heldout_sent > $dir/valid.in # validation data
|
||||
gunzip -c $dir/all.gz | tail -n +$heldout_sent | \
|
||||
perl -e ' use List::Util qw(shuffle); @A=<>; print join("", shuffle(@A)); ' \
|
||||
> $dir/train.in # training data
|
||||
|
||||
|
||||
# The rest will consist of a word-class represented by <RNN_UNK>, that
|
||||
# maps (with probabilities) to a whole class of words.
|
||||
|
||||
# Get unigram counts from our training data, and use this to select word-list
|
||||
# for RNNLM training; e.g. 10k most frequent words. Rest will go in a class
|
||||
# that we (manually, at the shell level) assign probabilities for words that
|
||||
# are in that class. Note: this word-list doesn't need to include </s>; this
|
||||
# automatically gets added inside the rnnlm program.
|
||||
# Note: by concatenating with $dir/wordlist.all, we are doing add-one
|
||||
# smoothing of the counts.
|
||||
|
||||
cat $dir/train.in $dir/wordlist.all | grep -v '</s>' | grep -v '<s>' | \
|
||||
awk '{ for(x=1;x<=NF;x++) count[$x]++; } END{for(w in count){print count[w], w;}}' | \
|
||||
sort -nr > $dir/unigram.counts
|
||||
|
||||
head -$nwords $dir/unigram.counts | awk '{print $2}' > $dir/wordlist.rnn
|
||||
|
||||
tail -n +$nwords $dir/unigram.counts > $dir/unk_class.counts
|
||||
|
||||
tot=`awk '{x=x+$1} END{print x}' $dir/unk_class.counts`
|
||||
awk -v tot=$tot '{print $2, ($1*1.0/tot);}' <$dir/unk_class.counts >$dir/unk.probs
|
||||
|
||||
|
||||
for type in train valid; do
|
||||
cat $dir/$type.in | awk -v w=$dir/wordlist.rnn \
|
||||
'BEGIN{while((getline<w)>0) v[$1]=1;}
|
||||
{for (i=1;i<=NF;i++) if ($i in v) printf $i" ";else printf "<RNN_UNK> ";print ""}'|sed 's/ $//g' \
|
||||
> $dir/$type
|
||||
done
|
||||
rm $dir/train.in # no longer needed-- and big.
|
||||
|
||||
# Now randomize the order of the training data.
|
||||
cat $dir/train | awk -v rand_seed=$rand_seed 'BEGIN{srand(rand_seed);} {printf("%f\t%s\n", rand(), $0);}' | \
|
||||
sort | cut -f 2 > $dir/foo
|
||||
mv $dir/foo $dir/train
|
||||
|
||||
# OK we'll train the RNNLM on this data.
|
||||
|
||||
# todo: change 100 to 320.
|
||||
# using 100 classes as square root of 10k.
|
||||
echo "Training RNNLM (note: this uses a lot of memory! Run it on a big machine.)"
|
||||
#time rnnlm -train $dir/train -valid $dir/valid -rnnlm $dir/100.rnnlm \
|
||||
# -hidden 100 -rand-seed 1 -debug 2 -class 100 -bptt 2 -bptt-block 20 \
|
||||
# -direct-order 4 -direct 1000 -binary >& $dir/rnnlm1.log &
|
||||
|
||||
$cmd $dir/rnnlm.log \
|
||||
$KALDI_ROOT/tools/$rnnlm_ver/rnnlm -independent -train $dir/train -valid $dir/valid \
|
||||
-rnnlm $dir/rnnlm -hidden $hidden -rand-seed 1 -debug 2 -class $class -bptt 2 -bptt-block 20 \
|
||||
-direct-order 4 -direct $direct -binary || exit 1;
|
||||
|
||||
|
||||
# make it like a Kaldi table format, with fake utterance-ids.
|
||||
cat $dir/valid.in | awk '{ printf("uttid-%d ", NR); print; }' > $dir/valid.with_ids
|
||||
|
||||
utils/rnnlm_compute_scores.sh $dir $dir/tmp.valid $dir/valid.with_ids \
|
||||
$dir/valid.scores
|
||||
nw=`wc -w < $dir/valid.with_ids` # Note: valid.with_ids includes utterance-ids which
|
||||
# is one per word, to account for the </s> at the end of each sentence; this is the
|
||||
# correct number to normalize buy.
|
||||
p=`awk -v nw=$nw '{x=x+$2} END{print exp(x/nw);}' <$dir/valid.scores`
|
||||
echo Perplexity is $p | tee $dir/perplexity.log
|
||||
|
||||
rm $dir/train $dir/all.gz
|
||||
|
||||
# This is a better setup, but takes a long time to train:
|
||||
#echo "Training RNNLM (note: this uses a lot of memory! Run it on a big machine.)"
|
||||
#time rnnlm -train $dir/train -valid $dir/valid -rnnlm $dir/320.rnnlm \
|
||||
# -hidden 320 -rand-seed 1 -debug 2 -class 300 -bptt 2 -bptt-block 20 \
|
||||
# -direct-order 4 -direct 2000 -binary
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
export KALDI_ROOT=`pwd`/../../..
|
||||
export PATH=$PWD/utils/:$KALDI_ROOT/src/bin:$KALDI_ROOT/tools/openfst/bin:$KALDI_ROOT/src/fstbin/:$KALDI_ROOT/src/gmmbin/:$KALDI_ROOT/src/featbin/:$KALDI_ROOT/src/lm/:$KALDI_ROOT/src/sgmmbin/:$KALDI_ROOT/src/sgmm2bin/:$KALDI_ROOT/src/fgmmbin/:$KALDI_ROOT/src/latbin/:$KALDI_ROOT/src/nnetbin:$KALDI_ROOT/src/nnet-cpubin/:$KALDI_ROOT/src/kwsbin:$PWD:$PATH
|
||||
export LC_ALL=C
|
||||
|
|
@ -0,0 +1,261 @@
|
|||
#!/bin/bash
|
||||
|
||||
. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
|
||||
## This relates to the queue.
|
||||
|
||||
# This is a shell script, but it's recommended that you run the commands one by
|
||||
# one by copying and pasting into the shell.
|
||||
|
||||
case 0 in #goto here
|
||||
1)
|
||||
;; #here:
|
||||
esac
|
||||
|
||||
#exit 1;
|
||||
#need wsj0 for the clean version and LMs
|
||||
wsj0=/mnt/spdb/wall_street_journal
|
||||
local/clean_wsj0_data_prep.sh $wsj0
|
||||
|
||||
reverb=/mnt/spdb/CHiME/chime2-wsj0/reverberated
|
||||
local/reverb_wsj0_data_prep.sh $reverb
|
||||
|
||||
noisy=/mnt/spdb/CHiME/chime2-wsj0/isolated
|
||||
local/noisy_wsj0_data_prep.sh $noisy
|
||||
|
||||
local/wsj_prepare_dict.sh || exit 1;
|
||||
|
||||
utils/prepare_lang.sh data/local/dict "<SPOKEN_NOISE>" data/local/lang_tmp data/lang || exit 1;
|
||||
|
||||
local/chime_format_data.sh || exit 1;
|
||||
|
||||
# Now make MFCC features.
|
||||
# mfccdir should be some place with a largish disk where you
|
||||
# want to store MFCC features.
|
||||
|
||||
mfccdir=mfcc
|
||||
for x in test_eval92_clean test_eval92_5k_clean dev_dt_05_clean dev_dt_20_clean train_si84_clean; do
|
||||
steps/make_mfcc.sh --nj 10 \
|
||||
data/$x exp/make_mfcc/$x $mfccdir || exit 1;
|
||||
steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
|
||||
done
|
||||
|
||||
# Note: the --boost-silence option should probably be omitted by default
|
||||
# for normal setups. It doesn't always help. [it's to discourage non-silence
|
||||
# models from modeling silence.]
|
||||
mfccdir=mfcc
|
||||
for x in test_eval92_5k_noisy dev_dt_05_noisy train_si84_noisy; do
|
||||
steps/make_mfcc.sh --nj 10 \
|
||||
data/$x exp/make_mfcc/$x $mfccdir || exit 1;
|
||||
steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
|
||||
done
|
||||
|
||||
mfccdir=mfcc
|
||||
for x in dev_dt_05_reverb train_si84_reverb; do
|
||||
steps/make_mfcc.sh --nj 10 \
|
||||
data/$x exp/make_mfcc/$x $mfccdir || exit 1;
|
||||
steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
|
||||
done
|
||||
|
||||
#begin train gmm systems using multi condition data
|
||||
#train_si84 = clean+reverb+noisy,
|
||||
for s in train_si84 ; do
|
||||
mkdir -p data/$s
|
||||
cp data/${s}_clean/spk2gender data/$s/
|
||||
for x in text wav.scp; do
|
||||
cat data/${s}_clean/$x data/${s}_reverb/$x data/${s}_noisy/$x | sort -k1 > data/$s/$x
|
||||
done
|
||||
cat data/$s/wav.scp | awk '{print $1}' | perl -ane 'chop; m:^...:; print "$_ $&\n";' > data/$s/utt2spk
|
||||
cat data/$s/utt2spk | utils/utt2spk_to_spk2utt.pl > data/$s/spk2utt
|
||||
done
|
||||
|
||||
mfccdir=mfcc
|
||||
for x in train_si84; do
|
||||
steps/make_mfcc.sh --nj 10 \
|
||||
data/$x exp/make_mfcc/$x $mfccdir || exit 1;
|
||||
steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
|
||||
done
|
||||
|
||||
steps/train_mono.sh --boost-silence 1.25 --nj 10 \
|
||||
data/train_si84 data/lang exp/mono0a || exit 1;
|
||||
|
||||
|
||||
|
||||
utils/mkgraph.sh --mono data/lang_test_tgpr_5k exp/mono0a exp/mono0a/graph_tgpr_5k
|
||||
#steps/decode.sh --nj 8 \
|
||||
# exp/mono0a/graph_tgpr_5k data/test_eval92_5k_clean exp/mono0a/decode_tgpr_eval92_5k_clean
|
||||
steps/decode.sh --nj 8 \
|
||||
exp/mono0a/graph_tgpr_5k data/test_eval92_5k_noisy exp/mono0a/decode_tgpr_eval92_5k_noisy
|
||||
|
||||
|
||||
steps/align_si.sh --boost-silence 1.25 --nj 10 \
|
||||
data/train_si84 data/lang exp/mono0a exp/mono0a_ali || exit 1;
|
||||
|
||||
steps/train_deltas.sh --boost-silence 1.25 \
|
||||
2000 10000 data/train_si84 data/lang exp/mono0a_ali exp/tri1 || exit 1;
|
||||
|
||||
while [ ! -f data/lang_test_tgpr/tmp/LG.fst ] || \
|
||||
[ -z data/lang_test_tgpr/tmp/LG.fst ]; do
|
||||
sleep 20;
|
||||
done
|
||||
sleep 30;
|
||||
# or the mono mkgraph.sh might be writing
|
||||
# data/lang_test_tgpr/tmp/LG.fst which will cause this to fail.
|
||||
|
||||
utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri1 exp/tri1/graph_tgpr_5k || exit 1;
|
||||
|
||||
#steps/decode.sh --nj 8 \
|
||||
# exp/tri1/graph_tgpr data/test_eval92_5k_clean exp/tri1/decode_tgpr_eval92_5k_clean || exit 1;
|
||||
steps/decode.sh --nj 8 \
|
||||
exp/tri1/graph_tgpr_5k data/test_eval92_5k_noisy exp/tri1/decode_tgpr_eval92_5k_noisy || exit 1;
|
||||
|
||||
|
||||
# test various modes of LM rescoring (4 is the default one).
|
||||
# This is just confirming they're equivalent.
|
||||
#for mode in 1 2 3 4; do
|
||||
#steps/lmrescore.sh --mode $mode --cmd "$decode_cmd" data/lang_test_{tgpr,tg} \
|
||||
# data/test_dev93 exp/tri1/decode_tgpr_dev93 exp/tri1/decode_tgpr_dev93_tg$mode || exit 1;
|
||||
#done
|
||||
|
||||
# demonstrate how to get lattices that are "word-aligned" (arcs coincide with
|
||||
# words, with boundaries in the right place).
|
||||
#sil_label=`grep '!SIL' data/lang_test_tgpr/words.txt | awk '{print $2}'`
|
||||
#steps/word_align_lattices.sh --cmd "$train_cmd" --silence-label $sil_label \
|
||||
# data/lang_test_tgpr exp/tri1/decode_tgpr_dev93 exp/tri1/decode_tgpr_dev93_aligned || exit 1;
|
||||
|
||||
steps/align_si.sh --nj 10 \
|
||||
data/train_si84 data/lang exp/tri1 exp/tri1_ali_si84 || exit 1;
|
||||
|
||||
# Train tri2a, which is deltas + delta-deltas, on si84 data.
|
||||
steps/train_deltas.sh \
|
||||
2500 15000 data/train_si84 data/lang exp/tri1_ali_si84 exp/tri2a || exit 1;
|
||||
|
||||
utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri2a exp/tri2a/graph_tgpr_5k || exit 1;
|
||||
|
||||
#steps/decode.sh --nj 8 \
|
||||
# exp/tri2a/graph_tgpr_5k data/test_eval92_5k_clean exp/tri2a/decode_tgpr_eval92_5k_clean || exit 1;
|
||||
steps/decode.sh --nj 8 \
|
||||
exp/tri2a/graph_tgpr_5k data/test_eval92_5k_noisy exp/tri2a/decode_tgpr_eval92_5k_noisy|| exit 1;
|
||||
|
||||
#utils/mkgraph.sh data/lang_test_bg_5k exp/tri2a exp/tri2a/graph_bg5k
|
||||
#steps/decode.sh --nj 8 \
|
||||
# exp/tri2a/graph_bg5k data/test_eval92_5k_clean exp/tri2a/decode_bg_eval92_5k_clean || exit 1;
|
||||
|
||||
steps/train_lda_mllt.sh \
|
||||
--splice-opts "--left-context=3 --right-context=3" \
|
||||
2500 15000 data/train_si84 data/lang exp/tri1_ali_si84 exp/tri2b || exit 1;
|
||||
|
||||
utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri2b exp/tri2b/graph_tgpr_5k || exit 1;
|
||||
steps/decode.sh --nj 8 \
|
||||
exp/tri2b/graph_tgpr_5k data/test_eval92_5k_noisy exp/tri2b/decode_tgpr_eval92_5k_noisy || exit 1;
|
||||
#steps/decode.sh --nj 8 \
|
||||
# exp/tri2b/graph_tgpr data/test_eval92_clean exp/tri2b/decode_tgpr_eval92_clean || exit 1;
|
||||
|
||||
|
||||
# Align tri2b system with si84 data.
|
||||
steps/align_si.sh --nj 10 \
|
||||
--use-graphs true data/train_si84 data/lang exp/tri2b exp/tri2b_ali_si84 || exit 1;
|
||||
|
||||
|
||||
# From 2b system, train 3b which is LDA + MLLT + SAT.
|
||||
steps/train_sat.sh \
|
||||
2500 15000 data/train_si84 data/lang exp/tri2b_ali_si84 exp/tri3b || exit 1;
|
||||
utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri3b exp/tri3b/graph_tgpr_5k || exit 1;
|
||||
steps/decode_fmllr.sh --nj 8 \
|
||||
exp/tri3b/graph_tgpr_5k data/test_eval92_5k_noisy exp/tri3b/decode_tgpr_eval92_5k_noisy || exit 1;
|
||||
|
||||
|
||||
# From 3b multi-condition system, align noisy si84 data.
|
||||
steps/align_fmllr.sh --nj 10 \
|
||||
data/train_si84_noisy data/lang exp/tri3b exp/tri3b_ali_si84_noisy || exit 1;
|
||||
|
||||
steps/align_fmllr.sh --nj 10 \
|
||||
data/dev_dt_05_noisy data/lang exp/tri3b exp/tri3b_ali_dev_dt_05 || exit 1;
|
||||
|
||||
#begin training DNN-HMM system
|
||||
#only on noisy si84
|
||||
|
||||
. ./path.sh
|
||||
#RBM pretraining
|
||||
dir=exp/tri4a_dnn_pretrain
|
||||
$cuda_cmd $dir/_pretrain_dbn.log \
|
||||
steps/pretrain_dbn.sh --use-gpu-id 0 --nn-depth 7 --rbm-iter 3 data-fbank/train_si84_noisy $dir
|
||||
#BP
|
||||
dir=exp/tri4a_dnn
|
||||
ali=exp/tri3b_ali_si84_noisy
|
||||
ali_dev=exp/tri3b_ali_dev_dt_05
|
||||
feature_transform=exp/tri4a_dnn_pretrain/final.feature_transform
|
||||
dbn=exp/tri4a_dnn_pretrain/7.dbn
|
||||
$cuda_cmd $dir/_train_nnet.log \
|
||||
steps/train_nnet.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 --use-gpu-id 0 \
|
||||
data-fbank/train_si84_noisy data-fbank/dev_dt_05_noisy data/lang $ali $ali_dev $dir || exit 1;
|
||||
|
||||
utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri4a_dnn exp/tri4a_dnn/graph_tgpr_5k || exit 1;
|
||||
steps/decode_nnet.sh --nj 8 --acwt 0.10 --config conf/decode_dnn.config \
|
||||
exp/tri4a_dnn/graph_tgpr_5k data-fbank/test_eval92_5k_noisy $dir/decode_tgpr_5k_eval92_5k_noisy || exit 1;
|
||||
|
||||
#Retrain system using new ali,
|
||||
#this is essential
|
||||
#repeat this process for 3 times
|
||||
srcdir=exp/tri4a_dnn
|
||||
steps/align_nnet.sh --nj 10 \
|
||||
data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_ali_si84_noisy || exit 1;
|
||||
steps/align_nnet.sh --nj 10 \
|
||||
data-fbank/dev_dt_05_noisy data/lang $srcdir ${srcdir}_ali_dt_05_noisy || exit 1;
|
||||
|
||||
#no need to do pretraining again
|
||||
dir=exp/tri5a_dnn
|
||||
ali=exp/tri4a_dnn_ali_si84_noisy
|
||||
ali_dev=exp/tri4a_dnn_ali_dt_05_noisy
|
||||
feature_transform=exp/tri4a_dnn_pretrain/final.feature_transform
|
||||
dbn=exp/tri4a_dnn_pretrain/7.dbn
|
||||
$cuda_cmd $dir/_train_nnet.log \
|
||||
steps/train_nnet.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 --use-gpu-id 0 \
|
||||
data-fbank/train_si84_noisy data-fbank/dev_dt_05_noisy data/lang $ali $ali_dev $dir || exit 1;
|
||||
|
||||
utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri5a_dnn exp/tri5a_dnn/graph_tgpr_5k || exit 1;
|
||||
steps/decode_nnet.sh --nj 8 --acwt 0.10 --config conf/decode_dnn.config \
|
||||
exp/tri5a_dnn/graph_tgpr_5k data-fbank/test_eval92_5k_noisy $dir/decode_tgpr_5k_eval92_5k_noisy || exit 1;
|
||||
|
||||
|
||||
srcdir=exp/tri5a_dnn
|
||||
steps/align_nnet.sh --nj 10 \
|
||||
data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_ali_si84_noisy || exit 1;
|
||||
steps/align_nnet.sh --nj 10 \
|
||||
data-fbank/dev_dt_05_noisy data/lang $srcdir ${srcdir}_ali_dt_05_noisy || exit 1;
|
||||
|
||||
. ./path.sh
|
||||
dir=exp/tri6a_dnn
|
||||
ali=exp/tri5a_dnn_ali_si84_noisy
|
||||
ali_dev=exp/tri5a_dnn_ali_dt_05_noisy
|
||||
feature_transform=exp/tri4a_dnn_pretrain/final.feature_transform
|
||||
dbn=exp/tri4a_dnn_pretrain/7.dbn
|
||||
$cuda_cmd $dir/_train_nnet.log \
|
||||
steps/train_nnet.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 --use-gpu-id 0 \
|
||||
data-fbank/train_si84_noisy data-fbank/dev_dt_05_noisy data/lang $ali $ali_dev $dir || exit 1;
|
||||
|
||||
utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri6a_dnn exp/tri6a_dnn/graph_tgpr_5k || exit 1;
|
||||
steps/decode_nnet.sh --nj 8 --acwt 0.10 --config conf/decode_dnn.config \
|
||||
exp/tri6a_dnn/graph_tgpr_5k data-fbank/test_eval92_5k_noisy $dir/decode_tgpr_5k_eval92_5k_noisy || exit 1;
|
||||
|
||||
srcdir=exp/tri6a_dnn
|
||||
steps/align_nnet.sh --nj 10 \
|
||||
data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_ali_si84_noisy || exit 1;
|
||||
steps/align_nnet.sh --nj 10 \
|
||||
data-fbank/dev_dt_05_noisy data/lang $srcdir ${srcdir}_ali_dt_05_noisy || exit 1;
|
||||
|
||||
. ./path.sh
|
||||
dir=exp/tri7a_dnn
|
||||
ali=exp/tri6a_dnn_ali_si84_noisy
|
||||
ali_dev=exp/tri6a_dnn_ali_dt_05_noisy
|
||||
feature_transform=exp/tri4a_dnn_pretrain/final.feature_transform
|
||||
dbn=exp/tri4a_dnn_pretrain/7.dbn
|
||||
$cuda_cmd $dir/_train_nnet.log \
|
||||
steps/train_nnet.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 --use-gpu-id 0 \
|
||||
data-fbank/train_si84_noisy data-fbank/dev_dt_05_noisy data/lang $ali $ali_dev $dir || exit 1;
|
||||
|
||||
utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri7a_dnn exp/tri7a_dnn/graph_tgpr_5k || exit 1;
|
||||
steps/decode_nnet.sh --nj 8 --acwt 0.10 --config conf/decode_dnn.config \
|
||||
exp/tri7a_dnn/graph_tgpr_5k data-fbank/test_eval92_5k_noisy $dir/decode_tgpr_5k_eval92_5k_noisy || exit 1;
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,150 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Copyright 2013 GoVivace Inc (Author: Nagendra Goel)
|
||||
# Apache 2.0
|
||||
|
||||
# Computes training alignments; assumes features are (LDA+MLLT or delta+delta-delta)
|
||||
# + fMLLR (probably with SAT models).
|
||||
# It first computes an alignment with the final.alimdl (or the final.mdl if final.alimdl
|
||||
# is not present), then does 2 iterations of fMLLR estimation.
|
||||
|
||||
# If you supply the --use-graphs option, it will use the training
|
||||
# graphs from the source directory (where the model is). In this
|
||||
# case the number of jobs must match the source directory.
|
||||
|
||||
|
||||
# Begin configuration section.
|
||||
stage=0
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
use_graphs=false
|
||||
# Begin configuration.
|
||||
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
|
||||
beam=10
|
||||
retry_beam=40
|
||||
boost_silence=1.5 # factor by which to boost silence during alignment.
|
||||
fmllr_update_type=full
|
||||
# End configuration options.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f path.sh ] && . ./path.sh # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "usage: steps/align_fmllr.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
|
||||
echo "e.g.: steps/align_fmllr.sh data/train data/lang exp/tri1 exp/tri1_ali"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --use-graphs true # use graphs in src-dir"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --fmllr-update-type (full|diag|offset|none) # default full."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
dir=$4
|
||||
graphdir=$dir
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
silphonelist=`cat $lang/phones/silence.csl` || exit 1;
|
||||
sdata=$data/split$nj
|
||||
|
||||
mkdir -p $dir/log
|
||||
echo $nj > $dir/num_jobs
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
|
||||
cp $srcdir/{tree,final.mdl} $dir || exit 1;
|
||||
cp $srcdir/final.occs $dir;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
|
||||
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) sifeats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) sifeats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
cp $srcdir/final.mat $dir
|
||||
;;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
## Set up model and alignment model.
|
||||
mdl=$srcdir/final.mdl
|
||||
if [ -f $srcdir/final.alimdl ]; then
|
||||
alimdl=$srcdir/final.alimdl
|
||||
else
|
||||
alimdl=$srcdir/final.mdl
|
||||
fi
|
||||
[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
|
||||
alimdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/boost_phones.csl` $alimdl - |"
|
||||
mdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $mdl - |"
|
||||
|
||||
## Work out where we're getting the graphs from.
|
||||
if $use_graphs; then
|
||||
[ "$nj" != "`cat $srcdir/num_jobs`" ] && \
|
||||
echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
|
||||
[ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
|
||||
graphdir=$srcdir
|
||||
else
|
||||
graphdir=$dir
|
||||
if [ $stage -le 0 ]; then
|
||||
echo "$0: compiling training graphs"
|
||||
tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
|
||||
$cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \
|
||||
compile-train-graphs $dir/tree $dir/final.mdl $lang/L.fst "$tra" \
|
||||
"ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: aligning data in $data using $alimdl and speaker-independent features."
|
||||
$cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
|
||||
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$alimdl_cmd" \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$sifeats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: computing fMLLR transforms"
|
||||
if [ "$alimdl" != "$mdl" ]; then
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
gmm-post-to-gpost $alimdl "$sifeats" ark:- ark:- \| \
|
||||
gmm-est-basis-fmllr-gpost --fmllr-min-count=22 --num-iters=10 \
|
||||
--size-scale=0.2 --step-size-iters=3 \
|
||||
--write-weights=ark:$dir/pre_wgt.JOB \
|
||||
$mdl $srcdir/fmllr.basis "$sifeats" ark,s,cs:- \
|
||||
ark:$dir/trans.JOB || exit 1;
|
||||
# else
|
||||
# $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
# ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
# weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
# gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \
|
||||
# --spk2utt=ark:$sdata/JOB/spk2utt $mdl "$sifeats" \
|
||||
# ark,s,cs:- ark:$dir/trans.JOB || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
feats="$sifeats transform-feats ark:$dir/pre_trans.JOB ark:- ark:- |"
|
||||
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: doing final alignment."
|
||||
$cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
|
||||
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl_cmd" \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
#rm $dir/pre_ali.*.gz
|
||||
|
||||
echo "$0: done aligning data."
|
||||
|
||||
utils/summarize_warnings.pl $dir/log
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,148 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
# Computes training alignments; assumes features are (LDA+MLLT or delta+delta-delta)
|
||||
# + fMLLR (probably with SAT models).
|
||||
# It first computes an alignment with the final.alimdl (or the final.mdl if final.alimdl
|
||||
# is not present), then does 2 iterations of fMLLR estimation.
|
||||
|
||||
# If you supply the --use-graphs option, it will use the training
|
||||
# graphs from the source directory (where the model is). In this
|
||||
# case the number of jobs must match the source directory.
|
||||
|
||||
|
||||
# Begin configuration section.
|
||||
stage=0
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
use_graphs=false
|
||||
# Begin configuration.
|
||||
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
|
||||
beam=10
|
||||
retry_beam=40
|
||||
boost_silence=1.0 # factor by which to boost silence during alignment.
|
||||
fmllr_update_type=full
|
||||
norm_vars=false
|
||||
# End configuration options.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f path.sh ] && . ./path.sh # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "usage: steps/align_fmllr.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
|
||||
echo "e.g.: steps/align_fmllr.sh data/train data/lang exp/tri1 exp/tri1_ali"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --use-graphs true # use graphs in src-dir"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --fmllr-update-type (full|diag|offset|none) # default full."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
dir=$4
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
silphonelist=`cat $lang/phones/silence.csl` || exit 1;
|
||||
sdata=$data/split$nj
|
||||
|
||||
mkdir -p $dir/log
|
||||
echo $nj > $dir/num_jobs
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
|
||||
cp $srcdir/{tree,final.mdl} $dir || exit 1;
|
||||
cp $srcdir/final.occs $dir;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
|
||||
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) sifeats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) sifeats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
cp $srcdir/final.mat $srcdir/full.mat $dir
|
||||
;;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
## Set up model and alignment model.
|
||||
mdl=$srcdir/final.mdl
|
||||
if [ -f $srcdir/final.alimdl ]; then
|
||||
alimdl=$srcdir/final.alimdl
|
||||
else
|
||||
alimdl=$srcdir/final.mdl
|
||||
fi
|
||||
[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
|
||||
alimdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $alimdl - |"
|
||||
mdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $mdl - |"
|
||||
|
||||
|
||||
## Work out where we're getting the graphs from.
|
||||
if $use_graphs; then
|
||||
[ "$nj" != "`cat $srcdir/num_jobs`" ] && \
|
||||
echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
|
||||
[ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
|
||||
graphdir=$srcdir
|
||||
else
|
||||
graphdir=$dir
|
||||
if [ $stage -le 0 ]; then
|
||||
echo "$0: compiling training graphs"
|
||||
tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
|
||||
$cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \
|
||||
compile-train-graphs $dir/tree $dir/final.mdl $lang/L.fst "$tra" \
|
||||
"ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: aligning data in $data using $alimdl and speaker-independent features."
|
||||
$cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
|
||||
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$alimdl_cmd" \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$sifeats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: computing fMLLR transforms"
|
||||
if [ "$alimdl" != "$mdl" ]; then
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
gmm-post-to-gpost $alimdl "$sifeats" ark:- ark:- \| \
|
||||
gmm-est-fmllr-gpost --fmllr-update-type=$fmllr_update_type \
|
||||
--spk2utt=ark:$sdata/JOB/spk2utt $mdl "$sifeats" \
|
||||
ark,s,cs:- ark:$dir/trans.JOB || exit 1;
|
||||
else
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \
|
||||
--spk2utt=ark:$sdata/JOB/spk2utt $mdl "$sifeats" \
|
||||
ark,s,cs:- ark:$dir/trans.JOB || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
|
||||
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: doing final alignment."
|
||||
$cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
|
||||
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl_cmd" \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
rm $dir/pre_ali.*.gz
|
||||
|
||||
echo "$0: done aligning data."
|
||||
|
||||
utils/summarize_warnings.pl $dir/log
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,99 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Brno University of Technology (Author: Karel Vesely)
|
||||
# Apache 2.0
|
||||
|
||||
# Computes training alignments using MLP model
|
||||
|
||||
# If you supply the "--use-graphs true" option, it will use the training
|
||||
# graphs from the source directory (where the model is). In this
|
||||
# case the number of jobs must match with the source directory.
|
||||
|
||||
|
||||
# Begin configuration section.
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
# Begin configuration.
|
||||
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
|
||||
beam=10
|
||||
retry_beam=40
|
||||
use_gpu_id=-1 # disable gpu
|
||||
# End configuration options.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f path.sh ] && . ./path.sh # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "usage: $0 <data-dir> <lang-dir> <src-dir> <align-dir>"
|
||||
echo "e.g.: $0 data/train data/lang exp/tri1 exp/tri1_ali"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
dir=$4
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
mkdir -p $dir/log
|
||||
echo $nj > $dir/num_jobs
|
||||
sdata=$data/split$nj
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
|
||||
cp $srcdir/{tree,final.mdl} $dir || exit 1;
|
||||
|
||||
#Get the files we will need
|
||||
nnet=$srcdir/final.nnet;
|
||||
[ -z "$nnet" ] && echo "Error nnet '$nnet' does not exist!" && exit 1;
|
||||
|
||||
class_frame_counts=$srcdir/ali_train_pdf.counts
|
||||
[ -z "$class_frame_counts" ] && echo "Error class_frame_counts '$class_frame_counts' does not exist!" && exit 1;
|
||||
|
||||
feature_transform=$srcdir/final.feature_transform
|
||||
if [ ! -f $feature_transform ]; then
|
||||
echo "Missing feature_transform '$feature_transform'"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
model=$dir/final.mdl
|
||||
[ -z "$model" ] && echo "Error transition model '$model' does not exist!" && exit 1;
|
||||
|
||||
###
|
||||
### Prepare feature pipeline (same as for decoding)
|
||||
###
|
||||
# Create the feature stream:
|
||||
feats="ark,s,cs:copy-feats scp:$sdata/JOB/feats.scp ark:- |"
|
||||
# Optionally add cmvn
|
||||
if [ -f $srcdir/norm_vars ]; then
|
||||
norm_vars=$(cat $srcdir/norm_vars 2>/dev/null)
|
||||
[ ! -f $sdata/1/cmvn.scp ] && echo "$0: cannot find cmvn stats $sdata/1/cmvn.scp" && exit 1
|
||||
feats="$feats apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp ark:- ark:- |"
|
||||
fi
|
||||
# Optionally add deltas
|
||||
if [ -f $srcdir/delta_order ]; then
|
||||
delta_order=$(cat $srcdir/delta_order)
|
||||
feats="$feats add-deltas --delta-order=$delta_order ark:- ark:- |"
|
||||
fi
|
||||
|
||||
# Finally add feature_transform and the MLP
|
||||
feats="$feats nnet-forward --feature-transform=$feature_transform --no-softmax=true --class-frame-counts=$class_frame_counts --use-gpu-id=$use_gpu_id $nnet ark:- ark:- |"
|
||||
###
|
||||
###
|
||||
###
|
||||
|
||||
echo "$0: aligning data in $data using model from $srcdir, putting alignments in $dir"
|
||||
|
||||
tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
|
||||
# We could just use gmm-align-mapped in the next line, but it's less efficient as it compiles the
|
||||
# training graphs one by one.
|
||||
$cmd JOB=1:$nj $dir/log/align.JOB.log \
|
||||
compile-train-graphs $dir/tree $dir/final.mdl $lang/L.fst "$tra" ark:- \| \
|
||||
align-compiled-mapped $scale_opts --beam=$beam --retry-beam=$retry_beam $dir/final.mdl ark:- \
|
||||
"$feats" "ark,t:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
|
||||
echo "$0: done aligning data."
|
||||
|
|
@ -0,0 +1,142 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012-2013 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
# Computes training alignments; assumes features are (LDA+MLLT or delta+delta-delta)
|
||||
# + fMLLR (probably with SAT models).
|
||||
# It first computes an alignment with the final.alimdl (or the final.mdl if final.alimdl
|
||||
# is not present), then does 2 iterations of fMLLR estimation.
|
||||
|
||||
# If you supply the --use-graphs option, it will use the training
|
||||
# graphs from the source directory (where the model is). In this
|
||||
# case the number of jobs must match the source directory.
|
||||
|
||||
|
||||
# Begin configuration section.
|
||||
stage=0
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
use_graphs=false
|
||||
# Begin configuration.
|
||||
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
|
||||
beam=10
|
||||
retry_beam=40
|
||||
boost_silence=1.0 # factor by which to boost silence during alignment.
|
||||
norm_vars=false
|
||||
# End configuration options.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f path.sh ] && . ./path.sh # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "usage: steps/align_fmllr.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
|
||||
echo "e.g.: steps/align_fmllr.sh data/train data/lang exp/tri1 exp/tri1_ali"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --use-graphs true # use graphs in src-dir"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
dir=$4
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
silphonelist=`cat $lang/phones/silence.csl` || exit 1;
|
||||
sdata=$data/split$nj
|
||||
|
||||
mkdir -p $dir/log
|
||||
echo $nj > $dir/num_jobs
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
|
||||
cp $srcdir/{tree,final.mdl} $dir || exit 1;
|
||||
cp $srcdir/final.occs $dir;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
|
||||
|
||||
if [[ ! -f $srcdir/final.mat || ! -f $srcdir/full.mat ]]; then
|
||||
echo "$0: we require final.mat and full.mat in the source directory $srcdir"
|
||||
fi
|
||||
|
||||
full_lda_mat="get-full-lda-mat --print-args=false $srcdir/final.mat $srcdir/full.mat -|"
|
||||
cp $srcdir/full.mat $srcdir/final.mat $dir
|
||||
|
||||
splicedfeats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- |"
|
||||
sifeats="$splicedfeats transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
|
||||
## Set up model and alignment model.
|
||||
mdl=$srcdir/final.mdl
|
||||
if [ -f $srcdir/final.alimdl ]; then
|
||||
alimdl=$srcdir/final.alimdl
|
||||
else
|
||||
alimdl=$srcdir/final.mdl
|
||||
fi
|
||||
[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
|
||||
alimdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $alimdl - |"
|
||||
mdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $mdl - |"
|
||||
|
||||
|
||||
## Work out where we're getting the graphs from.
|
||||
if $use_graphs; then
|
||||
[ "$nj" != "`cat $srcdir/num_jobs`" ] && \
|
||||
echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
|
||||
[ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
|
||||
graphdir=$srcdir
|
||||
else
|
||||
graphdir=$dir
|
||||
if [ $stage -le 0 ]; then
|
||||
echo "$0: compiling training graphs"
|
||||
tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
|
||||
$cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \
|
||||
compile-train-graphs $dir/tree $dir/final.mdl $lang/L.fst "$tra" \
|
||||
"ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: aligning data in $data using $alimdl and speaker-independent features."
|
||||
$cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
|
||||
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$alimdl_cmd" \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$sifeats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: computing fMLLR transforms"
|
||||
if [ "$alimdl" != "$mdl" ]; then
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
gmm-post-to-gpost $alimdl "$sifeats" ark:- ark:- \| \
|
||||
gmm-est-fmllr-raw-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
$mdl "$full_lda_mat" "$splicedfeats" ark,s,cs:- ark:$dir/raw_trans.JOB || exit 1;
|
||||
else
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
gmm-est-fmllr-raw --spk2utt=ark:$sdata/JOB/spk2utt $mdl "$full_lda_mat" \
|
||||
"$splicedfeats" ark,s,cs:- ark:$dir/raw_trans.JOB || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
feats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/raw_trans.JOB ark:- ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: doing final alignment."
|
||||
$cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
|
||||
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl_cmd" \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
rm $dir/pre_ali.*.gz
|
||||
|
||||
echo "$0: done aligning data."
|
||||
|
||||
utils/summarize_warnings.pl $dir/log
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,193 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
# Computes training alignments and (if needed) speaker-vectors, given an
|
||||
# SGMM system. If the system is built on top of SAT, you should supply
|
||||
# transforms with the --transform-dir option.
|
||||
|
||||
# If you supply the --use-graphs option, it will use the training
|
||||
# graphs from the source directory.
|
||||
|
||||
# Begin configuration section.
|
||||
stage=0
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
use_graphs=false # use graphs from srcdir
|
||||
use_gselect=false # use gselect info from srcdir [regardless, we use
|
||||
# Gaussian-selection info, we might have to compute it though.]
|
||||
gselect=15 # Number of Gaussian-selection indices for SGMMs.
|
||||
# Begin configuration.
|
||||
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
|
||||
beam=10
|
||||
retry_beam=40
|
||||
transform_dir= # directory to find fMLLR transforms in.
|
||||
# End configuration options.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f path.sh ] && . ./path.sh # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "usage: steps/align_sgmm.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
|
||||
echo "e.g.: steps/align_sgmm.sh --transform-dir exp/tri3b data/train data/lang \\"
|
||||
echo " exp/sgmm4a exp/sgmm5a_ali"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --use-graphs true # use graphs in src-dir"
|
||||
echo " --transform-dir <transform-dir> # directory to find fMLLR transforms"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
dir=$4
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
silphonelist=`cat $lang/phones/silence.csl` || exit 1;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
sdata=$data/split$nj
|
||||
|
||||
mkdir -p $dir/log
|
||||
cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
|
||||
echo $nj > $dir/num_jobs
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
|
||||
cp $srcdir/{tree,final.mdl} $dir || exit 1;
|
||||
[ -f $srcdir/final.alimdl ] && cp $srcdir/final.alimdl $dir
|
||||
cp $srcdir/final.occs $dir;
|
||||
|
||||
## Set up features.
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
cp $srcdir/final.mat $dir
|
||||
;;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then
|
||||
echo "$0: using transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
|
||||
[ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
|
||||
&& echo "$0: #jobs mismatch with transform-dir." && exit 1;
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
|
||||
echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
|
||||
echo " but you are not providing the --transform-dir option during alignment."
|
||||
fi
|
||||
##
|
||||
|
||||
## Set up model and alignment model.
|
||||
mdl=$srcdir/final.mdl
|
||||
if [ -f $srcdir/final.alimdl ]; then
|
||||
alimdl=$srcdir/final.alimdl
|
||||
else
|
||||
alimdl=$srcdir/final.mdl
|
||||
fi
|
||||
[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
|
||||
|
||||
## Work out where we're getting the graphs from.
|
||||
if $use_graphs; then
|
||||
[ "$nj" != "`cat $srcdir/num_jobs`" ] && \
|
||||
echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
|
||||
[ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
|
||||
graphdir=$srcdir
|
||||
ln.pl $srcdir/fsts.*.gz $dir
|
||||
else
|
||||
graphdir=$dir
|
||||
if [ $stage -le 0 ]; then
|
||||
echo "$0: compiling training graphs"
|
||||
tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
|
||||
$cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \
|
||||
compile-train-graphs $dir/tree $dir/final.mdl $lang/L.fst "$tra" \
|
||||
"ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
## Work out where we're getting the Gaussian-selection info from
|
||||
if $use_gselect; then
|
||||
[ "$nj" != "`cat $srcdir/num_jobs`" ] && \
|
||||
echo "$0: you specified --use-gselect true, but #jobs mismatch." && exit 1;
|
||||
[ ! -f $srcdir/gselect.1.gz ] && echo "No gselect info in $srcdir" && exit 1;
|
||||
graphdir=$srcdir
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $srcdir/gselect.JOB.gz|"
|
||||
ln.pl $srcdir/gselect.*.gz $dir
|
||||
else
|
||||
graphdir=$dir
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: computing Gaussian-selection info"
|
||||
# Note: doesn't matter whether we use $alimdl or $mdl, they will
|
||||
# have the same gselect info.
|
||||
$cmd JOB=1:$nj $dir/log/gselect.JOB.log \
|
||||
sgmm-gselect --full-gmm-nbest=$gselect $alimdl \
|
||||
"$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
|
||||
fi
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|"
|
||||
fi
|
||||
|
||||
|
||||
if [ $alimdl == $mdl ]; then
|
||||
# Speaker-independent decoding-- just one pass. Not normal.
|
||||
T=`sgmm-info $mdl | grep 'speaker vector space' | awk '{print $NF}'` || exit 1;
|
||||
[ "$T" -ne 0 ] && echo "No alignment model, yet speaker vector space nonempty" && exit 1;
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: aligning data in $data using model $mdl (no speaker-vectors)"
|
||||
$cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
|
||||
sgmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam $alimdl \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
echo "$0: done aligning data."
|
||||
exit 0;
|
||||
fi
|
||||
|
||||
# Continue with system with speaker vectors.
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: aligning data in $data using model $alimdl"
|
||||
$cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
|
||||
sgmm-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam $alimdl \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: computing speaker vectors (1st pass)"
|
||||
$cmd JOB=1:$nj $dir/log/spk_vecs1.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
sgmm-post-to-gpost "$gselect_opt" $alimdl "$feats" ark:- ark:- \| \
|
||||
sgmm-est-spkvecs-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
$mdl "$feats" ark,s,cs:- ark:$dir/pre_vecs.JOB || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 4 ]; then
|
||||
echo "$0: computing speaker vectors (2nd pass)"
|
||||
$cmd JOB=1:$nj $dir/log/spk_vecs2.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
sgmm-est-spkvecs --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" \
|
||||
--spk-vecs=ark:$dir/pre_vecs.JOB $mdl "$feats" ark,s,cs:- ark:$dir/vecs.JOB || exit 1;
|
||||
rm $dir/pre_vecs.*
|
||||
fi
|
||||
|
||||
if [ $stage -le 5 ]; then
|
||||
echo "$0: doing final alignment."
|
||||
$cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
|
||||
sgmm-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam \
|
||||
--utt2spk=ark:$sdata/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
|
||||
$mdl "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
rm $dir/pre_ali.*.gz
|
||||
|
||||
echo "$0: done aligning data."
|
||||
|
||||
utils/summarize_warnings.pl $dir/log
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,193 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
# Computes training alignments and (if needed) speaker-vectors, given an
|
||||
# SGMM system. If the system is built on top of SAT, you should supply
|
||||
# transforms with the --transform-dir option.
|
||||
|
||||
# If you supply the --use-graphs option, it will use the training
|
||||
# graphs from the source directory.
|
||||
|
||||
# Begin configuration section.
|
||||
stage=0
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
use_graphs=false # use graphs from srcdir
|
||||
use_gselect=false # use gselect info from srcdir [regardless, we use
|
||||
# Gaussian-selection info, we might have to compute it though.]
|
||||
gselect=15 # Number of Gaussian-selection indices for SGMMs.
|
||||
# Begin configuration.
|
||||
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
|
||||
beam=10
|
||||
retry_beam=40
|
||||
transform_dir= # directory to find fMLLR transforms in.
|
||||
# End configuration options.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f path.sh ] && . ./path.sh # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "usage: steps/align_sgmm.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
|
||||
echo "e.g.: steps/align_sgmm.sh --transform-dir exp/tri3b data/train data/lang \\"
|
||||
echo " exp/sgmm4a exp/sgmm5a_ali"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --use-graphs true # use graphs in src-dir"
|
||||
echo " --transform-dir <transform-dir> # directory to find fMLLR transforms"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
dir=$4
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
silphonelist=`cat $lang/phones/silence.csl` || exit 1;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
sdata=$data/split$nj
|
||||
|
||||
mkdir -p $dir/log
|
||||
cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
|
||||
echo $nj > $dir/num_jobs
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
|
||||
cp $srcdir/{tree,final.mdl} $dir || exit 1;
|
||||
[ -f $srcdir/final.alimdl ] && cp $srcdir/final.alimdl $dir
|
||||
cp $srcdir/final.occs $dir;
|
||||
|
||||
## Set up features.
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
cp $srcdir/final.mat $dir
|
||||
;;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then
|
||||
echo "$0: using transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
|
||||
[ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
|
||||
&& echo "$0: #jobs mismatch with transform-dir." && exit 1;
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
|
||||
echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
|
||||
echo " but you are not providing the --transform-dir option during alignment."
|
||||
fi
|
||||
##
|
||||
|
||||
## Set up model and alignment model.
|
||||
mdl=$srcdir/final.mdl
|
||||
if [ -f $srcdir/final.alimdl ]; then
|
||||
alimdl=$srcdir/final.alimdl
|
||||
else
|
||||
alimdl=$srcdir/final.mdl
|
||||
fi
|
||||
[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
|
||||
|
||||
## Work out where we're getting the graphs from.
|
||||
if $use_graphs; then
|
||||
[ "$nj" != "`cat $srcdir/num_jobs`" ] && \
|
||||
echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
|
||||
[ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
|
||||
graphdir=$srcdir
|
||||
ln.pl $srcdir/fsts.*.gz $dir
|
||||
else
|
||||
graphdir=$dir
|
||||
if [ $stage -le 0 ]; then
|
||||
echo "$0: compiling training graphs"
|
||||
tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
|
||||
$cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \
|
||||
compile-train-graphs $dir/tree $dir/final.mdl $lang/L.fst "$tra" \
|
||||
"ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
## Work out where we're getting the Gaussian-selection info from
|
||||
if $use_gselect; then
|
||||
[ "$nj" != "`cat $srcdir/num_jobs`" ] && \
|
||||
echo "$0: you specified --use-gselect true, but #jobs mismatch." && exit 1;
|
||||
[ ! -f $srcdir/gselect.1.gz ] && echo "No gselect info in $srcdir" && exit 1;
|
||||
graphdir=$srcdir
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $srcdir/gselect.JOB.gz|"
|
||||
ln.pl $srcdir/gselect.*.gz $dir
|
||||
else
|
||||
graphdir=$dir
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: computing Gaussian-selection info"
|
||||
# Note: doesn't matter whether we use $alimdl or $mdl, they will
|
||||
# have the same gselect info.
|
||||
$cmd JOB=1:$nj $dir/log/gselect.JOB.log \
|
||||
sgmm2-gselect --full-gmm-nbest=$gselect $alimdl \
|
||||
"$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
|
||||
fi
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|"
|
||||
fi
|
||||
|
||||
|
||||
if [ $alimdl == $mdl ]; then
|
||||
# Speaker-independent decoding-- just one pass. Not normal.
|
||||
T=`sgmm2-info $mdl | grep 'speaker vector space' | awk '{print $NF}'` || exit 1;
|
||||
[ "$T" -ne 0 ] && echo "No alignment model, yet speaker vector space nonempty" && exit 1;
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: aligning data in $data using model $mdl (no speaker-vectors)"
|
||||
$cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
|
||||
sgmm2-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam $alimdl \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
echo "$0: done aligning data."
|
||||
exit 0;
|
||||
fi
|
||||
|
||||
# Continue with system with speaker vectors.
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: aligning data in $data using model $alimdl"
|
||||
$cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
|
||||
sgmm2-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam $alimdl \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: computing speaker vectors (1st pass)"
|
||||
$cmd JOB=1:$nj $dir/log/spk_vecs1.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
sgmm2-post-to-gpost "$gselect_opt" $alimdl "$feats" ark:- ark:- \| \
|
||||
sgmm2-est-spkvecs-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
$mdl "$feats" ark,s,cs:- ark:$dir/pre_vecs.JOB || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 4 ]; then
|
||||
echo "$0: computing speaker vectors (2nd pass)"
|
||||
$cmd JOB=1:$nj $dir/log/spk_vecs2.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
sgmm2-est-spkvecs --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" \
|
||||
--spk-vecs=ark:$dir/pre_vecs.JOB $mdl "$feats" ark,s,cs:- ark:$dir/vecs.JOB || exit 1;
|
||||
rm $dir/pre_vecs.*
|
||||
fi
|
||||
|
||||
if [ $stage -le 5 ]; then
|
||||
echo "$0: doing final alignment."
|
||||
$cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
|
||||
sgmm2-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam \
|
||||
--utt2spk=ark:$sdata/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
|
||||
$mdl "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
rm $dir/pre_ali.*.gz
|
||||
|
||||
echo "$0: done aligning data."
|
||||
|
||||
utils/summarize_warnings.pl $dir/log
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,89 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
# Computes training alignments using a model with delta or
|
||||
# LDA+MLLT features.
|
||||
|
||||
# If you supply the "--use-graphs true" option, it will use the training
|
||||
# graphs from the source directory (where the model is). In this
|
||||
# case the number of jobs must match with the source directory.
|
||||
|
||||
|
||||
# Begin configuration section.
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
use_graphs=false
|
||||
# Begin configuration.
|
||||
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
|
||||
beam=10
|
||||
retry_beam=40
|
||||
boost_silence=1.0 # Factor by which to boost silence during alignment.
|
||||
# End configuration options.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f path.sh ] && . ./path.sh # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "usage: steps/align_si.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
|
||||
echo "e.g.: steps/align_si.sh data/train data/lang exp/tri1 exp/tri1_ali"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --use-graphs true # use graphs in src-dir"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
dir=$4
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
mkdir -p $dir/log
|
||||
echo $nj > $dir/num_jobs
|
||||
sdata=$data/split$nj
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
|
||||
cp $srcdir/{tree,final.mdl} $dir || exit 1;
|
||||
cp $srcdir/final.occs $dir;
|
||||
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
cp $srcdir/final.mat $dir
|
||||
;;
|
||||
*) echo "$0: invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
echo "$0: aligning data in $data using model from $srcdir, putting alignments in $dir"
|
||||
|
||||
mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/final.mdl - |"
|
||||
|
||||
if $use_graphs; then
|
||||
[ $nj != "`cat $srcdir/num_jobs`" ] && echo "$0: mismatch in num-jobs" && exit 1;
|
||||
[ ! -f $srcdir/fsts.1.gz ] && echo "$0: no such file $srcdir/fsts.1.gz" && exit 1;
|
||||
|
||||
$cmd JOB=1:$nj $dir/log/align.JOB.log \
|
||||
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl" \
|
||||
"ark:gunzip -c $srcdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
else
|
||||
tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
|
||||
# We could just use gmm-align in the next line, but it's less efficient as it compiles the
|
||||
# training graphs one by one.
|
||||
$cmd JOB=1:$nj $dir/log/align.JOB.log \
|
||||
compile-train-graphs $dir/tree $dir/final.mdl $lang/L.fst "$tra" ark:- \| \
|
||||
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl" ark:- \
|
||||
"$feats" "ark,t:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
echo "$0: done aligning data."
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
# This script appends the features in two data directories.
|
||||
|
||||
# To be run from .. (one directory up from here)
|
||||
# see ../run.sh for example
|
||||
|
||||
# Begin configuration section.
|
||||
cmd=run.pl
|
||||
nj=4
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
if [ -f path.sh ]; then . ./path.sh; fi
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 5 ]; then
|
||||
echo "usage: append_feats.sh [options] <src-data-dir1> <src-data-dir2> <dest-data-dir> <log-dir> <path-to-storage-dir>";
|
||||
echo "options: "
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data_src1=$1
|
||||
data_src2=$2
|
||||
data=$3
|
||||
logdir=$4
|
||||
mfccdir=$5
|
||||
|
||||
# make $mfccdir an absolute pathname.
|
||||
mfccdir=`perl -e '($dir,$pwd)= @ARGV; if($dir!~m:^/:) { $dir = "$pwd/$dir"; } print $dir; ' $mfccdir ${PWD}`
|
||||
|
||||
utils/split_data.sh $data_src1 $nj || exit 1;
|
||||
utils/split_data.sh $data_src2 $nj || exit 1;
|
||||
|
||||
mkdir -p $mfccdir $logdir
|
||||
|
||||
mkdir -p $data
|
||||
cp $data_src1/* $data/ 2>/dev/null # so we get the other files, such as utt2spk.
|
||||
rm $data/cmvn.scp 2>/dev/null
|
||||
rm -r $data/split* 2>/dev/null
|
||||
|
||||
# use "name" as part of name of the archive.
|
||||
name=`basename $data`
|
||||
|
||||
$cmd JOB=1:$nj $logdir/append.JOB.log \
|
||||
append-feats --truncate-frames=true \
|
||||
scp:$data_src1/split$nj/JOB/feats.scp scp:$data_src2/split$nj/JOB/feats.scp \
|
||||
ark,scp:$mfccdir/appended_$name.JOB.ark,$mfccdir/appended_$name.JOB.scp || exit 1;
|
||||
|
||||
# concatenate the .scp files together.
|
||||
for ((n=1; n<=nj; n++)); do
|
||||
cat $mfccdir/appended_$name.$n.scp >> $data/feats.scp || exit 1;
|
||||
done > $data/feats.scp || exit 1;
|
||||
|
||||
|
||||
nf=`cat $data/feats.scp | wc -l`
|
||||
nu=`cat $data/utt2spk | wc -l`
|
||||
if [ $nf -ne $nu ]; then
|
||||
echo "It seems not all of the feature files were successfully ($nf != $nu);"
|
||||
echo "consider using utils/fix_data_dir.sh $data"
|
||||
fi
|
||||
|
||||
echo "Succeeded creating MFCC features for $name"
|
||||
|
|
@ -0,0 +1,80 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
# To be run from .. (one directory up from here)
|
||||
# see ../run.sh for example
|
||||
|
||||
# Compute cepstral mean and variance statistics per speaker.
|
||||
# We do this in just one job; it's fast.
|
||||
# This script takes no options.
|
||||
#
|
||||
# Note: there is no option to do CMVN per utterance. The idea is
|
||||
# that if you did it per utterance it would not make sense to do
|
||||
# per-speaker fMLLR on top of that (since you'd be doing fMLLR on
|
||||
# top of different offsets). Therefore what would be the use
|
||||
# of the speaker information? In this case you should probably
|
||||
# make the speaker-ids identical to the utterance-ids. The
|
||||
# speaker information does not have to correspond to actual
|
||||
# speakers, it's just the level you want to adapt at.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
fake=false
|
||||
if [ $1 == "--fake" ]; then
|
||||
fake=true
|
||||
shift
|
||||
fi
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "usage: compute_cmvn_stats.sh [--fake] <data-dir> <log-dir> <path-to-cmvn-dir>";
|
||||
echo "(note: --fake gives you fake cmvn stats that do no normalization.)"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
if [ -f path.sh ]; then . ./path.sh; fi
|
||||
|
||||
data=$1
|
||||
logdir=$2
|
||||
cmvndir=$3
|
||||
|
||||
# make $cmvndir an absolute pathname.
|
||||
cmvndir=`perl -e '($dir,$pwd)= @ARGV; if($dir!~m:^/:) { $dir = "$pwd/$dir"; } print $dir; ' $cmvndir ${PWD}`
|
||||
|
||||
# use "name" as part of name of the archive.
|
||||
name=`basename $data`
|
||||
|
||||
mkdir -p $cmvndir || exit 1;
|
||||
mkdir -p $logdir || exit 1;
|
||||
|
||||
|
||||
required="$data/feats.scp $data/spk2utt"
|
||||
|
||||
for f in $required; do
|
||||
if [ ! -f $f ]; then
|
||||
echo "make_cmvn.sh: no such file $f"
|
||||
exit 1;
|
||||
fi
|
||||
done
|
||||
|
||||
if $fake; then
|
||||
dim=`feat-to-dim scp:$data/feats.scp -`
|
||||
! cat $data/spk2utt | awk -v dim=$dim '{print $1, "["; for (n=0; n < dim; n++) { printf("0 "); } print "1";
|
||||
for (n=0; n < dim; n++) { printf("1 "); } print "0 ]";}' | \
|
||||
copy-matrix ark:- ark,scp:$cmvndir/cmvn_$name.ark,$cmvndir/cmvn_$name.scp && \
|
||||
echo "Error creating fake CMVN stats" && exit 1;
|
||||
else
|
||||
! compute-cmvn-stats --spk2utt=ark:$data/spk2utt scp:$data/feats.scp ark,scp:$cmvndir/cmvn_$name.ark,$cmvndir/cmvn_$name.scp \
|
||||
2> $logdir/cmvn_$name.log && echo "Error computing CMVN stats" && exit 1;
|
||||
fi
|
||||
|
||||
cp $cmvndir/cmvn_$name.scp $data/cmvn.scp || exit 1;
|
||||
|
||||
nc=`cat $data/cmvn.scp | wc -l`
|
||||
nu=`cat $data/spk2utt | wc -l`
|
||||
if [ $nc -ne $nu ]; then
|
||||
echo "Error: it seems not all of the speakers got cmvn stats ($nc != $nu);"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
echo "Succeeded creating CMVN stats for $name"
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
# Begin configuration section.
|
||||
transform_dir=
|
||||
iter=
|
||||
model= # You can specify the model to use (e.g. if you want to use the .alimdl)
|
||||
stage=0
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
max_active=7000
|
||||
max_arcs=-1
|
||||
beam=13.0
|
||||
latbeam=6.0
|
||||
acwt=0.083333 # note: only really affects pruning (scoring is on lattices).
|
||||
num_threads=1 # if >1, will use gmm-latgen-faster-parallel
|
||||
parallel_opts= # If you supply num-threads, you should supply this too.
|
||||
scoring_opts=
|
||||
# note: there are no more min-lmwt and max-lmwt options, instead use
|
||||
# e.g. --scoring-opts "--min-lmwt 1 --max-lmwt 20"
|
||||
skip_scoring=false
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Usage: steps/decode.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo "... where <decode-dir> is assumed to be a sub-directory of the directory"
|
||||
echo " where the model is."
|
||||
echo "e.g.: steps/decode.sh exp/mono/graph_tgpr data/test_dev93 exp/mono/decode_dev93_tgpr"
|
||||
echo ""
|
||||
echo "This script works on CMN + (delta+delta-delta | LDA+MLLT) features; it works out"
|
||||
echo "what type of features you used (assuming it's one of these two)"
|
||||
echo ""
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --iter <iter> # Iteration of model to test."
|
||||
echo " --model <model> # which model to use (e.g. to"
|
||||
echo " # specify the final.alimdl)"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --transform-dir <trans-dir> # dir to find fMLLR transforms "
|
||||
echo " --acwt <float> # acoustic scale used for lattice generation "
|
||||
echo " --scoring-opts <string> # options to local/score.sh"
|
||||
echo " --num-threads <n> # number of threads to use, default 1."
|
||||
echo " --parallel-opts <opts> # e.g. '-pe smp 4' if you supply --num-threads 4"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=$3
|
||||
srcdir=`dirname $dir`; # The model directory is one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
if [ -z "$model" ]; then # if --model <mdl> was not specified on the command line...
|
||||
if [ -z $iter ]; then model=$srcdir/final.mdl;
|
||||
else model=$srcdir/$iter.mdl; fi
|
||||
fi
|
||||
|
||||
for f in $sdata/1/feats.scp $sdata/1/cmvn.scp $model $graphdir/HCLG.fst; do
|
||||
[ ! -f $f ] && echo "decode.sh: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "decode.sh: feature type is $feat_type";
|
||||
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
thread_string=
|
||||
[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then # add transforms to features...
|
||||
echo "Using fMLLR transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
|
||||
[ "`cat $transform_dir/num_jobs`" -ne $nj ] && \
|
||||
echo "Mismatch in number of jobs with $transform_dir";
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
if [ $stage -le 0 ]; then
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode.JOB.log \
|
||||
gmm-latgen-faster$thread_string --max-arcs=$max_arcs --max-active=$max_active --beam=$beam --lattice-beam=$latbeam \
|
||||
--acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$model $graphdir/HCLG.fst "$feats" "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
if ! $skip_scoring ; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh --cmd "$cmd" $scoring_opts $data $graphdir $dir
|
||||
fi
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,206 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Carnegie Mellon University (Author: Yajie Miao)
|
||||
# Johns Hopkins University (Author: Daniel Povey)
|
||||
|
||||
# Decoding script that does basis fMLLR. This can be on top of delta+delta-delta,
|
||||
# or LDA+MLLT features.
|
||||
|
||||
# There are 3 models involved potentially in this script,
|
||||
# and for a standard, speaker-independent system they will all be the same.
|
||||
# The "alignment model" is for the 1st-pass decoding and to get the
|
||||
# Gaussian-level alignments for the "adaptation model" the first time we
|
||||
# do fMLLR. The "adaptation model" is used to estimate fMLLR transforms
|
||||
# and to generate state-level lattices. The lattices are then rescored
|
||||
# with the "final model".
|
||||
#
|
||||
# The following table explains where we get these 3 models from.
|
||||
# Note: $srcdir is one level up from the decoding directory.
|
||||
#
|
||||
# Model Default source:
|
||||
#
|
||||
# "alignment model" $srcdir/final.alimdl --alignment-model <model>
|
||||
# (or $srcdir/final.mdl if alimdl absent)
|
||||
# "adaptation model" $srcdir/final.mdl --adapt-model <model>
|
||||
# "final model" $srcdir/final.mdl --final-model <model>
|
||||
|
||||
|
||||
# Begin configuration section
|
||||
first_beam=10.0 # Beam used in initial, speaker-indep. pass
|
||||
first_max_active=2000 # max-active used in initial pass.
|
||||
alignment_model=
|
||||
adapt_model=
|
||||
final_model=
|
||||
stage=0
|
||||
acwt=0.083333 # Acoustic weight used in getting fMLLR transforms, and also in
|
||||
# lattice generation.
|
||||
|
||||
# Parameters in alignment of training data
|
||||
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
|
||||
align_beam=10
|
||||
retry_beam=40
|
||||
|
||||
max_active=7000
|
||||
beam=13.0
|
||||
lattice_beam=6.0
|
||||
nj=4
|
||||
silence_weight=0.01
|
||||
cmd=run.pl
|
||||
si_dir=
|
||||
# End configuration section
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Usage: steps/decode_basis_fmllr.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo " e.g.: steps/decode_basis_fmllr.sh exp/tri2b/graph_tgpr data/train_si84 data/test_dev93 exp/tri2b/decode_dev93_tgpr"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --adapt-model <adapt-mdl> # Model to compute transforms with"
|
||||
echo " --alignment-model <ali-mdl> # Model to get Gaussian-level alignments for"
|
||||
echo " # 1st pass of transform computation."
|
||||
echo " --final-model <finald-mdl> # Model to finally decode with"
|
||||
echo " --si-dir <speaker-indep-decoding-dir> # use this to skip 1st pass of decoding"
|
||||
echo " # Caution-- must be with same tree"
|
||||
echo " --acwt <acoustic-weight> # default 0.08333 ... used to get posteriors"
|
||||
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=`echo $3 | sed 's:/$::g'` # remove any trailing slash.
|
||||
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
|
||||
silphonelist=`cat $graphdir/phones/silence.csl` || exit 1;
|
||||
|
||||
# Some checks. Note: we don't need $srcdir/tree but we expect
|
||||
# it should exist, given the current structure of the scripts.
|
||||
for f in $graphdir/HCLG.fst $data/feats.scp $srcdir/tree $srcdir/fmllr.basis; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
## Work out name of alignment model. ##
|
||||
if [ -z "$alignment_model" ]; then
|
||||
if [ -f "$srcdir/final.alimdl" ]; then alignment_model=$srcdir/final.alimdl;
|
||||
else alignment_model=$srcdir/final.mdl; fi
|
||||
fi
|
||||
[ ! -f "$alignment_model" ] && echo "$0: no alignment model $alignment_model " && exit 1;
|
||||
##
|
||||
|
||||
## Do the speaker-independent decoding, if --si-dir option not present. ##
|
||||
if [ -z "$si_dir" ]; then # we need to do the speaker-independent decoding pass.
|
||||
si_dir=${dir}.si # Name it as our decoding dir, but with suffix ".si".
|
||||
if [ $stage -le 0 ]; then
|
||||
steps/decode.sh --acwt $acwt --nj $nj --cmd "$cmd" --beam $first_beam --model $alignment_model --max-active $first_max_active $graphdir $data $si_dir || exit 1;
|
||||
fi
|
||||
fi
|
||||
##
|
||||
|
||||
## Some checks, and setting of defaults for variables.
|
||||
[ "$nj" -ne "`cat $si_dir/num_jobs`" ] && echo "Mismatch in #jobs with si-dir" && exit 1;
|
||||
[ ! -f "$si_dir/lat.1.gz" ] && echo "No such file $si_dir/lat.1.gz" && exit 1;
|
||||
[ -z "$adapt_model" ] && adapt_model=$srcdir/final.mdl
|
||||
[ -z "$final_model" ] && final_model=$srcdir/final.mdl
|
||||
for f in $adapt_model $final_model; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
##
|
||||
|
||||
## Set up the unadapted features "$sifeats" for testing set
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type";
|
||||
case $feat_type in
|
||||
delta) sifeats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) sifeats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
##
|
||||
|
||||
## Now get the first-pass fMLLR transforms.
|
||||
## We give all the default parameters in gmm-est-basis-fmllr
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: getting first-pass fMLLR transforms."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr_pass1.JOB.log \
|
||||
gunzip -c $si_dir/lat.JOB.gz \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post $silence_weight $silphonelist $alignment_model ark:- ark:- \| \
|
||||
gmm-post-to-gpost $alignment_model "$sifeats" ark:- ark:- \| \
|
||||
gmm-est-basis-fmllr-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
--fmllr-min-count=200 --num-iters=10 --size-scale=0.2 \
|
||||
--step-size-iters=3 --write-weights=ark:$dir/pre_wgt.JOB \
|
||||
$adapt_model $srcdir/fmllr.basis "$sifeats" ark,s,cs:- \
|
||||
ark:$dir/pre_trans.JOB || exit 1;
|
||||
fi
|
||||
##
|
||||
|
||||
pass1feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/pre_trans.JOB ark:- ark:- |"
|
||||
|
||||
## Do the main lattice generation pass. Note: we don't determinize the lattices at
|
||||
## this stage, as we're going to use them in acoustic rescoring with the larger
|
||||
## model, and it's more correct to store the full state-level lattice for this purpose.
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: doing main lattice generation phase"
|
||||
$cmd JOB=1:$nj $dir/log/decode.JOB.log \
|
||||
gmm-latgen-faster --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
|
||||
--acoustic-scale=$acwt \
|
||||
--determinize-lattice=false --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$adapt_model $graphdir/HCLG.fst "$pass1feats" "ark:|gzip -c > $dir/lat.tmp.JOB.gz" \
|
||||
|| exit 1;
|
||||
fi
|
||||
##
|
||||
|
||||
## Do a second pass of estimating the transform-- this time with the lattices
|
||||
## generated from the alignment model. Compose the transforms to get
|
||||
## $dir/trans.1, etc.
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: estimating fMLLR transforms a second time."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr_pass2.JOB.log \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=4.0 \
|
||||
"ark:gunzip -c $dir/lat.tmp.JOB.gz|" ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post $silence_weight $silphonelist $adapt_model ark:- ark:- \| \
|
||||
gmm-est-basis-fmllr --fmllr-min-count=200 \
|
||||
--spk2utt=ark:$sdata/JOB/spk2utt --write-weights=ark:$dir/trans_tmp_wgt.JOB \
|
||||
$adapt_model $srcdir/fmllr.basis "$pass1feats" ark,s,cs:- ark:$dir/trans_tmp.JOB '&&' \
|
||||
compose-transforms --b-is-affine=true ark:$dir/trans_tmp.JOB ark:$dir/pre_trans.JOB \
|
||||
ark:$dir/trans.JOB || exit 1;
|
||||
fi
|
||||
##
|
||||
|
||||
feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
|
||||
|
||||
# Rescore the state-level lattices with the final adapted features, and the final model
|
||||
# (which by default is $srcdir/final.mdl, but which may be specified on the command line,
|
||||
# useful in case of discriminatively trained systems).
|
||||
# At this point we prune and determinize the lattices and write them out, ready for
|
||||
# language model rescoring.
|
||||
|
||||
if [ $stage -le 4 ]; then
|
||||
echo "$0: doing a final pass of acoustic rescoring."
|
||||
$cmd JOB=1:$nj $dir/log/acoustic_rescore.JOB.log \
|
||||
gmm-rescore-lattice $final_model "ark:gunzip -c $dir/lat.tmp.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" '&&' rm $dir/lat.tmp.JOB.gz || exit 1;
|
||||
fi
|
||||
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "$0: not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh --cmd "$cmd" $data $graphdir $dir
|
||||
|
||||
rm $dir/{trans_tmp,pre_trans}.*
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,86 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
# Begin configuration.
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
maxactive=7000
|
||||
beam=13.0
|
||||
latbeam=6.0
|
||||
acwt=0.083333
|
||||
# End configuration.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 5 ]; then
|
||||
echo "Usage: steps/decode_si_biglm.sh [options] <graph-dir> <old-LM-fst> <new-LM-fst> <data-dir> <decode-dir>"
|
||||
echo "... where <decode-dir> is assumed to be a sub-directory of the directory"
|
||||
echo " where the model is."
|
||||
echo "e.g.: steps/decode_si.sh exp/mono/graph_tgpr data/test_dev93 exp/mono/decode_dev93_tgpr"
|
||||
echo ""
|
||||
echo "This script works on CMN + (delta+delta-delta | LDA+MLLT) features; it works out"
|
||||
echo "what type of features you used (assuming it's one of these two)"
|
||||
echo ""
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
oldlm_fst=$2
|
||||
newlm_fst=$3
|
||||
data=$4
|
||||
dir=$5
|
||||
|
||||
srcdir=`dirname $dir`; # The model directory is one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
|
||||
for f in $sdata/1/feats.scp $sdata/1/cmvn.scp $srcdir/final.mdl $graphdir/HCLG.fst $oldlm_fst $newlm_fst; do
|
||||
[ ! -f $f ] && echo "decode_si.sh: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "decode_si.sh: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
[ -f `dirname $oldlm_fst`/words.txt ] && ! cmp `dirname $oldlm_fst`/words.txt $graphdir/words.txt && \
|
||||
echo "Warning: old LM words.txt does not match with that in $graphdir .. probably will not work.";
|
||||
[ -f `dirname $newlm_fst`/words.txt ] && ! cmp `dirname $oldlm_fst`/words.txt $graphdir/words.txt && \
|
||||
echo "Warning: new LM words.txt does not match with that in $graphdir .. probably will not work.";
|
||||
|
||||
# fstproject replaces the disambiguation symbol #0, which only appears on the
|
||||
# input side, with the <eps> that appears in the corresponding arcs on the output side.
|
||||
oldlm_cmd="fstproject --project_output=true $oldlm_fst | fstarcsort --sort_type=ilabel |"
|
||||
newlm_cmd="fstproject --project_output=true $newlm_fst | fstarcsort --sort_type=ilabel |"
|
||||
|
||||
$cmd JOB=1:$nj $dir/log/decode.JOB.log \
|
||||
gmm-latgen-biglm-faster --max-active=$maxactive --beam=$beam --lattice-beam=$latbeam \
|
||||
--acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$srcdir/final.mdl $graphdir/HCLG.fst "$oldlm_cmd" "$newlm_cmd" "$feats" \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh --cmd "$cmd" $data $graphdir $dir
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,59 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# Combine two decoding directories by composing the lattices (we
|
||||
# apply a weight to each of the original weights, by default 0.5 each).
|
||||
|
||||
# Begin configuration section.
|
||||
weight1=0.5 # Weight on 1st set of lattices.
|
||||
cmd=run.pl
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 5 ]; then
|
||||
echo "Usage: steps/decode_combine.sh [options] <data> <lang-dir|graph-dir> <decode-dir1> <decode-dir2> <decode-dir-out>"
|
||||
echo " e.g.: steps/decode_combine.sh data/lang data/test exp/dir1/decode exp/dir2/decode exp/combine_1_2/decode"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --weight1 <weight> # Weight on 1st set of lattices (default 0.5)"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang_or_graphdir=$2
|
||||
srcdir1=$3
|
||||
srcdir2=$4
|
||||
dir=$5
|
||||
|
||||
for f in $data/utt2spk $lang_or_graphdir/phones.txt $srcdir1/lat.1.gz $srcdir2/lat.1.gz; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
nj1=`cat $srcdir1/num_jobs` || exit 1;
|
||||
nj2=`cat $srcdir2/num_jobs` || exit 1;
|
||||
[ $nj1 -ne $nj2 ] && echo "$0: mismatch in number of jobs $nj1 versus $nj2" && exit 1;
|
||||
nj=$nj1
|
||||
|
||||
mkdir -p $dir/log
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
# The lattice-interp command does the score interpolation (with composition),
|
||||
# and the lattice-copy-backoff replaces the result with the 1st lattice, in
|
||||
# cases where the composed result was empty.
|
||||
$cmd JOB=1:$nj $dir/log/interp.JOB.log \
|
||||
lattice-interp --alpha=$weight1 "ark:gunzip -c $srcdir1/lat.JOB.gz|" \
|
||||
"ark,s,cs:gunzip -c $srcdir2/lat.JOB.gz|" ark:- \| \
|
||||
lattice-copy-backoff "ark,s,cs:gunzip -c $srcdir1/lat.JOB.gz|" ark,s,cs:- \
|
||||
"ark:|gzip -c >$dir/lat.JOB.gz" || exit 1;
|
||||
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh --cmd "$cmd" $data $lang_or_graphdir $dir
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,217 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
|
||||
# Decoding script that does fMLLR. This can be on top of delta+delta-delta, or
|
||||
# LDA+MLLT features.
|
||||
|
||||
# There are 3 models involved potentially in this script,
|
||||
# and for a standard, speaker-independent system they will all be the same.
|
||||
# The "alignment model" is for the 1st-pass decoding and to get the
|
||||
# Gaussian-level alignments for the "adaptation model" the first time we
|
||||
# do fMLLR. The "adaptation model" is used to estimate fMLLR transforms
|
||||
# and to generate state-level lattices. The lattices are then rescored
|
||||
# with the "final model".
|
||||
#
|
||||
# The following table explains where we get these 3 models from.
|
||||
# Note: $srcdir is one level up from the decoding directory.
|
||||
#
|
||||
# Model Default source:
|
||||
#
|
||||
# "alignment model" $srcdir/final.alimdl --alignment-model <model>
|
||||
# (or $srcdir/final.mdl if alimdl absent)
|
||||
# "adaptation model" $srcdir/final.mdl --adapt-model <model>
|
||||
# "final model" $srcdir/final.mdl --final-model <model>
|
||||
|
||||
|
||||
# Begin configuration section
|
||||
first_beam=10.0 # Beam used in initial, speaker-indep. pass
|
||||
first_max_active=2000 # max-active used in initial pass.
|
||||
first_max_arcs=-1
|
||||
alignment_model=
|
||||
adapt_model=
|
||||
final_model=
|
||||
stage=0
|
||||
acwt=0.083333 # Acoustic weight used in getting fMLLR transforms, and also in
|
||||
# lattice generation.
|
||||
max_active=7000
|
||||
max_arcs=-1
|
||||
beam=13.0
|
||||
lattice_beam=6.0
|
||||
nj=4
|
||||
silence_weight=0.01
|
||||
cmd=run.pl
|
||||
si_dir=
|
||||
fmllr_update_type=full
|
||||
num_threads=1 # if >1, will use gmm-latgen-faster-parallel
|
||||
parallel_opts= # If you supply num-threads, you should supply this too.
|
||||
skip_scoring=false
|
||||
scoring_opts=
|
||||
norm_vars=false
|
||||
# End configuration section
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Wrong #arguments ($#, expected 3)"
|
||||
echo "Usage: steps/decode_fmllr.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo " e.g.: steps/decode_fmllr.sh exp/tri2b/graph_tgpr data/test_dev93 exp/tri2b/decode_dev93_tgpr"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --adapt-model <adapt-mdl> # Model to compute transforms with"
|
||||
echo " --alignment-model <ali-mdl> # Model to get Gaussian-level alignments for"
|
||||
echo " # 1st pass of transform computation."
|
||||
echo " --final-model <finald-mdl> # Model to finally decode with"
|
||||
echo " --si-dir <speaker-indep-decoding-dir> # use this to skip 1st pass of decoding"
|
||||
echo " # Caution-- must be with same tree"
|
||||
echo " --acwt <acoustic-weight> # default 0.08333 ... used to get posteriors"
|
||||
echo " --num-threads <n> # number of threads to use, default 1."
|
||||
echo " --parallel-opts <opts> # e.g. '-pe smp 4' if you supply --num-threads 4"
|
||||
echo " --scoring-opts <opts> # options to local/score.sh"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=`echo $3 | sed 's:/$::g'` # remove any trailing slash.
|
||||
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
|
||||
thread_string=
|
||||
[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
|
||||
|
||||
|
||||
mkdir -p $dir/log
|
||||
split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
|
||||
silphonelist=`cat $graphdir/phones/silence.csl` || exit 1;
|
||||
|
||||
# Some checks. Note: we don't need $srcdir/tree but we expect
|
||||
# it should exist, given the current structure of the scripts.
|
||||
for f in $graphdir/HCLG.fst $data/feats.scp $srcdir/tree; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
## Work out name of alignment model. ##
|
||||
if [ -z "$alignment_model" ]; then
|
||||
if [ -f "$srcdir/final.alimdl" ]; then alignment_model=$srcdir/final.alimdl;
|
||||
else alignment_model=$srcdir/final.mdl; fi
|
||||
fi
|
||||
[ ! -f "$alignment_model" ] && echo "$0: no alignment model $alignment_model " && exit 1;
|
||||
##
|
||||
|
||||
## Do the speaker-independent decoding, if --si-dir option not present. ##
|
||||
if [ -z "$si_dir" ]; then # we need to do the speaker-independent decoding pass.
|
||||
si_dir=${dir}.si # Name it as our decoding dir, but with suffix ".si".
|
||||
if [ $stage -le 0 ]; then
|
||||
steps/decode.sh --parallel-opts "$parallel_opts" --scoring-opts "$scoring_opts" \
|
||||
--num-threads $num_threads --skip-scoring $skip_scoring \
|
||||
--acwt $acwt --nj $nj --cmd "$cmd" --beam $first_beam \
|
||||
--model $alignment_model --max-arcs $max_arcs --max-active \
|
||||
$first_max_active $graphdir $data $si_dir || exit 1;
|
||||
fi
|
||||
fi
|
||||
##
|
||||
|
||||
## Some checks, and setting of defaults for variables.
|
||||
[ "$nj" -ne "`cat $si_dir/num_jobs`" ] && echo "Mismatch in #jobs with si-dir" && exit 1;
|
||||
[ ! -f "$si_dir/lat.1.gz" ] && echo "No such file $si_dir/lat.1.gz" && exit 1;
|
||||
[ -z "$adapt_model" ] && adapt_model=$srcdir/final.mdl
|
||||
[ -z "$final_model" ] && final_model=$srcdir/final.mdl
|
||||
for f in $adapt_model $final_model; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
##
|
||||
|
||||
## Set up the unadapted features "$sifeats"
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type";
|
||||
case $feat_type in
|
||||
delta) sifeats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) sifeats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
##
|
||||
|
||||
## Now get the first-pass fMLLR transforms.
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: getting first-pass fMLLR transforms."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr_pass1.JOB.log \
|
||||
gunzip -c $si_dir/lat.JOB.gz \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post $silence_weight $silphonelist $alignment_model ark:- ark:- \| \
|
||||
gmm-post-to-gpost $alignment_model "$sifeats" ark:- ark:- \| \
|
||||
gmm-est-fmllr-gpost --fmllr-update-type=$fmllr_update_type \
|
||||
--spk2utt=ark:$sdata/JOB/spk2utt $adapt_model "$sifeats" ark,s,cs:- \
|
||||
ark:$dir/pre_trans.JOB || exit 1;
|
||||
fi
|
||||
##
|
||||
|
||||
pass1feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/pre_trans.JOB ark:- ark:- |"
|
||||
|
||||
## Do the main lattice generation pass. Note: we don't determinize the lattices at
|
||||
## this stage, as we're going to use them in acoustic rescoring with the larger
|
||||
## model, and it's more correct to store the full state-level lattice for this purpose.
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: doing main lattice generation phase"
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode.JOB.log \
|
||||
gmm-latgen-faster$thread_string --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
|
||||
--acoustic-scale=$acwt --max-arcs=$max_arcs \
|
||||
--determinize-lattice=false --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$adapt_model $graphdir/HCLG.fst "$pass1feats" "ark:|gzip -c > $dir/lat.tmp.JOB.gz" \
|
||||
|| exit 1;
|
||||
fi
|
||||
##
|
||||
|
||||
## Do a second pass of estimating the transform-- this time with the lattices
|
||||
## generated from the alignment model. Compose the transforms to get
|
||||
## $dir/trans.1, etc.
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: estimating fMLLR transforms a second time."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr_pass2.JOB.log \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=4.0 \
|
||||
"ark:gunzip -c $dir/lat.tmp.JOB.gz|" ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post $silence_weight $silphonelist $adapt_model ark:- ark:- \| \
|
||||
gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \
|
||||
--spk2utt=ark:$sdata/JOB/spk2utt $adapt_model "$pass1feats" \
|
||||
ark,s,cs:- ark:$dir/trans_tmp.JOB '&&' \
|
||||
compose-transforms --b-is-affine=true ark:$dir/trans_tmp.JOB ark:$dir/pre_trans.JOB \
|
||||
ark:$dir/trans.JOB || exit 1;
|
||||
fi
|
||||
##
|
||||
|
||||
feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
|
||||
|
||||
# Rescore the state-level lattices with the final adapted features, and the final model
|
||||
# (which by default is $srcdir/final.mdl, but which may be specified on the command line,
|
||||
# useful in case of discriminatively trained systems).
|
||||
# At this point we prune and determinize the lattices and write them out, ready for
|
||||
# language model rescoring.
|
||||
|
||||
if [ $stage -le 4 ]; then
|
||||
echo "$0: doing a final pass of acoustic rescoring."
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/acoustic_rescore.JOB.log \
|
||||
gmm-rescore-lattice $final_model "ark:gunzip -c $dir/lat.tmp.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned$thread_string --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" '&&' rm $dir/lat.tmp.JOB.gz || exit 1;
|
||||
fi
|
||||
|
||||
if ! $skip_scoring ; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "$0: not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh $scoring_opts --cmd "$cmd" $data $graphdir $dir
|
||||
fi
|
||||
|
||||
rm $dir/{trans_tmp,pre_trans}.*
|
||||
|
||||
exit 0;
|
||||
|
||||
|
|
@ -0,0 +1,250 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
|
||||
# Decoding script that does fMLLR. This can be on top of delta+delta-delta, or
|
||||
# LDA+MLLT features.
|
||||
# This script does an extra pass of lattice generation over and above what the original
|
||||
# script did-- it's for robustness in the case where your original cepstral mean
|
||||
# normalization was way off.
|
||||
# We also added a new option --distribute=true (by default) to
|
||||
# weight-silence-post. This weights the silence frames in a different way,
|
||||
# weighting all posteriors on the frame rather than just the silence ones, which
|
||||
# removes a particular kind of bias that the old approach suffered from.
|
||||
|
||||
# There are 3 models involved potentially in this script,
|
||||
# and for a standard, speaker-independent system they will all be the same.
|
||||
# The "alignment model" is for the 1st-pass decoding and to get the
|
||||
# Gaussian-level alignments for the "adaptation model" the first time we
|
||||
# do fMLLR. The "adaptation model" is used to estimate fMLLR transforms
|
||||
# and to generate state-level lattices. The lattices are then rescored
|
||||
# with the "final model".
|
||||
#
|
||||
# The following table explains where we get these 3 models from.
|
||||
# Note: $srcdir is one level up from the decoding directory.
|
||||
#
|
||||
# Model Default source:
|
||||
#
|
||||
# "alignment model" $srcdir/final.alimdl --alignment-model <model>
|
||||
# (or $srcdir/final.mdl if alimdl absent)
|
||||
# "adaptation model" $srcdir/final.mdl --adapt-model <model>
|
||||
# "final model" $srcdir/final.mdl --final-model <model>
|
||||
|
||||
|
||||
# Begin configuration section
|
||||
first_beam=10.0 # Beam used in initial, speaker-indep. pass
|
||||
first_max_active=2000 # max-active used in first two passes.
|
||||
first_latbeam=4.0 # lattice pruning beam for si decode and first-pass fMLLR decode.
|
||||
# the different spelling from lattice_beam is unfortunate; these scripts
|
||||
# have a history.
|
||||
alignment_model=
|
||||
adapt_model=
|
||||
final_model=
|
||||
cleanup=true
|
||||
stage=0
|
||||
acwt=0.083333 # Acoustic weight used in getting fMLLR transforms, and also in
|
||||
# lattice generation.
|
||||
max_active=7000
|
||||
beam=13.0
|
||||
lattice_beam=6.0
|
||||
nj=4
|
||||
silence_weight=0.01
|
||||
distribute=true # option to weight-silence-post.
|
||||
cmd=run.pl
|
||||
si_dir=
|
||||
fmllr_update_type=full
|
||||
skip_scoring=false
|
||||
num_threads=1 # if >1, will use gmm-latgen-faster-parallel
|
||||
parallel_opts= # If you supply num-threads, you should supply this too.
|
||||
scoring_opts=
|
||||
|
||||
# End configuration section
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Usage: steps/decode_fmllr.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo " e.g.: steps/decode_fmllr.sh exp/tri2b/graph_tgpr data/test_dev93 exp/tri2b/decode_dev93_tgpr"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --adapt-model <adapt-mdl> # Model to compute transforms with"
|
||||
echo " --alignment-model <ali-mdl> # Model to get Gaussian-level alignments for"
|
||||
echo " # 1st pass of transform computation."
|
||||
echo " --final-model <finald-mdl> # Model to finally decode with"
|
||||
echo " --si-dir <speaker-indep-decoding-dir> # use this to skip 1st pass of decoding"
|
||||
echo " # Caution-- must be with same tree"
|
||||
echo " --acwt <acoustic-weight> # default 0.08333 ... used to get posteriors"
|
||||
echo " --num-threads <n> # number of threads to use, default 1."
|
||||
echo " --parallel-opts <opts> # e.g. '-pe smp 4' if you supply --num-threads 4"
|
||||
echo " --scoring-opts <opts> # options to local/score.sh"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=`echo $3 | sed 's:/$::g'` # remove any trailing slash.
|
||||
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
|
||||
thread_string=
|
||||
[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
|
||||
silphonelist=`cat $graphdir/phones/silence.csl` || exit 1;
|
||||
|
||||
# Some checks. Note: we don't need $srcdir/tree but we expect
|
||||
# it should exist, given the current structure of the scripts.
|
||||
for f in $graphdir/HCLG.fst $data/feats.scp $srcdir/tree; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
## Work out name of alignment model. ##
|
||||
if [ -z "$alignment_model" ]; then
|
||||
if [ -f "$srcdir/final.alimdl" ]; then alignment_model=$srcdir/final.alimdl;
|
||||
else alignment_model=$srcdir/final.mdl; fi
|
||||
fi
|
||||
[ ! -f "$alignment_model" ] && echo "$0: no alignment model $alignment_model " && exit 1;
|
||||
##
|
||||
|
||||
## Do the speaker-independent decoding, if --si-dir option not present. ##
|
||||
if [ -z "$si_dir" ]; then # we need to do the speaker-independent decoding pass.
|
||||
si_dir=${dir}.si # Name it as our decoding dir, but with suffix ".si".
|
||||
if [ $stage -le 0 ]; then
|
||||
steps/decode.sh --acwt $acwt --nj $nj --cmd "$cmd" --beam $first_beam --model $alignment_model\
|
||||
--max-active $first_max_active --parallel-opts "${parallel_opts}" --num-threads $num_threads\
|
||||
--skip-scoring true $graphdir $data $si_dir || exit 1;
|
||||
fi
|
||||
fi
|
||||
##
|
||||
|
||||
## Some checks, and setting of defaults for variables.
|
||||
[ "$nj" -ne "`cat $si_dir/num_jobs`" ] && echo "Mismatch in #jobs with si-dir" && exit 1;
|
||||
[ ! -f "$si_dir/lat.1.gz" ] && echo "No such file $si_dir/lat.1.gz" && exit 1;
|
||||
[ -z "$adapt_model" ] && adapt_model=$srcdir/final.mdl
|
||||
[ -z "$final_model" ] && final_model=$srcdir/final.mdl
|
||||
for f in $adapt_model $final_model; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
##
|
||||
|
||||
## Set up the unadapted features "$sifeats"
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type";
|
||||
case $feat_type in
|
||||
delta) sifeats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) sifeats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
##
|
||||
|
||||
## Now get the first-pass fMLLR transforms.
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: getting first-pass fMLLR transforms."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr_pass1.JOB.log \
|
||||
gunzip -c $si_dir/lat.JOB.gz \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post --distribute=$distribute $silence_weight $silphonelist $alignment_model ark:- ark:- \| \
|
||||
gmm-post-to-gpost $alignment_model "$sifeats" ark:- ark:- \| \
|
||||
gmm-est-fmllr-gpost --fmllr-update-type=$fmllr_update_type \
|
||||
--spk2utt=ark:$sdata/JOB/spk2utt $adapt_model "$sifeats" ark,s,cs:- \
|
||||
ark:$dir/trans1.JOB || exit 1;
|
||||
fi
|
||||
##
|
||||
|
||||
pass1feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans1.JOB ark:- ark:- |"
|
||||
|
||||
## Do the first adapted lattice generation pass.
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: doing first adapted lattice generation phase"
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode1.JOB.log\
|
||||
gmm-latgen-faster$thread_string --max-active=$first_max_active --beam=$first_beam --lattice-beam=$first_latbeam \
|
||||
--acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$adapt_model $graphdir/HCLG.fst "$pass1feats" "ark:|gzip -c > $dir/lat1.JOB.gz" \
|
||||
|| exit 1;
|
||||
fi
|
||||
|
||||
|
||||
## Do a second pass of estimating the transform. Compose the transforms to get
|
||||
## $dir/trans2.*.
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: estimating fMLLR transforms a second time."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr_pass2.JOB.log \
|
||||
lattice-to-post --acoustic-scale=$acwt "ark:gunzip -c $dir/lat1.JOB.gz|" ark:- \| \
|
||||
weight-silence-post --distribute=$distribute $silence_weight $silphonelist $adapt_model ark:- ark:- \| \
|
||||
gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \
|
||||
--spk2utt=ark:$sdata/JOB/spk2utt $adapt_model "$pass1feats" \
|
||||
ark,s,cs:- ark:$dir/trans1b.JOB '&&' \
|
||||
compose-transforms --b-is-affine=true ark:$dir/trans1b.JOB ark:$dir/trans1.JOB \
|
||||
ark:$dir/trans2.JOB || exit 1;
|
||||
if $cleanup; then
|
||||
rm $dir/trans1b.* $dir/trans1.* $dir/lat1.*.gz
|
||||
fi
|
||||
fi
|
||||
##
|
||||
|
||||
pass2feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans2.JOB ark:- ark:- |"
|
||||
|
||||
# Generate a 3rd set of lattices, with the "adaptation model"; we'll use these
|
||||
# to adapt a 3rd time, and we'll rescore them. Since we should be close to the final
|
||||
# fMLLR, we don't bother dumping un-determinized lattices to disk.
|
||||
|
||||
## Do the final lattice generation pass (but we'll rescore these lattices
|
||||
## after another stage of adaptation.)
|
||||
if [ $stage -le 4 ]; then
|
||||
echo "$0: doing final lattice generation phase"
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode2.JOB.log\
|
||||
gmm-latgen-faster$thread_string --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
|
||||
--acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$adapt_model $graphdir/HCLG.fst "$pass2feats" "ark:|gzip -c > $dir/lat2.JOB.gz" \
|
||||
|| exit 1;
|
||||
fi
|
||||
|
||||
|
||||
## Do a third pass of estimating the transform. Compose the transforms to get
|
||||
## $dir/trans.*.
|
||||
if [ $stage -le 5 ]; then
|
||||
echo "$0: estimating fMLLR transforms a third time."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr_pass3.JOB.log \
|
||||
lattice-to-post --acoustic-scale=$acwt "ark:gunzip -c $dir/lat2.JOB.gz|" ark:- \| \
|
||||
weight-silence-post --distribute=$distribute $silence_weight $silphonelist $adapt_model ark:- ark:- \| \
|
||||
gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \
|
||||
--spk2utt=ark:$sdata/JOB/spk2utt $adapt_model "$pass2feats" \
|
||||
ark,s,cs:- ark:$dir/trans2b.JOB '&&' \
|
||||
compose-transforms --b-is-affine=true ark:$dir/trans2b.JOB ark:$dir/trans2.JOB \
|
||||
ark:$dir/trans.JOB || exit 1;
|
||||
if $cleanup; then
|
||||
rm $dir/trans2b.* $dir/trans2.*
|
||||
fi
|
||||
fi
|
||||
##
|
||||
|
||||
feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
|
||||
|
||||
if [ $stage -le 6 ]; then
|
||||
echo "$0: doing a final pass of acoustic rescoring."
|
||||
$cmd JOB=1:$nj $dir/log/acoustic_rescore.JOB.log \
|
||||
gmm-rescore-lattice $final_model "ark:gunzip -c $dir/lat2.JOB.gz|" "$feats" \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
if $cleanup; then
|
||||
rm $dir/lat2.*.gz
|
||||
fi
|
||||
fi
|
||||
|
||||
if ! $skip_scoring ; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "$0: not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh $scoring_opts --cmd "$cmd" $data $graphdir $dir
|
||||
fi
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
# Decoding of fMMI or fMPE models (feature-space discriminative training).
|
||||
# If transform-dir supplied, expects e.g. fMLLR transforms in that dir.
|
||||
|
||||
# Begin configuration section.
|
||||
stage=1
|
||||
iter=final
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
maxactive=7000
|
||||
beam=13.0
|
||||
latbeam=6.0
|
||||
acwt=0.083333 # note: only really affects pruning (scoring is on lattices).
|
||||
ngselect=2; # Just use the 2 top Gaussians for fMMI/fMPE. Should match train.
|
||||
transform_dir=
|
||||
num_threads=1 # if >1, will use gmm-latgen-faster-parallel
|
||||
parallel_opts= # If you supply num-threads, you should supply this too.
|
||||
scoring_opts=
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Usage: steps/decode_fmmi.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo "... where <decode-dir> is assumed to be a sub-directory of the directory"
|
||||
echo " where the model is."
|
||||
echo "e.g.: steps/decode_fmmi.sh exp/mono/graph_tgpr data/test_dev93 exp/mono/decode_dev93_tgpr"
|
||||
echo ""
|
||||
echo "This script works on CMN + (delta+delta-delta | LDA+MLLT) features; it works out"
|
||||
echo "what type of features you used (assuming it's one of these two)"
|
||||
echo "You can also use fMLLR features-- you have to supply --transform-dir option."
|
||||
echo ""
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --iter <iter> # Iteration of model to test."
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --acwt <float> # acoustic scale used for lattice generation "
|
||||
echo " --transform-dir <transform-dir> # where to find fMLLR transforms."
|
||||
echo " --scoring-opts <string> # options to local/score.sh"
|
||||
echo " # speaker-adapted decoding"
|
||||
echo " --num-threads <n> # number of threads to use, default 1."
|
||||
echo " --parallel-opts <opts> # e.g. '-pe smp 4' if you supply --num-threads 4"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=$3
|
||||
srcdir=`dirname $dir`; # The model directory is one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
thread_string=
|
||||
[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
model=$srcdir/$iter.mdl
|
||||
|
||||
for f in $sdata/1/feats.scp $sdata/1/cmvn.scp $model $graphdir/HCLG.fst; do
|
||||
[ ! -f $f ] && echo "decode_fmmi.sh: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "decode_fmmi.sh: feature type is $feat_type";
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
if [ ! -z "$transform_dir" ]; then # add transforms to features...
|
||||
echo "Using fMLLR transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
|
||||
[ "`cat $transform_dir/num_jobs`" -ne $nj ] && \
|
||||
echo "Mismatch in number of jobs with $transform_dir";
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
fmpefeats="$feats fmpe-apply-transform $srcdir/$iter.fmpe ark:- 'ark,s,cs:gunzip -c $dir/gselect.JOB.gz|' ark:- |"
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
# Get Gaussian selection info.
|
||||
$cmd JOB=1:$nj $dir/log/gselect.JOB.log \
|
||||
gmm-gselect --n=$ngselect $srcdir/$iter.fmpe "$feats" \
|
||||
"ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode.JOB.log \
|
||||
gmm-latgen-faster$thread_string --max-active=$maxactive --beam=$beam --lattice-beam=$latbeam \
|
||||
--acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$model $graphdir/HCLG.fst "$fmpefeats" "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 3 ]; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh $scoring_opts --cmd "$cmd" $data $graphdir $dir
|
||||
fi
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
# Decode, limited to the word-sequences that were present in a set
|
||||
# of lattices on disk. The other lattices do not have to be built
|
||||
# with the same tree or the same context size-- however, you do
|
||||
# have to be using the same vocabulary (words.txt)-- if not you'd
|
||||
# have to map the vocabulary somehow.
|
||||
|
||||
# Note: if the trees are identical, you can use gmm-rescore-lattice.
|
||||
|
||||
# Mechanism: create an unweighted acceptor (on words) for each utterance,
|
||||
# compose that with G, determinize, and then use compile-train-graphs-fsts
|
||||
# to compile a graph for each utterance, to decode with.
|
||||
|
||||
# Begin configuration.
|
||||
cmd=run.pl
|
||||
maxactive=7000
|
||||
beam=20.0
|
||||
latbeam=7.0
|
||||
acwt=0.083333
|
||||
batch_size=75 # Limits memory blowup in compile-train-graphs-fsts
|
||||
scale_opts="--transition-scale=1.0 --self-loop-scale=0.1"
|
||||
# End configuration.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "Usage: steps/decode_si_fromlats.sh [options] <data-dir> <lang> <old-decode-dir> <decode-dir>"
|
||||
echo "e.g.: steps/decode_si_fromlats.sh data/test_dev93 data/lang_test_tg exp/tri2b/decode_tgpr_dev93 exp/tri2a/decode_tgpr_dev93_fromlats"
|
||||
echo ""
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
olddir=$3
|
||||
dir=$4
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
|
||||
mkdir -p $dir/log
|
||||
|
||||
nj=`cat $olddir/num_jobs` || exit 1;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
sdata=$data/split$nj
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj >$dir/num_jobs
|
||||
|
||||
for f in $sdata/1/feats.scp $sdata/1/cmvn.scp $srcdir/final.mdl $olddir/lat.1.gz \
|
||||
$srcdir/tree $lang/L_disambig.fst $lang/phones.txt; do
|
||||
[ ! -f $f ] && echo "decode_si_fromlats.sh: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "decode_si.sh: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
|
||||
$cmd JOB=1:$nj $dir/log/decode_lats.JOB.log \
|
||||
lattice-to-fst "ark:gunzip -c $olddir/lat.JOB.gz|" ark:- \| \
|
||||
fsttablecompose "fstproject --project_output=true $lang/G.fst | fstarcsort |" ark:- ark:- \| \
|
||||
fstdeterminizestar ark:- ark:- \| \
|
||||
compile-train-graphs-fsts --read-disambig-syms=$lang/phones/disambig.int \
|
||||
--batch-size=$batch_size $scale_opts $srcdir/tree $srcdir/final.mdl $lang/L_disambig.fst ark:- ark:- \| \
|
||||
gmm-latgen-faster --max-active=$maxactive --beam=$beam --lattice-beam=$latbeam --acoustic-scale=$acwt \
|
||||
--allow-partial=true --word-symbol-table=$lang/words.txt \
|
||||
$srcdir/final.mdl ark:- "$feats" "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh --cmd "$cmd" $data $lang $dir
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,122 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey), BUT (Author: Mirko Hannemann)
|
||||
# Apache 2.0
|
||||
|
||||
# Begin configuration section.
|
||||
transform_dir=
|
||||
first_pass=
|
||||
iter=
|
||||
model= # You can specify the model to use (e.g. if you want to use the .alimdl)
|
||||
nj=4
|
||||
reverse=false
|
||||
cmd=run.pl
|
||||
max_active=7000
|
||||
beam=13.0
|
||||
latbeam=6.0
|
||||
acwt=0.083333 # note: only really affects pruning (scoring is on lattices).
|
||||
extra_beam=0.0 # small additional beam over varying beam
|
||||
max_beam=100.0 # maximum of varying beam
|
||||
scoring_opts=
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Usage: steps/decode_fwdbwd.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo "... where <decode-dir> is assumed to be a sub-directory of the directory"
|
||||
echo " where the model is."
|
||||
echo "e.g.: steps/decode.sh exp/mono/graph_tgpr data/test_dev93 exp/mono/decode_dev93_tgpr"
|
||||
echo ""
|
||||
echo "This script works on CMN + (delta+delta-delta | LDA+MLLT) features; it works out"
|
||||
echo "what type of features you used (assuming it's one of these two)"
|
||||
echo ""
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --first_pass <decode-dir> # decoding dir of first pass"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --iter <iter> # Iteration of model to test."
|
||||
echo " --model <model> # which model to use (e.g. to"
|
||||
echo " # specify the final.alimdl)"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --transform_dir <trans-dir> # dir to find fMLLR transforms "
|
||||
echo " # speaker-adapted decoding"
|
||||
echo " --scoring-opts <string> # options to local/score.sh"
|
||||
echo " --reverse [true/false] # time reversal of features"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=$3
|
||||
srcdir=`dirname $dir`; # The model directory is one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
if [ -z "$model" ]; then # if --model <mdl> was not specified on the command line...
|
||||
if [ -z $iter ]; then model=$srcdir/final.mdl;
|
||||
else model=$srcdir/$iter.mdl; fi
|
||||
fi
|
||||
|
||||
for f in $sdata/1/feats.scp $sdata/1/cmvn.scp $model $graphdir/HCLG.fst $graphdir/words.txt; do
|
||||
[ ! -f $f ] && echo "decode_fwdbwd.sh: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "decode_fwdbwd.sh: feature type is $feat_type";
|
||||
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then # add transforms to features...
|
||||
echo "Using fMLLR transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
|
||||
[ "`cat $transform_dir/num_jobs`" -ne $nj ] && \
|
||||
echo "Mismatch in number of jobs with $transform_dir";
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
if $reverse; then
|
||||
feats="$feats reverse-feats ark:- ark:- |"
|
||||
fi
|
||||
|
||||
if [ -f $first_pass/lat.1.gz ]; then
|
||||
echo "converting first pass lattice to graph arc acceptor"
|
||||
$cmd JOB=1:$nj $dir/log/arc_graph.JOB.log \
|
||||
time lattice-arcgraph $model $graphdir/HCLG.fst \
|
||||
"ark:gunzip -c $first_pass/lat.JOB.gz|" ark,t:$dir/lat.JOB.arcs || exit 1;
|
||||
# --write-lattices=ark,t:$dir/lat.det
|
||||
# --acoustic-scale=$acwt --lattice-beam=$latbeam --prune=false \
|
||||
|
||||
echo "decode with tracking first pass lattice"
|
||||
$cmd JOB=1:$nj $dir/log/decode_fwdbwd.JOB.log \
|
||||
gmm-latgen-tracking --max-active=$max_active --beam=$beam --lattice-beam=$latbeam \
|
||||
--acoustic-scale=$acwt --allow-partial=true \
|
||||
--extra-beam=$extra_beam --max-beam=$max_beam \
|
||||
--word-symbol-table=$graphdir/words.txt --verbose=2 \
|
||||
$model $graphdir/HCLG.fst "$feats" ark:$dir/lat.JOB.arcs "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
|
||||
else
|
||||
$cmd JOB=1:$nj $dir/log/decode.JOB.log \
|
||||
gmm-latgen-faster --max-active=$max_active --beam=$beam --lattice-beam=$latbeam \
|
||||
--acoustic-scale=$acwt --allow-partial=true \
|
||||
--word-symbol-table=$graphdir/words.txt \
|
||||
$model $graphdir/HCLG.fst "$feats" "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh $scoring_opts --cmd "$cmd" --reverse $reverse $scoring_opts $data $graphdir $dir
|
||||
|
||||
echo "Decoding done."
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,128 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012-2013 Karel Vesely, Daniel Povey
|
||||
# Apache 2.0
|
||||
|
||||
# Begin configuration section.
|
||||
nnet= # Optionally pre-select network to use for getting state-likelihoods
|
||||
feature_transform= # Optionally pre-select feature transform (in front of nnet)
|
||||
model= # Optionally pre-select transition model
|
||||
class_frame_counts= # Optionally pre-select class-counts used to compute PDF priors
|
||||
|
||||
stage=0 # stage=1 skips lattice generation
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
max_active=7000 # maximum of active tokens
|
||||
max_mem=50000000 # limit the fst-size to 50MB (larger fsts are minimized)
|
||||
beam=13.0 # GMM:13.0
|
||||
latbeam=8.0 # GMM:6.0
|
||||
acwt=0.10 # GMM:0.0833, note: only really affects pruning (scoring is on lattices).
|
||||
scoring_opts="--min-lmwt 4 --max-lmwt 15"
|
||||
skip_scoring=false
|
||||
use_gpu_id=-1 # disable gpu
|
||||
parallel_opts="-pe smp 2" # use 2 CPUs (1 DNN-forward, 1 decoder)
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Usage: $0 [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo "... where <decode-dir> is assumed to be a sub-directory of the directory"
|
||||
echo " where the DNN + transition model is."
|
||||
echo "e.g.: $0 exp/dnn1/graph_tgpr data/test exp/dnn1/decode_tgpr"
|
||||
echo ""
|
||||
echo "This script works on plain or modified features (CMN,delta+delta-delta),"
|
||||
echo "which are then sent through feature-transform. It works out what type"
|
||||
echo "of features you used from content of srcdir."
|
||||
echo ""
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo ""
|
||||
echo " --nnet <nnet> # which nnet to use (opt.)"
|
||||
echo " --feature-transform <nnet> # select transform in front of nnet (opt.)"
|
||||
echo " --class-frame-counts <file> # file with frame counts (used to compute priors) (opt.)"
|
||||
echo " --model <model> # which transition model to use (opt.)"
|
||||
echo ""
|
||||
echo " --acwt <float> # select acoustic scale for decoding"
|
||||
echo " --scoring-opts <opts> # options forwarded to local/score.sh"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=$3
|
||||
srcdir=`dirname $dir`; # The model directory is one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
if [ -z "$nnet" ]; then # if --nnet <nnet> was not specified on the command line...
|
||||
nnet=$srcdir/final.nnet;
|
||||
fi
|
||||
[ -z "$nnet" ] && echo "Error nnet '$nnet' does not exist!" && exit 1;
|
||||
|
||||
if [ -z "$model" ]; then # if --model <mdl> was not specified on the command line...
|
||||
model=$srcdir/final.mdl;
|
||||
fi
|
||||
|
||||
# find the feature_transform to use
|
||||
if [ -z "$feature_transform" ]; then
|
||||
feature_transform=$srcdir/final.feature_transform
|
||||
fi
|
||||
if [ ! -f $feature_transform ]; then
|
||||
echo "Missing feature_transform '$feature_transform'"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# check that files exist
|
||||
for f in $sdata/1/feats.scp $nnet_i $nnet $model $graphdir/HCLG.fst; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
# PREPARE THE LOG-POSTERIOR COMPUTATION PIPELINE
|
||||
if [ -z "$class_frame_counts" ]; then
|
||||
class_frame_counts=$srcdir/ali_train_pdf.counts
|
||||
else
|
||||
echo "Overriding class_frame_counts by $class_frame_counts"
|
||||
fi
|
||||
|
||||
# Create the feature stream:
|
||||
feats="ark,s,cs:copy-feats scp:$sdata/JOB/feats.scp ark:- |"
|
||||
# Optionally add cmvn
|
||||
if [ -f $srcdir/norm_vars ]; then
|
||||
norm_vars=$(cat $srcdir/norm_vars 2>/dev/null)
|
||||
[ ! -f $sdata/1/cmvn.scp ] && echo "$0: cannot find cmvn stats $sdata/1/cmvn.scp" && exit 1
|
||||
feats="$feats apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp ark:- ark:- |"
|
||||
fi
|
||||
# Optionally add deltas
|
||||
if [ -f $srcdir/delta_order ]; then
|
||||
delta_order=$(cat $srcdir/delta_order)
|
||||
feats="$feats add-deltas --delta-order=$delta_order ark:- ark:- |"
|
||||
fi
|
||||
|
||||
|
||||
# Run the decoding in the queue
|
||||
if [ $stage -le 0 ]; then
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode.JOB.log \
|
||||
nnet-forward --feature-transform=$feature_transform --no-softmax=true --class-frame-counts=$class_frame_counts --use-gpu-id=$use_gpu_id $nnet "$feats" ark:- \| \
|
||||
latgen-faster-mapped --max-active=$max_active --max-mem=$max_mem --beam=$beam --lattice-beam=$latbeam \
|
||||
--acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$model $graphdir/HCLG.fst ark:- "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
# Run the scoring
|
||||
if ! $skip_scoring ; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh $scoring_opts --cmd "$cmd" $data $graphdir $dir || exit 1;
|
||||
fi
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,127 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey).
|
||||
# Apache 2.0.
|
||||
|
||||
# This script does decoding with a neural-net. If the neural net was built on
|
||||
# top of fMLLR transforms from a conventional system, you should provide the
|
||||
# --transform-dir option.
|
||||
|
||||
# Begin configuration section.
|
||||
stage=1
|
||||
transform_dir= # dir to find fMLLR transforms.
|
||||
nj=4 # number of decoding jobs. If --transform-dir set, must match that number!
|
||||
acwt=0.1 # Just a default value, used for adaptation and beam-pruning..
|
||||
cmd=run.pl
|
||||
beam=15.0
|
||||
max_active=7000
|
||||
|
||||
#WARNING: This option is renamed lat_beam (it was renamed to follow the naming
|
||||
# in the other scripts
|
||||
lattice_beam=8.0 # Beam we use in lattice generation.
|
||||
iter=final
|
||||
num_threads=1 # if >1, will use gmm-latgen-faster-parallel
|
||||
parallel_opts= # If you supply num-threads, you should supply this too.
|
||||
scoring_opts=
|
||||
skip_scoring=false
|
||||
feat_type=
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 3 ]; then
|
||||
echo "Usage: steps/decode_nnet_cpu.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo " e.g.: steps/decode_nnet_cpu.sh --transform-dir exp/tri3b/decode_dev93_tgpr \\"
|
||||
echo " exp/tri3b/graph_tgpr data/test_dev93 exp/tri4a_nnet/decode_dev93_tgpr"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --transform-dir <decoding-dir> # directory of previous decoding"
|
||||
echo " # where we can find transforms for SAT systems."
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --beam <beam> # Decoding beam; default 15.0"
|
||||
echo " --iter <iter> # Iteration of model to decode; default is final."
|
||||
echo " --scoring-opts <string> # options to local/score.sh"
|
||||
echo " --num-threads <n> # number of threads to use, default 1."
|
||||
echo " --parallel-opts <opts> # e.g. '-pe smp 4' if you supply --num-threads 4"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=$3
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
model=$srcdir/$iter.mdl
|
||||
|
||||
for f in $graphdir/HCLG.fst $data/feats.scp $model; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
sdata=$data/split$nj;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
thread_string=
|
||||
[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
|
||||
## Set up features.
|
||||
if [ -z "$feat_type" ]; then
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
fi
|
||||
|
||||
case $feat_type in
|
||||
raw) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- |";;
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
;;
|
||||
*) echo "$0: invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then
|
||||
echo "$0: using transforms from $transform_dir"
|
||||
if [ "$feat_type" == "raw" ]; then
|
||||
[ ! -f $transform_dir/raw_trans.1 ] && echo "$0: no such file $transform_dir/raw_trans.1" && exit 1;
|
||||
[ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
|
||||
&& echo "$0: #jobs mismatch with transform-dir." && exit 1;
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/raw_trans.JOB ark:- ark:- |"
|
||||
else
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
|
||||
[ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
|
||||
&& echo "$0: #jobs mismatch with transform-dir." && exit 1;
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
elif grep 'transform-feats --utt2spk' $srcdir/log/train.1.log >&/dev/null; then
|
||||
echo "$0: **WARNING**: you seem to be using a neural net system trained with transforms,"
|
||||
echo " but you are not providing the --transform-dir option in test time."
|
||||
fi
|
||||
##
|
||||
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode.JOB.log \
|
||||
nnet-latgen-faster$thread_string --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
|
||||
--acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt "$model" \
|
||||
$graphdir/HCLG.fst "$feats" "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
# The output of this script is the files "lat.*.gz"-- we'll rescore this at
|
||||
# different acoustic scales to get the final output.
|
||||
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
if ! $skip_scoring ; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
echo "score best paths"
|
||||
local/score.sh $scoring_opts --cmd "$cmd" $data $graphdir $dir
|
||||
echo "score confidence and timing with sclite"
|
||||
fi
|
||||
fi
|
||||
echo "Decoding done."
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,235 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012-2013 Johns Hopkins University (Author: Daniel Povey)
|
||||
|
||||
# This decoding script is like decode_fmllr.sh, but it does the fMLLR on
|
||||
# the raw cepstra, using the model in the LDA+MLLT space
|
||||
#
|
||||
# Decoding script that does fMLLR. This can be on top of delta+delta-delta, or
|
||||
# LDA+MLLT features.
|
||||
|
||||
# There are 3 models involved potentially in this script,
|
||||
# and for a standard, speaker-independent system they will all be the same.
|
||||
# The "alignment model" is for the 1st-pass decoding and to get the
|
||||
# Gaussian-level alignments for the "adaptation model" the first time we
|
||||
# do fMLLR. The "adaptation model" is used to estimate fMLLR transforms
|
||||
# and to generate state-level lattices. The lattices are then rescored
|
||||
# with the "final model".
|
||||
#
|
||||
# The following table explains where we get these 3 models from.
|
||||
# Note: $srcdir is one level up from the decoding directory.
|
||||
#
|
||||
# Model Default source:
|
||||
#
|
||||
# "alignment model" $srcdir/final.alimdl --alignment-model <model>
|
||||
# (or $srcdir/final.mdl if alimdl absent)
|
||||
# "adaptation model" $srcdir/final.mdl --adapt-model <model>
|
||||
# "final model" $srcdir/final.mdl --final-model <model>
|
||||
|
||||
|
||||
# Begin configuration section
|
||||
first_beam=10.0 # Beam used in initial, speaker-indep. pass
|
||||
first_max_active=2000 # max-active used in initial pass.
|
||||
first_max_arcs=-1
|
||||
alignment_model=
|
||||
adapt_model=
|
||||
final_model=
|
||||
stage=0
|
||||
acwt=0.083333 # Acoustic weight used in getting fMLLR transforms, and also in
|
||||
# lattice generation.
|
||||
max_active=7000
|
||||
use_normal_fmllr=false
|
||||
max_arcs=-1
|
||||
beam=13.0
|
||||
lattice_beam=6.0
|
||||
nj=4
|
||||
silence_weight=0.01
|
||||
cmd=run.pl
|
||||
si_dir=
|
||||
num_threads=1 # if >1, will use gmm-latgen-faster-parallel
|
||||
parallel_opts= # If you supply num-threads, you should supply this too.
|
||||
skip_scoring=false
|
||||
scoring_opts=
|
||||
norm_vars=false
|
||||
# End configuration section
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Wrong #arguments ($#, expected 3)"
|
||||
echo "Usage: steps/decode_fmllr.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo " e.g.: steps/decode_fmllr.sh exp/tri2b/graph_tgpr data/test_dev93 exp/tri2b/decode_dev93_tgpr"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --adapt-model <adapt-mdl> # Model to compute transforms with"
|
||||
echo " --alignment-model <ali-mdl> # Model to get Gaussian-level alignments for"
|
||||
echo " # 1st pass of transform computation."
|
||||
echo " --final-model <finald-mdl> # Model to finally decode with"
|
||||
echo " --si-dir <speaker-indep-decoding-dir> # use this to skip 1st pass of decoding"
|
||||
echo " # Caution-- must be with same tree"
|
||||
echo " --acwt <acoustic-weight> # default 0.08333 ... used to get posteriors"
|
||||
echo " --num-threads <n> # number of threads to use, default 1."
|
||||
echo " --parallel-opts <opts> # e.g. '-pe smp 4' if you supply --num-threads 4"
|
||||
echo " --scoring-opts <opts> # options to local/score.sh"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=`echo $3 | sed 's:/$::g'` # remove any trailing slash.
|
||||
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
|
||||
thread_string=
|
||||
[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
|
||||
|
||||
|
||||
mkdir -p $dir/log
|
||||
split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
|
||||
silphonelist=`cat $graphdir/phones/silence.csl` || exit 1;
|
||||
|
||||
# Some checks. Note: we don't need $srcdir/tree but we expect
|
||||
# it should exist, given the current structure of the scripts.
|
||||
for f in $graphdir/HCLG.fst $data/feats.scp $srcdir/tree; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
## Work out name of alignment model. ##
|
||||
if [ -z "$alignment_model" ]; then
|
||||
if [ -f "$srcdir/final.alimdl" ]; then alignment_model=$srcdir/final.alimdl;
|
||||
else alignment_model=$srcdir/final.mdl; fi
|
||||
fi
|
||||
[ ! -f "$alignment_model" ] && echo "$0: no alignment model $alignment_model " && exit 1;
|
||||
##
|
||||
|
||||
## Do the speaker-independent decoding, if --si-dir option not present. ##
|
||||
if [ -z "$si_dir" ]; then # we need to do the speaker-independent decoding pass.
|
||||
si_dir=${dir}.si # Name it as our decoding dir, but with suffix ".si".
|
||||
if [ $stage -le 0 ]; then
|
||||
steps/decode.sh --parallel-opts "$parallel_opts" --scoring-opts "$scoring_opts" \
|
||||
--num-threads $num_threads --skip-scoring $skip_scoring \
|
||||
--acwt $acwt --nj $nj --cmd "$cmd" --beam $first_beam \
|
||||
--model $alignment_model --max-arcs $max_arcs --max-active \
|
||||
$first_max_active $graphdir $data $si_dir || exit 1;
|
||||
fi
|
||||
fi
|
||||
##
|
||||
|
||||
## Some checks, and setting of defaults for variables.
|
||||
[ "$nj" -ne "`cat $si_dir/num_jobs`" ] && echo "Mismatch in #jobs with si-dir" && exit 1;
|
||||
[ ! -f "$si_dir/lat.1.gz" ] && echo "No such file $si_dir/lat.1.gz" && exit 1;
|
||||
[ -z "$adapt_model" ] && adapt_model=$srcdir/final.mdl
|
||||
[ -z "$final_model" ] && final_model=$srcdir/final.mdl
|
||||
for f in $adapt_model $final_model; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
##
|
||||
|
||||
if [[ ! -f $srcdir/final.mat || ! -f $srcdir/full.mat ]]; then
|
||||
echo "$0: we require final.mat and full.mat in the source directory $srcdir"
|
||||
fi
|
||||
|
||||
splicedfeats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- |"
|
||||
sifeats="$splicedfeats transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
|
||||
full_lda_mat="get-full-lda-mat --print-args=false $srcdir/final.mat $srcdir/full.mat -|"
|
||||
|
||||
##
|
||||
|
||||
## Now get the first-pass fMLLR transforms.
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: getting first-pass raw-fMLLR transforms."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr_pass1.JOB.log \
|
||||
gunzip -c $si_dir/lat.JOB.gz \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post $silence_weight $silphonelist $alignment_model ark:- ark:- \| \
|
||||
gmm-post-to-gpost $alignment_model "$sifeats" ark:- ark:- \| \
|
||||
gmm-est-fmllr-raw-gpost --spk2utt=ark:$sdata/JOB/spk2utt $adapt_model "$full_lda_mat" \
|
||||
"$splicedfeats" ark,s,cs:- ark:$dir/pre_trans.JOB || exit 1;
|
||||
fi
|
||||
##
|
||||
|
||||
pass1splicedfeats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/pre_trans.JOB ark:- ark:- | splice-feats $splice_opts ark:- ark:- |"
|
||||
pass1feats="$pass1splicedfeats transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
|
||||
## Do the main lattice generation pass. Note: we don't determinize the lattices at
|
||||
## this stage, as we're going to use them in acoustic rescoring with the larger
|
||||
## model, and it's more correct to store the full state-level lattice for this purpose.
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: doing main lattice generation phase"
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode.JOB.log \
|
||||
gmm-latgen-faster$thread_string --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
|
||||
--acoustic-scale=$acwt --max-arcs=$max_arcs \
|
||||
--determinize-lattice=false --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$adapt_model $graphdir/HCLG.fst "$pass1feats" "ark:|gzip -c > $dir/lat.tmp.JOB.gz" \
|
||||
|| exit 1;
|
||||
fi
|
||||
##
|
||||
|
||||
## Do a second pass of estimating the transform-- this time with the lattices
|
||||
## generated from the alignment model. Compose the transforms to get
|
||||
## $dir/trans.1, etc.
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: estimating raw-fMLLR transforms a second time."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr_pass2.JOB.log \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=4.0 \
|
||||
"ark:gunzip -c $dir/lat.tmp.JOB.gz|" ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post $silence_weight $silphonelist $adapt_model ark:- ark:- \| \
|
||||
gmm-est-fmllr-raw --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
$adapt_model "$full_lda_mat" "$pass1splicedfeats" ark,s,cs:- ark:$dir/trans_tmp.JOB '&&' \
|
||||
compose-transforms --b-is-affine=true ark:$dir/trans_tmp.JOB ark:$dir/pre_trans.JOB \
|
||||
ark:$dir/raw_trans.JOB || exit 1;
|
||||
fi
|
||||
##
|
||||
|
||||
feats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/raw_trans.JOB ark:- ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
|
||||
if [ $stage -le 4 ] && $use_normal_fmllr; then
|
||||
echo "$0: estimating normal fMLLR transforms"
|
||||
$cmd JOB=1:$nj $dir/log/fmllr_pass3.JOB.log \
|
||||
gmm-rescore-lattice $final_model "ark:gunzip -c $dir/lat.tmp.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=4.0 ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post $silence_weight $silphonelist $adapt_model ark:- ark:- \| \
|
||||
gmm-est-fmllr --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
$adapt_model "$feats" ark,s,cs:- ark:$dir/trans.JOB || exit 1;
|
||||
fi
|
||||
|
||||
if $use_normal_fmllr; then
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
# Rescore the state-level lattices with the final adapted features, and the final model
|
||||
# (which by default is $srcdir/final.mdl, but which may be specified on the command line,
|
||||
# useful in case of discriminatively trained systems).
|
||||
# At this point we prune and determinize the lattices and write them out, ready for
|
||||
# language model rescoring.
|
||||
|
||||
if [ $stage -le 5 ]; then
|
||||
echo "$0: doing a final pass of acoustic rescoring."
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/acoustic_rescore.JOB.log \
|
||||
gmm-rescore-lattice $final_model "ark:gunzip -c $dir/lat.tmp.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned$thread_string --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" '&&' rm $dir/lat.tmp.JOB.gz || exit 1;
|
||||
fi
|
||||
|
||||
if ! $skip_scoring ; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "$0: not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh $scoring_opts --cmd "$cmd" $data $graphdir $dir
|
||||
fi
|
||||
|
||||
#rm $dir/{trans_tmp,pre_trans}.*
|
||||
|
||||
exit 0;
|
||||
|
||||
|
|
@ -0,0 +1,257 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# This script does decoding with an SGMM system, with speaker vectors.
|
||||
# If the SGMM system was
|
||||
# built on top of fMLLR transforms from a conventional system, you should
|
||||
# provide the --transform-dir option.
|
||||
|
||||
# Begin configuration section.
|
||||
stage=1
|
||||
alignment_model=
|
||||
transform_dir= # dir to find fMLLR transforms.
|
||||
nj=4 # number of decoding jobs.
|
||||
acwt=0.1 # Just a default value, used for adaptation and beam-pruning..
|
||||
cmd=run.pl
|
||||
beam=15.0
|
||||
gselect=15 # Number of Gaussian-selection indices for SGMMs. [Note:
|
||||
# the first_pass_gselect variable is used for the 1st pass of
|
||||
# decoding and can be tighter.
|
||||
first_pass_gselect=3 # Use a smaller number of Gaussian-selection indices in
|
||||
# the 1st pass of decoding (lattice generation).
|
||||
max_active=7000
|
||||
|
||||
#WARNING: This option is renamed lat_beam (it was renamed to follow the naming
|
||||
# in the other scripts
|
||||
lattice_beam=8.0 # Beam we use in lattice generation.
|
||||
vecs_beam=4.0 # Beam we use to prune lattices while getting posteriors for
|
||||
# speaker-vector computation. Can be quite tight (actually we could
|
||||
# probably just do best-path.
|
||||
use_fmllr=false
|
||||
fmllr_iters=10
|
||||
fmllr_min_count=1000
|
||||
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 3 ]; then
|
||||
echo "Usage: steps/decode_sgmm.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo " e.g.: steps/decode_sgmm.sh --transform-dir exp/tri3b/decode_dev93_tgpr \\"
|
||||
echo " exp/sgmm3a/graph_tgpr data/test_dev93 exp/sgmm3a/decode_dev93_tgpr"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --transform-dir <decoding-dir> # directory of previous decoding"
|
||||
echo " # where we can find transforms for SAT systems."
|
||||
echo " --alignment-model <ali-mdl> # Model for the first-pass decoding."
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --beam <beam> # Decoding beam; default 13.0"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=$3
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
|
||||
for f in $graphdir/HCLG.fst $data/feats.scp $srcdir/final.mdl; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
sdata=$data/split$nj;
|
||||
silphonelist=`cat $graphdir/phones/silence.csl` || exit 1
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|"
|
||||
gselect_opt_1stpass="$gselect_opt copy-gselect --n=$first_pass_gselect ark:- ark:- |"
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
|
||||
## Set up features.
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
;;
|
||||
*) echo "$0: invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then
|
||||
echo "$0: using transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
|
||||
[ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
|
||||
&& echo "$0: #jobs mismatch with transform-dir." && exit 1;
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
|
||||
echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
|
||||
echo " but you are not providing the --transform-dir option in test time."
|
||||
fi
|
||||
##
|
||||
|
||||
## Calculate FMLLR pre-transforms if needed. We are doing this here since this
|
||||
## step is requried by models both with and without speaker vectors
|
||||
if $use_fmllr; then
|
||||
if [ ! -f $srcdir/final.fmllr_mdl ] || [ $srcdir/final.fmllr_mdl -ot $srcdir/final.mdl ]; then
|
||||
echo "$0: computing pre-transform for fMLLR computation."
|
||||
sgmm-comp-prexform $srcdir/final.mdl $srcdir/final.occs $srcdir/final.fmllr_mdl || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
## Save Gaussian-selection info to disk.
|
||||
# Note: we can use final.mdl regardless of whether there is an alignment model--
|
||||
# they use the same UBM.
|
||||
if [ $stage -le 1 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/gselect.JOB.log \
|
||||
sgmm-gselect --full-gmm-nbest=$gselect $srcdir/final.mdl \
|
||||
"$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
## Work out name of alignment model. ##
|
||||
if [ -z "$alignment_model" ]; then
|
||||
if [ -f "$srcdir/final.alimdl" ]; then alignment_model=$srcdir/final.alimdl;
|
||||
else alignment_model=$srcdir/final.mdl; fi
|
||||
fi
|
||||
[ ! -f "$alignment_model" ] && echo "$0: no alignment model $alignment_model " && exit 1;
|
||||
|
||||
# Generate state-level lattice which we can rescore. This is done with the
|
||||
# alignment model and no speaker-vectors.
|
||||
if [ $stage -le 2 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/decode_pass1.JOB.log \
|
||||
sgmm-latgen-faster --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
|
||||
--acoustic-scale=$acwt --determinize-lattice=false --allow-partial=true \
|
||||
--word-symbol-table=$graphdir/words.txt "$gselect_opt_1stpass" $alignment_model \
|
||||
$graphdir/HCLG.fst "$feats" "ark:|gzip -c > $dir/pre_lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
## Check if the model has speaker vectors
|
||||
spkdim=`sgmm-info $srcdir/final.mdl | grep 'speaker vector' | awk '{print $NF}'`
|
||||
|
||||
if [ $spkdim -gt 0 ]; then ### For models with speaker vectors:
|
||||
|
||||
# Estimate speaker vectors (1st pass). Prune before determinizing
|
||||
# because determinization can take a while on un-pruned lattices.
|
||||
# Note: the sgmm-post-to-gpost stage is necessary because we have
|
||||
# a separate alignment-model and final model, otherwise we'd skip it
|
||||
# and use sgmm-est-spkvecs.
|
||||
if [ $stage -le 3 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/vecs_pass1.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alignment_model ark:- ark:- \| \
|
||||
sgmm-post-to-gpost "$gselect_opt" $alignment_model "$feats" ark:- ark:- \| \
|
||||
sgmm-est-spkvecs-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
$srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/pre_vecs.JOB" || exit 1;
|
||||
fi
|
||||
|
||||
# Estimate speaker vectors (2nd pass). Since we already have spk vectors,
|
||||
# at this point we need to rescore the lattice to get the correct posteriors.
|
||||
if [ $stage -le 4 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/vecs_pass2.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm-rescore-lattice --spk-vecs=ark:$dir/pre_vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm-est-spkvecs --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/pre_vecs.JOB \
|
||||
$srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/vecs.JOB" || exit 1;
|
||||
fi
|
||||
rm $dir/pre_vecs.*
|
||||
|
||||
if $use_fmllr; then
|
||||
# Estimate fMLLR transforms (note: these may be on top of any
|
||||
# fMLLR transforms estimated with the baseline GMM system.
|
||||
if [ $stage -le 5 ]; then # compute fMLLR transforms.
|
||||
echo "$0: computing fMLLR transforms."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm-rescore-lattice --spk-vecs=ark:$dir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm-est-fmllr --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/vecs.JOB \
|
||||
--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
|
||||
$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
|
||||
fi
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
# Now rescore the state-level lattices with the adapted features and the
|
||||
# corresponding model. Prune and determinize the lattices to limit
|
||||
# their size.
|
||||
if [ $stage -le 6 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/rescore.JOB.log \
|
||||
sgmm-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
|
||||
$srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
rm $dir/pre_lat.*.gz
|
||||
|
||||
else ### For models without speaker vectors:
|
||||
|
||||
if $use_fmllr; then
|
||||
# Estimate fMLLR transforms (note: these may be on top of any
|
||||
# fMLLR transforms estimated with the baseline GMM system.
|
||||
if [ $stage -le 5 ]; then # compute fMLLR transforms.
|
||||
echo "$0: computing fMLLR transforms."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm-rescore-lattice --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm-est-fmllr --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" \
|
||||
--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
|
||||
$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
|
||||
fi
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
# Now rescore the state-level lattices with the adapted features and the
|
||||
# corresponding model. Prune and determinize the lattices to limit
|
||||
# their size.
|
||||
if [ $stage -le 6 ] && $use_fmllr; then
|
||||
$cmd JOB=1:$nj $dir/log/rescore.JOB.log \
|
||||
sgmm-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
$srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
rm $dir/pre_lat.*.gz
|
||||
else # Already done with decoding if no adaptation needed.
|
||||
for n in `seq 1 $nj`; do
|
||||
mv $dir/pre_lat.${n}.gz $dir/lat.${n}.gz
|
||||
done
|
||||
fi
|
||||
|
||||
fi
|
||||
|
||||
# The output of this script is the files "lat.*.gz"-- we'll rescore this at
|
||||
# different acoustic scales to get the final output.
|
||||
|
||||
|
||||
if [ $stage -le 7 ]; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
echo "score best paths"
|
||||
local/score.sh --cmd "$cmd" $data $graphdir $dir
|
||||
echo "score confidence and timing with sclite"
|
||||
#local/score_sclite_conf.sh --cmd "$cmd" --language turkish $data $graphdir $dir
|
||||
fi
|
||||
echo "Decoding done."
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,211 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# This script does decoding with an SGMM system, with speaker vectors.
|
||||
# If the SGMM system was
|
||||
# built on top of fMLLR transforms from a conventional system, you should
|
||||
# provide the --transform-dir option.
|
||||
|
||||
# Begin configuration section.
|
||||
stage=1
|
||||
transform_dir= # dir to find fMLLR transforms.
|
||||
nj=4 # number of decoding jobs.
|
||||
acwt=0.1 # Just a default value, used for adaptation and beam-pruning..
|
||||
cmd=run.pl
|
||||
beam=13.0
|
||||
gselect=15 # Number of Gaussian-selection indices for SGMMs. [Note:
|
||||
# the first_pass_gselect variable is used for the 1st pass of
|
||||
# decoding and can be tighter.
|
||||
first_pass_gselect=3 # Use a smaller number of Gaussian-selection indices in
|
||||
# the 1st pass of decoding (lattice generation).
|
||||
max_active=7000
|
||||
max_arcs=-1
|
||||
|
||||
#WARNING: This option is renamed lat_beam (it was renamed to follow the naming
|
||||
# in the other scripts
|
||||
lattice_beam=6.0 # Beam we use in lattice generation.
|
||||
vecs_beam=4.0 # Beam we use to prune lattices while getting posteriors for
|
||||
# speaker-vector computation. Can be quite tight (actually we could
|
||||
# probably just do best-path.
|
||||
use_fmllr=false
|
||||
fmllr_iters=10
|
||||
fmllr_min_count=1000
|
||||
num_threads=1 # if >1, will use gmm-latgen-faster-parallel
|
||||
parallel_opts= # If you supply num-threads, you should supply this too.
|
||||
skip_scoring=false
|
||||
scoring_opts=
|
||||
# note: there are no more min-lmwt and max-lmwt options, instead use
|
||||
# e.g. --scoring-opts "--min-lmwt 1 --max-lmwt 20"
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 3 ]; then
|
||||
echo "Usage: steps/decode_sgmm2.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo " e.g.: steps/decode_sgmm2.sh --transform-dir exp/tri3b/decode_dev93_tgpr \\"
|
||||
echo " exp/sgmm3a/graph_tgpr data/test_dev93 exp/sgmm3a/decode_dev93_tgpr"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --transform-dir <decoding-dir> # directory of previous decoding"
|
||||
echo " # where we can find transforms for SAT systems."
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --beam <beam> # Decoding beam; default 13.0"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=$3
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
|
||||
for f in $graphdir/HCLG.fst $data/feats.scp $srcdir/final.mdl; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
sdata=$data/split$nj;
|
||||
silphonelist=`cat $graphdir/phones/silence.csl` || exit 1
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|"
|
||||
gselect_opt_1stpass="$gselect_opt copy-gselect --n=$first_pass_gselect ark:- ark:- |"
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
thread_string=
|
||||
[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
|
||||
|
||||
## Set up features.
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
;;
|
||||
*) echo "$0: invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then
|
||||
[ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
|
||||
&& echo "$0: #jobs mismatch with transform-dir." && exit 1;
|
||||
if [ -f $transform_dir/trans.1 ]; then
|
||||
echo "$0: using transforms from $transform_dir"
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
elif [ -f $transform_dir/raw_trans.1 ]; then
|
||||
feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/raw_trans.JOB ark:- ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
else
|
||||
echo "$0: no such file $transform_dir/trans.1 or $transform_dir/raw_trans.1, invalid --transform-dir option?"
|
||||
exit 1;
|
||||
fi
|
||||
elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
|
||||
echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
|
||||
echo " but you are not providing the --transform-dir option in test time."
|
||||
fi
|
||||
##
|
||||
|
||||
## Save Gaussian-selection info to disk.
|
||||
# Note: we can use final.mdl regardless of whether there is an alignment model--
|
||||
# they use the same UBM.
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/gselect.JOB.log \
|
||||
sgmm2-gselect --full-gmm-nbest=$gselect $srcdir/final.mdl \
|
||||
"$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
# Generate state-level lattice which we can rescore. This is done with the alignment
|
||||
# model and no speaker-vectors.
|
||||
if [ $stage -le 2 ]; then
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode_pass1.JOB.log \
|
||||
sgmm2-latgen-faster$thread_string --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
|
||||
--max-arcs=$max_arcs --acoustic-scale=$acwt --determinize-lattice=false --allow-partial=true \
|
||||
--word-symbol-table=$graphdir/words.txt "$gselect_opt_1stpass" $srcdir/final.alimdl \
|
||||
$graphdir/HCLG.fst "$feats" "ark:|gzip -c > $dir/pre_lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
# Estimate speaker vectors (1st pass). Prune before determinizing
|
||||
# because determinization can take a while on un-pruned lattices.
|
||||
# Note: the sgmm2-post-to-gpost stage is necessary because we have
|
||||
# a separate alignment-model and final model, otherwise we'd skip it
|
||||
# and use sgmm2-est-spkvecs.
|
||||
if [ $stage -le 3 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/vecs_pass1.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.alimdl ark:- ark:- \| \
|
||||
sgmm2-post-to-gpost "$gselect_opt" $srcdir/final.alimdl "$feats" ark:- ark:- \| \
|
||||
sgmm2-est-spkvecs-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
$srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/pre_vecs.JOB" || exit 1;
|
||||
fi
|
||||
|
||||
# Estimate speaker vectors (2nd pass). Since we already have spk vectors,
|
||||
# at this point we need to rescore the lattice to get the correct posteriors.
|
||||
if [ $stage -le 4 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/vecs_pass2.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm2-rescore-lattice --spk-vecs=ark:$dir/pre_vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm2-est-spkvecs --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/pre_vecs.JOB \
|
||||
$srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/vecs.JOB" || exit 1;
|
||||
fi
|
||||
rm $dir/pre_vecs.*
|
||||
|
||||
if $use_fmllr; then
|
||||
# Estimate fMLLR transforms (note: these may be on top of any
|
||||
# fMLLR transforms estimated with the baseline GMM system.
|
||||
if [ $stage -le 5 ]; then # compute fMLLR transforms.
|
||||
echo "$0: computing fMLLR transforms."
|
||||
if [ ! -f $srcdir/final.fmllr_mdl ] || [ $srcdir/final.fmllr_mdl -ot $srcdir/final.mdl ]; then
|
||||
echo "$0: computing pre-transform for fMLLR computation."
|
||||
sgmm2-comp-prexform $srcdir/final.mdl $srcdir/final.occs $srcdir/final.fmllr_mdl || exit 1;
|
||||
fi
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm2-rescore-lattice --spk-vecs=ark:$dir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm2-est-fmllr --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/vecs.JOB \
|
||||
--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
|
||||
$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
|
||||
fi
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
# Now rescore the state-level lattices with the adapted features and the
|
||||
# corresponding model. Prune and determinize the lattices to limit
|
||||
# their size.
|
||||
if [ $stage -le 6 ]; then
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/rescore.JOB.log \
|
||||
sgmm2-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
|
||||
$srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned$thread_string --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
rm $dir/pre_lat.*.gz
|
||||
|
||||
# The output of this script is the files "lat.*.gz"-- we'll rescore this at different
|
||||
# acoustic scales to get the final output.
|
||||
|
||||
|
||||
if [ $stage -le 7 ]; then
|
||||
if ! $skip_scoring ; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh $scoring_opts --cmd "$cmd" $data $graphdir $dir
|
||||
fi
|
||||
fi
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,270 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012-2013 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# This script does decoding with an SGMM2 system, with speaker vectors. If the
|
||||
# SGMM2 system was built on top of fMLLR transforms from a conventional system,
|
||||
# you should provide the --transform-dir option.
|
||||
|
||||
# This script does not use a decoding graph, but instead you provide
|
||||
# a previous decoding directory with lattices in it. This script will only
|
||||
# make use of the word sequences in the lattices; it limits the decoding
|
||||
# to those sequences. You should also provide a "lang" directory from
|
||||
# which this script will use the G.fst and L.fst.
|
||||
|
||||
# Begin configuration section.
|
||||
stage=1
|
||||
alignment_model=
|
||||
transform_dir= # dir to find fMLLR transforms.
|
||||
acwt=0.08333 # Just a default value, used for adaptation and beam-pruning..
|
||||
batch_size=75 # Limits memory blowup in compile-train-graphs-fsts
|
||||
cmd=run.pl
|
||||
beam=20.0
|
||||
gselect=15 # Number of Gaussian-selection indices for SGMMs. [Note:
|
||||
# the first_pass_gselect variable is used for the 1st pass of
|
||||
# decoding and can be tighter.
|
||||
first_pass_gselect=3 # Use a smaller number of Gaussian-selection indices in
|
||||
# the 1st pass of decoding (lattice generation).
|
||||
max_active=7000
|
||||
lattice_beam=8.0 # Beam we use in lattice generation.
|
||||
vecs_beam=4.0 # Beam we use to prune lattices while getting posteriors for
|
||||
# speaker-vector computation. Can be quite tight (actually we could
|
||||
# probably just do best-path.
|
||||
use_fmllr=false
|
||||
fmllr_iters=10
|
||||
fmllr_min_count=1000
|
||||
scale_opts="--transition-scale=1.0 --self-loop-scale=0.1"
|
||||
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 4 ]; then
|
||||
echo "Usage: steps/decode_sgmm_fromlats.sh [options] <data-dir> <lang-dir> <old-decode-dir> <decode-dir>"
|
||||
echo ""
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --transform-dir <decoding-dir> # directory of previous decoding"
|
||||
echo " # where we can find transforms for SAT systems."
|
||||
echo " --alignment-model <ali-mdl> # Model for the first-pass decoding."
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --beam <beam> # Decoding beam; default 13.0"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
olddir=$3
|
||||
dir=$4
|
||||
srcdir=`dirname $dir`
|
||||
|
||||
for f in $data/feats.scp $lang/G.fst $lang/L_disambig.fst $lang/phones/disambig.int \
|
||||
$srcdir/final.mdl $srcdir/tree $olddir/lat.1.gz; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
nj=`cat $olddir/num_jobs` || exit 1;
|
||||
sdata=$data/split$nj;
|
||||
silphonelist=`cat $lang/phones/silence.csl` || exit 1
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|"
|
||||
gselect_opt_1stpass="$gselect_opt copy-gselect --n=$first_pass_gselect ark:- ark:- |"
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
|
||||
## Set up features
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
if [ -z "$transform_dir" ] && [ -f $olddir/trans.1 ]; then
|
||||
transform_dir=$olddir
|
||||
fi
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
;;
|
||||
*) echo "$0: invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then
|
||||
echo "$0: using transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
|
||||
[ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
|
||||
&& echo "$0: #jobs mismatch with transform-dir." && exit 1;
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
|
||||
echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
|
||||
echo " but you are not providing the --transform-dir option in test time."
|
||||
fi
|
||||
|
||||
## Calculate FMLLR pre-transforms if needed. We are doing this here since this
|
||||
## step is requried by models both with and without speaker vectors
|
||||
if $use_fmllr; then
|
||||
if [ ! -f $srcdir/final.fmllr_mdl ] || [ $srcdir/final.fmllr_mdl -ot $srcdir/final.mdl ]; then
|
||||
echo "$0: computing pre-transform for fMLLR computation."
|
||||
sgmm2-comp-prexform $srcdir/final.mdl $srcdir/final.occs $srcdir/final.fmllr_mdl || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
## Save Gaussian-selection info to disk.
|
||||
# Note: we can use final.mdl regardless of whether there is an alignment model--
|
||||
# they use the same UBM.
|
||||
if [ $stage -le 1 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/gselect.JOB.log \
|
||||
sgmm2-gselect --full-gmm-nbest=$gselect $srcdir/final.mdl \
|
||||
"$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
## Work out name of alignment model. ##
|
||||
if [ -z "$alignment_model" ]; then
|
||||
if [ -f "$srcdir/final.alimdl" ]; then alignment_model=$srcdir/final.alimdl;
|
||||
else alignment_model=$srcdir/final.mdl; fi
|
||||
fi
|
||||
[ ! -f "$alignment_model" ] && echo "$0: no alignment model $alignment_model " && exit 1;
|
||||
|
||||
# Generate state-level lattice which we can rescore. This is done with the
|
||||
# alignment model and no speaker-vectors.
|
||||
if [ $stage -le 2 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/decode_pass1.JOB.log \
|
||||
lattice-to-fst "ark:gunzip -c $olddir/lat.JOB.gz|" ark:- \| \
|
||||
fsttablecompose "fstproject --project_output=true $lang/G.fst | fstarcsort |" ark:- ark:- \| \
|
||||
fstdeterminizestar ark:- ark:- \| \
|
||||
compile-train-graphs-fsts --read-disambig-syms=$lang/phones/disambig.int \
|
||||
--batch-size=$batch_size $scale_opts \
|
||||
$srcdir/tree $srcdir/final.mdl $lang/L_disambig.fst ark:- ark:- \| \
|
||||
sgmm2-latgen-faster --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
|
||||
--acoustic-scale=$acwt --determinize-lattice=false --allow-partial=true \
|
||||
--word-symbol-table=$lang/words.txt "$gselect_opt_1stpass" $alignment_model \
|
||||
"ark:-" "$feats" "ark:|gzip -c > $dir/pre_lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
## Check if the model has speaker vectors
|
||||
spkdim=`sgmm2-info $srcdir/final.mdl | grep 'speaker vector' | awk '{print $NF}'`
|
||||
|
||||
if [ $spkdim -gt 0 ]; then ### For models with speaker vectors:
|
||||
|
||||
# Estimate speaker vectors (1st pass). Prune before determinizing
|
||||
# because determinization can take a while on un-pruned lattices.
|
||||
# Note: the sgmm2-post-to-gpost stage is necessary because we have
|
||||
# a separate alignment-model and final model, otherwise we'd skip it
|
||||
# and use sgmm2-est-spkvecs.
|
||||
if [ $stage -le 3 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/vecs_pass1.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alignment_model ark:- ark:- \| \
|
||||
sgmm2-post-to-gpost "$gselect_opt" $alignment_model "$feats" ark:- ark:- \| \
|
||||
sgmm2-est-spkvecs-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
$srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/pre_vecs.JOB" || exit 1;
|
||||
fi
|
||||
|
||||
# Estimate speaker vectors (2nd pass). Since we already have spk vectors,
|
||||
# at this point we need to rescore the lattice to get the correct posteriors.
|
||||
if [ $stage -le 4 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/vecs_pass2.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm2-rescore-lattice --spk-vecs=ark:$dir/pre_vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm2-est-spkvecs --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/pre_vecs.JOB \
|
||||
$srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/vecs.JOB" || exit 1;
|
||||
fi
|
||||
rm $dir/pre_vecs.*
|
||||
|
||||
if $use_fmllr; then
|
||||
# Estimate fMLLR transforms (note: these may be on top of any
|
||||
# fMLLR transforms estimated with the baseline GMM system.
|
||||
if [ $stage -le 5 ]; then # compute fMLLR transforms.
|
||||
echo "$0: computing fMLLR transforms."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm2-rescore-lattice --spk-vecs=ark:$dir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm2-est-fmllr --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/vecs.JOB \
|
||||
--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
|
||||
$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
|
||||
fi
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
# Now rescore the state-level lattices with the adapted features and the
|
||||
# corresponding model. Prune and determinize the lattices to limit
|
||||
# their size.
|
||||
if [ $stage -le 6 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/rescore.JOB.log \
|
||||
sgmm2-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
|
||||
$srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
rm $dir/pre_lat.*.gz
|
||||
|
||||
else ### For models without speaker vectors:
|
||||
|
||||
if $use_fmllr; then
|
||||
# Estimate fMLLR transforms (note: these may be on top of any
|
||||
# fMLLR transforms estimated with the baseline GMM system.
|
||||
if [ $stage -le 5 ]; then # compute fMLLR transforms.
|
||||
echo "$0: computing fMLLR transforms."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm2-rescore-lattice --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm2-est-fmllr --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" \
|
||||
--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
|
||||
$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
|
||||
fi
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
# Now rescore the state-level lattices with the adapted features and the
|
||||
# corresponding model. Prune and determinize the lattices to limit
|
||||
# their size.
|
||||
if [ $stage -le 6 ] && $use_fmllr; then
|
||||
$cmd JOB=1:$nj $dir/log/rescore.JOB.log \
|
||||
sgmm2-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
$srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
rm $dir/pre_lat.*.gz
|
||||
else # Already done with decoding if no adaptation needed.
|
||||
for n in `seq 1 $nj`; do
|
||||
mv $dir/pre_lat.${n}.gz $dir/lat.${n}.gz
|
||||
done
|
||||
fi
|
||||
|
||||
fi
|
||||
|
||||
# The output of this script is the files "lat.*.gz"-- we'll rescore this at
|
||||
# different acoustic scales to get the final output.
|
||||
|
||||
|
||||
if [ $stage -le 7 ]; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
echo "score best paths"
|
||||
local/score.sh --cmd "$cmd" $data $lang $dir
|
||||
echo "score confidence and timing with sclite"
|
||||
#local/score_sclite_conf.sh --cmd "$cmd" --language turkish $data $lang $dir
|
||||
fi
|
||||
echo "Decoding done."
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# This script does decoding with an SGMM system, by rescoring lattices
|
||||
# generated from a previous SGMM system. The directory with the lattices
|
||||
# is assumed to contain speaker vectors, if used. Basically it rescores
|
||||
# the lattices one final time, using the same setup as the final decoding
|
||||
# pass of the source dir. The assumption is that the model may have
|
||||
# been discriminatively trained.
|
||||
|
||||
# If the system was built on top of fMLLR transforms from a conventional system,
|
||||
# you should provide the --transform-dir option.
|
||||
|
||||
# Begin configuration section.
|
||||
transform_dir= # dir to find fMLLR transforms.
|
||||
cmd=run.pl
|
||||
iter=final
|
||||
skip_scoring=false
|
||||
scoring_opts=
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 4 ]; then
|
||||
echo "Usage: steps/decode_sgmm_rescore.sh [options] <graph-dir|lang-dir> <data-dir> <old-decode-dir> <decode-dir>"
|
||||
echo " e.g.: steps/decode_sgmm_rescore.sh --transform-dir exp/tri3b/decode_dev93_tgpr \\"
|
||||
echo " exp/sgmm3a/graph_tgpr data/test_dev93 exp/sgmm3a/decode_dev93_tgpr exp/sgmm3a_mmi/decode_dev93_tgpr"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --transform-dir <decoding-dir> # directory of previous decoding"
|
||||
echo " # where we can find transforms for SAT systems."
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --iter <iter> # iteration of model to use (default: final)"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
olddir=$3
|
||||
dir=$4
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
|
||||
for f in $graphdir/words.txt $data/feats.scp $olddir/lat.1.gz $olddir/gselect.1.gz \
|
||||
$srcdir/$iter.mdl; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
nj=`cat $olddir/num_jobs` || exit 1;
|
||||
sdata=$data/split$nj;
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $olddir/gselect.JOB.gz|"
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
if [ -f $olddir/vecs.1 ]; then
|
||||
echo "$0: using speaker vectors from $olddir"
|
||||
spkvecs_opt="--spk-vecs=ark:$olddir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk"
|
||||
else
|
||||
echo "$0: no speaker vectors found."
|
||||
spkvecs_opt=
|
||||
fi
|
||||
|
||||
|
||||
## Set up features.
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
;;
|
||||
*) echo "$0: invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then
|
||||
echo "$0: using transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
|
||||
[ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
|
||||
&& echo "$0: #jobs mismatch with transform-dir." && exit 1;
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
|
||||
echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
|
||||
echo " but you are not providing the --transform-dir option in test time."
|
||||
fi
|
||||
|
||||
if [ -f $olddir/trans.1 ]; then
|
||||
echo "$0: using (in addition to any previous transforms) transforms from $olddir"
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$olddir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
##
|
||||
|
||||
# Rescore the state-level lattices with the model provided. Just
|
||||
# one command in this script.
|
||||
echo "$0: rescoring lattices with SGMM model in $srcdir/$iter.mdl"
|
||||
$cmd JOB=1:$nj $dir/log/rescore.JOB.log \
|
||||
sgmm2-rescore-lattice "$gselect_opt" $spkvecs_opt \
|
||||
$srcdir/$iter.mdl "ark:gunzip -c $olddir/lat.JOB.gz|" "$feats" \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
|
||||
if ! $skip_scoring ; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh $scoring_opts --cmd "$cmd" $data $graphdir $dir
|
||||
fi
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,172 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# This script does decoding with an SGMM system, by rescoring lattices
|
||||
# generated from a previous SGMM system. This version does the "predictive"
|
||||
# SGMM, where we subtract some constant times the log-prob of the left
|
||||
# few spliced frames, and the same for the right few.
|
||||
# The directory with the lattices
|
||||
# is assumed to contain any speaker vectors, if used. This script just
|
||||
# adds into the acoustic scores, (some constant, default -0.25) times
|
||||
# the acoustic score of the left model, and the same for the right model.
|
||||
|
||||
# the lattices one final time, using the same setup as the final decoding
|
||||
# pass of the source dir. The assumption is that the model may have
|
||||
# been discriminatively trained.
|
||||
|
||||
# If the system was built on top of fMLLR transforms from a conventional system,
|
||||
# you should provide the --transform-dir option.
|
||||
|
||||
# Begin configuration section.
|
||||
stage=0
|
||||
transform_dir= # dir to find fMLLR transforms.
|
||||
cmd=run.pl
|
||||
iter=final
|
||||
prob_scale=-0.25
|
||||
dimensions=0:13:104:117
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 5 ]; then
|
||||
echo "Usage: steps/decode_sgmm_rescore_project.sh [options] <full-lda-mat> <graph-dir|lang-dir> <data-dir> <old-decode-dir> <decode-dir>"
|
||||
echo " e.g.: steps/decode_sgmm_rescore_project.sh --transform-dir exp/tri3b/decode_dev93_tgpr \\"
|
||||
echo " exp/tri2b/full.mat exp/sgmm3a/graph_tgpr data/test_dev93 exp/sgmm3a/decode_dev93_tgpr exp/sgmm3a/decode_dev93_tgpr_predict"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --transform-dir <decoding-dir> # directory of previous decoding"
|
||||
echo " # where we can find transforms for SAT systems."
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --prob-scale <scale> # Default -0.25, scale on left and right models."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
full_lda_mat=$1
|
||||
graphdir=$2
|
||||
data=$3
|
||||
olddir=$4
|
||||
dir=$5
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
|
||||
for f in $full_lda_mat $graphdir/words.txt $data/feats.scp $olddir/lat.1.gz \
|
||||
$olddir/gselect.1.gz $srcdir/$iter.mdl; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
nj=`cat $olddir/num_jobs` || exit 1;
|
||||
sdata=$data/split$nj;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
if [ -f $olddir/vecs.1 ]; then
|
||||
echo "$0: using speaker vectors from $olddir"
|
||||
spkvecs_opt="--spk-vecs=ark:$olddir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk"
|
||||
else
|
||||
echo "$0: no speaker vectors found."
|
||||
spkvecs_opt=
|
||||
fi
|
||||
|
||||
if [ $stage -le 0 ]; then
|
||||
# Get full LDA+MLLT mat and its inverse. Note: the full LDA+MLLT mat is
|
||||
# the LDA+MLLT mat, plus the "rejected" rows of the LDA matrix.
|
||||
$cmd $dir/log/get_full_lda.log \
|
||||
get-full-lda-mat $srcdir/final.mat $full_lda_mat $dir/full.mat $dir/full_inv.mat || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
left_start=`echo $dimensions | cut '-d:' -f 1`;
|
||||
left_end=`echo $dimensions | cut '-d:' -f 2`;
|
||||
right_start=`echo $dimensions | cut '-d:' -f 3`;
|
||||
right_end=`echo $dimensions | cut '-d:' -f 4`;
|
||||
|
||||
# Prepare left and right models. For now, the dimensions are hardwired (e.g., 13 MFCCs and splice 9 frames).
|
||||
# Note: the choice of dividing by the prob of the left 4 and the right 4 frames is a bit arbitrary and
|
||||
# we could investigate different configurations.
|
||||
$cmd $dir/log/left.log \
|
||||
sgmm2-project --start-dim=$left_start --end-dim=$left_end $srcdir/final.mdl $dir/full.mat $dir/left.mdl $dir/left.mat || exit 1;
|
||||
$cmd $dir/log/right.log \
|
||||
sgmm2-project --start-dim=$right_start --end-dim=$right_end $srcdir/final.mdl $dir/full.mat $dir/right.mdl $dir/right.mat || exit 1;
|
||||
fi
|
||||
|
||||
|
||||
# we apply the scaling on the new acoustic probs by adding the inverse
|
||||
# of that to the old acoustic probs, and then later inverting again.
|
||||
# this has to do with limitations in sgmm2-rescore-lattice: we can only
|
||||
# scale the *old* acoustic probs, not the new ones.
|
||||
inverse_prob_scale=`perl -e "print (1.0 / $prob_scale);"`
|
||||
cur_lats="ark:gunzip -c $olddir/lat.JOB.gz | lattice-scale --acoustic-scale=$inverse_prob_scale ark:- ark:- |"
|
||||
|
||||
## Set up features. Note: we only support LDA+MLLT features, this
|
||||
## is inherent in the method, we could not support deltas.
|
||||
|
||||
for model_type in left right; do
|
||||
|
||||
feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- |" # spliced features.
|
||||
if [ ! -z "$transform_dir" ]; then # using speaker-specific transforms.
|
||||
# we want to transform in the sequence: $dir/full.mat, then the result of
|
||||
# (extend-transform-dim $transform_dir/trans.JOB), then $dir/full_inv.mat to
|
||||
# get back to the spliced space, then the left.mat or right.mat. But
|
||||
# note that compose-transforms operates in matrix-multiplication order,
|
||||
# which is opposite from the "order of applying the transforms" order.
|
||||
new_dim=$[`copy-matrix --binary=false $dir/full.mat - | wc -l` - 1]; # 117 in normal case.
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk 'ark:extend-transform-dim --new-dimension=$new_dim ark:$transform_dir/trans.JOB ark:- | compose-transforms ark:- $dir/full.mat ark:- | compose-transforms $dir/full_inv.mat ark:- ark:- | compose-transforms $dir/${model_type}.mat ark:- ark:- |' ark:- ark:- |"
|
||||
else # else, we transform with the "left" or "right" matrix; these transform from the
|
||||
# spliced space.
|
||||
feats="$feats transform-feats $dir/${model_type}.mat |"
|
||||
# If we don't have the --transform-dir option, make sure the model was
|
||||
# trained in the same way.
|
||||
if grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
|
||||
echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
|
||||
echo " but you are not providing the --transform-dir option in test time."
|
||||
fi
|
||||
fi
|
||||
if [ -f $olddir/trans.1 ]; then
|
||||
echo "$0: warning: not using transforms in $olddir (this is just a "
|
||||
echo " limitation of the script right now, and could be fixed)."
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "Getting gselect info for $model_type model."
|
||||
$cmd JOB=1:$nj $dir/log/gselect.$model_type.JOB.log \
|
||||
sgmm2-gselect $dir/$model_type.mdl "$feats" \
|
||||
"ark,t:|gzip -c >$dir/gselect.$model_type.JOB.gz" || exit 1;
|
||||
fi
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.$model_type.JOB.gz|"
|
||||
|
||||
|
||||
# Rescore the state-level lattices with the model provided. Just
|
||||
# one command in this script.
|
||||
# The --old-acoustic-scale=1.0 option means we just add the scores
|
||||
# to the old scores.
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: rescoring lattices with $model_type model"
|
||||
$cmd JOB=1:$nj $dir/log/rescore.${model_type}.JOB.log \
|
||||
sgmm2-rescore-lattice --old-acoustic-scale=1.0 "$gselect_opt" $spkvecs_opt \
|
||||
$dir/$model_type.mdl "$cur_lats" "$feats" \
|
||||
"ark:|gzip -c > $dir/lat.${model_type}.JOB.gz" || exit 1;
|
||||
fi
|
||||
cur_lats="ark:gunzip -c $dir/lat.${model_type}.JOB.gz |"
|
||||
done
|
||||
|
||||
if [ $stage -le 4 ]; then
|
||||
echo "$0: getting final lattices."
|
||||
$cmd JOB=1:$nj $dir/log/scale_lats.JOB.log \
|
||||
lattice-scale --acoustic-scale=$prob_scale "$cur_lats" "ark:|gzip -c >$dir/lat.JOB.gz" \
|
||||
|| exit 1;
|
||||
fi
|
||||
|
||||
rm $dir/lat.{left,right}.*.gz 2>/dev/null # note: if these still exist, it will
|
||||
# confuse the scoring script.
|
||||
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh --cmd "$cmd" $data $graphdir $dir
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,273 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# This script does decoding with an SGMM system, with speaker vectors.
|
||||
# If the SGMM system was
|
||||
# built on top of fMLLR transforms from a conventional system, you should
|
||||
# provide the --transform-dir option.
|
||||
# This script does not use a decoding graph, but instead you provide
|
||||
# a previous decoding directory with lattices in it. This script will only
|
||||
# make use of the word sequences in the lattices; it limits the decoding
|
||||
# to those sequences. You should also provide a "lang" directory from
|
||||
# which this script will use the G.fst and L.fst.
|
||||
|
||||
# Begin configuration section.
|
||||
stage=1
|
||||
alignment_model=
|
||||
transform_dir= # dir to find fMLLR transforms.
|
||||
acwt=0.08333 # Just a default value, used for adaptation and beam-pruning..
|
||||
batch_size=75 # Limits memory blowup in compile-train-graphs-fsts
|
||||
cmd=run.pl
|
||||
beam=20.0
|
||||
gselect=15 # Number of Gaussian-selection indices for SGMMs. [Note:
|
||||
# the first_pass_gselect variable is used for the 1st pass of
|
||||
# decoding and can be tighter.
|
||||
first_pass_gselect=3 # Use a smaller number of Gaussian-selection indices in
|
||||
# the 1st pass of decoding (lattice generation).
|
||||
max_active=7000
|
||||
|
||||
#WARNING: This option is renamed lat_beam (it was renamed to follow the naming
|
||||
# in the other scripts
|
||||
lattice_beam=8.0 # Beam we use in lattice generation.
|
||||
vecs_beam=4.0 # Beam we use to prune lattices while getting posteriors for
|
||||
# speaker-vector computation. Can be quite tight (actually we could
|
||||
# probably just do best-path.
|
||||
use_fmllr=false
|
||||
fmllr_iters=10
|
||||
fmllr_min_count=1000
|
||||
scale_opts="--transition-scale=1.0 --self-loop-scale=0.1"
|
||||
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 4 ]; then
|
||||
echo "Usage: steps/decode_sgmm_fromlats.sh [options] <data-dir> <lang-dir> <old-decode-dir> <decode-dir>"
|
||||
echo ""
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --transform-dir <decoding-dir> # directory of previous decoding"
|
||||
echo " # where we can find transforms for SAT systems."
|
||||
echo " --alignment-model <ali-mdl> # Model for the first-pass decoding."
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --beam <beam> # Decoding beam; default 13.0"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
olddir=$3
|
||||
dir=$4
|
||||
srcdir=`dirname $dir`
|
||||
|
||||
for f in $data/feats.scp $lang/G.fst $lang/L_disambig.fst $lang/phones/disambig.int \
|
||||
$srcdir/final.mdl $srcdir/tree $olddir/lat.1.gz; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
nj=`cat $olddir/num_jobs` || exit 1;
|
||||
sdata=$data/split$nj;
|
||||
silphonelist=`cat $lang/phones/silence.csl` || exit 1
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|"
|
||||
gselect_opt_1stpass="$gselect_opt copy-gselect --n=$first_pass_gselect ark:- ark:- |"
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
|
||||
## Set up features
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
if [ -z "$transform_dir" ] && [ -f $olddir/trans.1 ]; then
|
||||
transform_dir=$olddir
|
||||
fi
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
;;
|
||||
*) echo "$0: invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then
|
||||
echo "$0: using transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
|
||||
[ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
|
||||
&& echo "$0: #jobs mismatch with transform-dir." && exit 1;
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
|
||||
echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
|
||||
echo " but you are not providing the --transform-dir option in test time."
|
||||
fi
|
||||
|
||||
## Calculate FMLLR pre-transforms if needed. We are doing this here since this
|
||||
## step is requried by models both with and without speaker vectors
|
||||
if $use_fmllr; then
|
||||
if [ ! -f $srcdir/final.fmllr_mdl ] || [ $srcdir/final.fmllr_mdl -ot $srcdir/final.mdl ]; then
|
||||
echo "$0: computing pre-transform for fMLLR computation."
|
||||
sgmm-comp-prexform $srcdir/final.mdl $srcdir/final.occs $srcdir/final.fmllr_mdl || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
## Save Gaussian-selection info to disk.
|
||||
# Note: we can use final.mdl regardless of whether there is an alignment model--
|
||||
# they use the same UBM.
|
||||
if [ $stage -le 1 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/gselect.JOB.log \
|
||||
sgmm-gselect --full-gmm-nbest=$gselect $srcdir/final.mdl \
|
||||
"$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
## Work out name of alignment model. ##
|
||||
if [ -z "$alignment_model" ]; then
|
||||
if [ -f "$srcdir/final.alimdl" ]; then alignment_model=$srcdir/final.alimdl;
|
||||
else alignment_model=$srcdir/final.mdl; fi
|
||||
fi
|
||||
[ ! -f "$alignment_model" ] && echo "$0: no alignment model $alignment_model " && exit 1;
|
||||
|
||||
# Generate state-level lattice which we can rescore. This is done with the
|
||||
# alignment model and no speaker-vectors.
|
||||
if [ $stage -le 2 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/decode_pass1.JOB.log \
|
||||
lattice-to-fst "ark:gunzip -c $olddir/lat.JOB.gz|" ark:- \| \
|
||||
fsttablecompose "fstproject --project_output=true $lang/G.fst | fstarcsort |" ark:- ark:- \| \
|
||||
fstdeterminizestar ark:- ark:- \| \
|
||||
compile-train-graphs-fsts --read-disambig-syms=$lang/phones/disambig.int \
|
||||
--batch-size=$batch_size $scale_opts \
|
||||
$srcdir/tree $srcdir/final.mdl $lang/L_disambig.fst ark:- ark:- \| \
|
||||
sgmm-latgen-faster --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
|
||||
--acoustic-scale=$acwt --determinize-lattice=false --allow-partial=true \
|
||||
--word-symbol-table=$lang/words.txt "$gselect_opt_1stpass" $alignment_model \
|
||||
"ark:-" "$feats" "ark:|gzip -c > $dir/pre_lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
## Check if the model has speaker vectors
|
||||
spkdim=`sgmm-info $srcdir/final.mdl | grep 'speaker vector' | awk '{print $NF}'`
|
||||
|
||||
if [ $spkdim -gt 0 ]; then ### For models with speaker vectors:
|
||||
|
||||
# Estimate speaker vectors (1st pass). Prune before determinizing
|
||||
# because determinization can take a while on un-pruned lattices.
|
||||
# Note: the sgmm-post-to-gpost stage is necessary because we have
|
||||
# a separate alignment-model and final model, otherwise we'd skip it
|
||||
# and use sgmm-est-spkvecs.
|
||||
if [ $stage -le 3 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/vecs_pass1.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alignment_model ark:- ark:- \| \
|
||||
sgmm-post-to-gpost "$gselect_opt" $alignment_model "$feats" ark:- ark:- \| \
|
||||
sgmm-est-spkvecs-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
$srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/pre_vecs.JOB" || exit 1;
|
||||
fi
|
||||
|
||||
# Estimate speaker vectors (2nd pass). Since we already have spk vectors,
|
||||
# at this point we need to rescore the lattice to get the correct posteriors.
|
||||
if [ $stage -le 4 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/vecs_pass2.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm-rescore-lattice --spk-vecs=ark:$dir/pre_vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm-est-spkvecs --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/pre_vecs.JOB \
|
||||
$srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/vecs.JOB" || exit 1;
|
||||
fi
|
||||
rm $dir/pre_vecs.*
|
||||
|
||||
if $use_fmllr; then
|
||||
# Estimate fMLLR transforms (note: these may be on top of any
|
||||
# fMLLR transforms estimated with the baseline GMM system.
|
||||
if [ $stage -le 5 ]; then # compute fMLLR transforms.
|
||||
echo "$0: computing fMLLR transforms."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm-rescore-lattice --spk-vecs=ark:$dir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm-est-fmllr --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/vecs.JOB \
|
||||
--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
|
||||
$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
|
||||
fi
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
# Now rescore the state-level lattices with the adapted features and the
|
||||
# corresponding model. Prune and determinize the lattices to limit
|
||||
# their size.
|
||||
if [ $stage -le 6 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/rescore.JOB.log \
|
||||
sgmm-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
|
||||
$srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
rm $dir/pre_lat.*.gz
|
||||
|
||||
else ### For models without speaker vectors:
|
||||
|
||||
if $use_fmllr; then
|
||||
# Estimate fMLLR transforms (note: these may be on top of any
|
||||
# fMLLR transforms estimated with the baseline GMM system.
|
||||
if [ $stage -le 5 ]; then # compute fMLLR transforms.
|
||||
echo "$0: computing fMLLR transforms."
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
gunzip -c $dir/pre_lat.JOB.gz \| \
|
||||
sgmm-rescore-lattice --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
|
||||
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
|
||||
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
|
||||
sgmm-est-fmllr --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" \
|
||||
--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
|
||||
$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
|
||||
fi
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
# Now rescore the state-level lattices with the adapted features and the
|
||||
# corresponding model. Prune and determinize the lattices to limit
|
||||
# their size.
|
||||
if [ $stage -le 6 ] && $use_fmllr; then
|
||||
$cmd JOB=1:$nj $dir/log/rescore.JOB.log \
|
||||
sgmm-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata/JOB/utt2spk \
|
||||
$srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
|
||||
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
rm $dir/pre_lat.*.gz
|
||||
else # Already done with decoding if no adaptation needed.
|
||||
for n in `seq 1 $nj`; do
|
||||
mv $dir/pre_lat.${n}.gz $dir/lat.${n}.gz
|
||||
done
|
||||
fi
|
||||
|
||||
fi
|
||||
|
||||
# The output of this script is the files "lat.*.gz"-- we'll rescore this at
|
||||
# different acoustic scales to get the final output.
|
||||
|
||||
|
||||
if [ $stage -le 7 ]; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
echo "score best paths"
|
||||
local/score.sh --cmd "$cmd" $data $lang $dir
|
||||
echo "score confidence and timing with sclite"
|
||||
#local/score_sclite_conf.sh --cmd "$cmd" --language turkish $data $lang $dir
|
||||
fi
|
||||
echo "Decoding done."
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,107 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# This script does decoding with an SGMM system, by rescoring lattices
|
||||
# generated from a previous SGMM system. The directory with the lattices
|
||||
# is assumed to contain speaker vectors, if used. Basically it rescores
|
||||
# the lattices one final time, using the same setup as the final decoding
|
||||
# pass of the source dir. The assumption is that the model may have
|
||||
# been discriminatively trained.
|
||||
|
||||
# If the system was built on top of fMLLR transforms from a conventional system,
|
||||
# you should provide the --transform-dir option.
|
||||
|
||||
# Begin configuration section.
|
||||
transform_dir= # dir to find fMLLR transforms.
|
||||
cmd=run.pl
|
||||
iter=final
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 4 ]; then
|
||||
echo "Usage: steps/decode_sgmm_rescore.sh [options] <graph-dir|lang-dir> <data-dir> <old-decode-dir> <decode-dir>"
|
||||
echo " e.g.: steps/decode_sgmm_rescore.sh --transform-dir exp/tri3b/decode_dev93_tgpr \\"
|
||||
echo " exp/sgmm3a/graph_tgpr data/test_dev93 exp/sgmm3a/decode_dev93_tgpr exp/sgmm3a_mmi/decode_dev93_tgpr"
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --transform-dir <decoding-dir> # directory of previous decoding"
|
||||
echo " # where we can find transforms for SAT systems."
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
echo " --iter <iter> # iteration of model to use (default: final)"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
olddir=$3
|
||||
dir=$4
|
||||
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
|
||||
|
||||
for f in $graphdir/words.txt $data/feats.scp $olddir/lat.1.gz $olddir/gselect.1.gz \
|
||||
$srcdir/$iter.mdl; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
nj=`cat $olddir/num_jobs` || exit 1;
|
||||
sdata=$data/split$nj;
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $olddir/gselect.JOB.gz|"
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
if [ -f $olddir/vecs.1 ]; then
|
||||
echo "$0: using speaker vectors from $olddir"
|
||||
spkvecs_opt="--spk-vecs=ark:$olddir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk"
|
||||
else
|
||||
echo "$0: no speaker vectors found."
|
||||
spkvecs_opt=
|
||||
fi
|
||||
|
||||
|
||||
## Set up features.
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
;;
|
||||
*) echo "$0: invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then
|
||||
echo "$0: using transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
|
||||
[ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
|
||||
&& echo "$0: #jobs mismatch with transform-dir." && exit 1;
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
|
||||
echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
|
||||
echo " but you are not providing the --transform-dir option in test time."
|
||||
fi
|
||||
|
||||
if [ -f $olddir/trans.1 ]; then
|
||||
echo "$0: using (in addition to any previous transforms) transforms from $olddir"
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$olddir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
##
|
||||
|
||||
# Rescore the state-level lattices with the model provided. Just
|
||||
# one command in this script.
|
||||
echo "$0: rescoring lattices with SGMM model in $srcdir/$iter.mdl"
|
||||
$cmd JOB=1:$nj $dir/log/rescore.JOB.log \
|
||||
sgmm-rescore-lattice "$gselect_opt" $spkvecs_opt \
|
||||
$srcdir/$iter.mdl "ark:gunzip -c $olddir/lat.JOB.gz|" "$feats" \
|
||||
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh --cmd "$cmd" $data $graphdir $dir
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
# Begin configuration section.
|
||||
transform_dir=
|
||||
iter=
|
||||
model= # You can specify the model to use (e.g. if you want to use the .alimdl)
|
||||
stage=0
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
max_active=7000
|
||||
max_arcs=-1
|
||||
beam=13.0
|
||||
latbeam=6.0
|
||||
acwt=0.083333 # note: only really affects pruning (scoring is on lattices).
|
||||
num_threads=1 # if >1, will use gmm-latgen-faster-parallel
|
||||
parallel_opts= # If you supply num-threads, you should supply this too.
|
||||
scoring_opts=
|
||||
# note: there are no more min-lmwt and max-lmwt options, instead use
|
||||
# e.g. --scoring-opts "--min-lmwt 1 --max-lmwt 20"
|
||||
skip_scoring=false
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Usage: steps/decode.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo "... where <decode-dir> is assumed to be a sub-directory of the directory"
|
||||
echo " where the model is."
|
||||
echo "e.g.: steps/decode.sh exp/mono/graph_tgpr data/test_dev93 exp/mono/decode_dev93_tgpr"
|
||||
echo ""
|
||||
echo "This script works on CMN + (delta+delta-delta | LDA+MLLT) features; it works out"
|
||||
echo "what type of features you used (assuming it's one of these two)"
|
||||
echo ""
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --iter <iter> # Iteration of model to test."
|
||||
echo " --model <model> # which model to use (e.g. to"
|
||||
echo " # specify the final.alimdl)"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --transform-dir <trans-dir> # dir to find fMLLR transforms "
|
||||
echo " --acwt <float> # acoustic scale used for lattice generation "
|
||||
echo " --scoring-opts <string> # options to local/score.sh"
|
||||
echo " --num-threads <n> # number of threads to use, default 1."
|
||||
echo " --parallel-opts <opts> # e.g. '-pe smp 4' if you supply --num-threads 4"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=$3
|
||||
srcdir=`dirname $dir`; # The model directory is one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
if [ -z "$model" ]; then # if --model <mdl> was not specified on the command line...
|
||||
if [ -z $iter ]; then model=$srcdir/final.mdl;
|
||||
else model=$srcdir/$iter.mdl; fi
|
||||
fi
|
||||
|
||||
for f in $sdata/1/feats.scp $sdata/1/cmvn.scp $model $graphdir/HCLG.fst; do
|
||||
[ ! -f $f ] && echo "decode.sh: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "decode.sh: feature type is $feat_type";
|
||||
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
thread_string=
|
||||
[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then # add transforms to features...
|
||||
echo "Using fMLLR transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
|
||||
[ "`cat $transform_dir/num_jobs`" -ne $nj ] && \
|
||||
echo "Mismatch in number of jobs with $transform_dir";
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
if [ $stage -le 0 ]; then
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode.JOB.log \
|
||||
gmm-latgen-faster$thread_string --max-arcs=$max_arcs --max-active=$max_active --beam=$beam --lattice-beam=$latbeam \
|
||||
--acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$model $graphdir/HCLG.fst "$feats" "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
if ! $skip_scoring ; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh --cmd "$cmd" $scoring_opts $data $graphdir $dir
|
||||
fi
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,113 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Neha Agrawal, Cisco Systems;
|
||||
# Johns Hopkins University (Author: Daniel Povey);
|
||||
#
|
||||
# Apache 2.0
|
||||
|
||||
# Begin configuration section.
|
||||
transform_dir=
|
||||
iter=
|
||||
model= # You can specify the model to use (e.g. if you want to use the .alimdl)
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
max_active=7000
|
||||
beam=13.0
|
||||
latbeam=6.0
|
||||
acwt=0.083333 # note: only really affects pruning (scoring is on lattices).
|
||||
mean_tau=20
|
||||
weight_tau=10
|
||||
flags=mw # could also contain "v" for variance; the default
|
||||
# tau for that is 50.
|
||||
stage=1
|
||||
# End configuration section.
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
if [ $# != 3 ]; then
|
||||
echo "Usage: steps/decode.sh [options] <graph-dir> <data-dir> <decode-dir>"
|
||||
echo "... where <decode-dir> is assumed to be a sub-directory of the directory"
|
||||
echo " where the model is."
|
||||
echo "e.g.: steps/decode.sh exp/mono/graph_tgpr data/test_dev93 exp/mono/decode_dev93_tgpr"
|
||||
echo ""
|
||||
echo "This script works on CMN + (delta+delta-delta | LDA+MLLT) features; it works out"
|
||||
echo "what type of features you used (assuming it's one of these two)"
|
||||
echo ""
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --iter <iter> # Iteration of model to test."
|
||||
echo " --model <model> # which model to use (e.g. to"
|
||||
echo " # specify the final.alimdl)"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --transform-dir <trans-dir> # dir to find fMLLR transforms "
|
||||
echo " # speaker-adapted decoding"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
graphdir=$1
|
||||
data=$2
|
||||
dir=$3
|
||||
srcdir=`dirname $dir`; # The model directory is one level up from decoding directory.
|
||||
sdata=$data/split$nj;
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
if [ -z "$model" ]; then # if --model <mdl> was not specified on the command line...
|
||||
if [ -z $iter ]; then model=$srcdir/final.mdl;
|
||||
else model=$srcdir/$iter.mdl; fi
|
||||
fi
|
||||
|
||||
for f in $sdata/1/feats.scp $sdata/1/cmvn.scp $model $graphdir/HCLG.fst; do
|
||||
[ ! -f $f ] && echo "decode.sh: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "decode.sh: feature type is $feat_type";
|
||||
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
if [ ! -z "$transform_dir" ]; then # add transforms to features...
|
||||
echo "Using fMLLR transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
|
||||
[ "`cat $transform_dir/num_jobs`" -ne $nj ] && \
|
||||
echo "Mismatch in number of jobs with $transform_dir";
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
fi
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "Doing first-pass decoding before MAP decoding."
|
||||
$cmd JOB=1:$nj $dir/log/decode_pass1.JOB.log \
|
||||
gmm-decode-faster --max-active=$max_active --beam=$beam \
|
||||
--acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$model $graphdir/HCLG.fst "$feats" ark:$dir/tmp.JOB.tra ark:$dir/pass1_decode.JOB.ali || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "Computing MAP stats and doing MAP-adapted decoding"
|
||||
$cmd JOB=1:$nj $dir/log/decode_pass2.JOB.log \
|
||||
ali-to-post ark:$dir/pass1_decode.JOB.ali ark:- \| \
|
||||
gmm-adapt-map --mean-tau=$mean_tau --weight-tau=$weight_tau \
|
||||
--update-flags=$flags --spk2utt=ark:$sdata/JOB/spk2utt \
|
||||
$model "$feats" ark:- ark:- \| \
|
||||
gmm-latgen-map --lattice-beam=$latbeam --acoustic-scale=$acwt \
|
||||
--utt2spk=ark:$sdata/JOB/utt2spk --max-active=$max_active --beam=$beam \
|
||||
--allow-partial=true --word-symbol-table=$graphdir/words.txt \
|
||||
$model ark,s,cs:- $graphdir/HCLG.fst "$feats" "ark:|gzip -c >$dir/lat.JOB.gz"
|
||||
fi
|
||||
#rm -f $dir/pass1_decode.*.ali
|
||||
#rm -f $dir/tmp.*.tra
|
||||
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh --cmd "$cmd" $data $graphdir $dir
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
#!/bin/bash
|
||||
# Copyright Johns Hopkins University (Author: Daniel Povey) 2012. Apache 2.0.
|
||||
|
||||
# This script produces CTM files from a decoding directory that has lattices
|
||||
# present.
|
||||
|
||||
|
||||
# begin configuration section.
|
||||
cmd=run.pl
|
||||
stage=0
|
||||
use_segments=true # if we have a segments file, use it to convert
|
||||
# the segments to be relative to the original files.
|
||||
#end configuration section.
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 3 ]; then
|
||||
echo "Usage: local/get_ctm.sh [options] <data-dir> <lang-dir|graph-dir> <decode-dir>"
|
||||
echo " Options:"
|
||||
echo " --cmd (run.pl|queue.pl...) # specify how to run the sub-processes."
|
||||
echo " --stage (0|1|2) # start scoring script from part-way through."
|
||||
echo " --use-segments (true|false) # use segments and reco2file_and_channel files "
|
||||
echo " # to produce a ctm relative to the original audio"
|
||||
echo " # files, with channel information (typically needed"
|
||||
echo " # for NIST scoring)."
|
||||
echo "e.g.:"
|
||||
echo "local/get_ctm.sh data/train data/lang exp/tri4a/decode/"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2 # Note: may be graph directory not lang directory, but has the necessary stuff copied.
|
||||
dir=$3
|
||||
|
||||
model=$dir/../final.mdl # assume model one level up from decoding dir.
|
||||
|
||||
|
||||
for f in $lang/words.txt $lang/phones/word_boundary.int \
|
||||
$model $dir/lat.1.gz; do
|
||||
[ ! -f $f ] && echo "$0: expecting file $f to exist" && exit 1;
|
||||
done
|
||||
|
||||
name=`basename $data`; # e.g. eval2000
|
||||
|
||||
mkdir -p $dir/scoring/log
|
||||
|
||||
if [ $stage -le 0 ]; then
|
||||
if [ -f $data/segments ]; then
|
||||
f=$data/reco2file_and_channel
|
||||
[ ! -f $f ] && echo "$0: expecting file $f to exist" && exit 1;
|
||||
filter_cmd="utils/convert_ctm.pl $data/segments $data/reco2file_and_channel"
|
||||
else
|
||||
filter_cmd=cat
|
||||
fi
|
||||
|
||||
$cmd LMWT=5:20 $dir/scoring/log/get_ctm.LMWT.log \
|
||||
mkdir -p $dir/score_LMWT/ '&&' \
|
||||
lattice-1best --lm-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \
|
||||
lattice-align-words $lang/phones/word_boundary.int $model ark:- ark:- \| \
|
||||
nbest-to-ctm ark:- - \| \
|
||||
utils/int2sym.pl -f 5 $lang/words.txt \| \
|
||||
$filter_cmd '>' $dir/score_LMWT/$name.ctm || exit 1;
|
||||
fi
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,95 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Carnegie Mellon University (Author: Yajie Miao)
|
||||
# Johns Hopkins University (Author: Daniel Povey)
|
||||
|
||||
# Decoding script that computes basis for basis-fMLLR (see decode_fmllr_basis.sh).
|
||||
# This can be on top of delta+delta-delta, or LDA+MLLT features.
|
||||
|
||||
stage=0
|
||||
# Parameters in alignment of training data
|
||||
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
|
||||
per_utt=true # If true, then treat each utterance as a separate speaker for purposes of
|
||||
# basis training... this is recommended if the number of actual speakers in your
|
||||
# training set is less than (feature-dim) * (feature-dim+1).
|
||||
align_beam=10
|
||||
retry_beam=40
|
||||
silence_weight=0.01
|
||||
cmd=run.pl
|
||||
# End configuration section
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Usage: steps/get_fmllr_basis.sh [options] <data-dir> <lang-dir> <exp-dir>"
|
||||
echo " e.g.: steps/decode_basis_fmllr.sh data/train_si84 data/lang exp/tri3b/"
|
||||
echo "Note: we currently assume that this is the same data you trained the model with."
|
||||
echo "main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --cmd <cmd> # Command to run in parallel with"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
dir=$3
|
||||
|
||||
nj=`cat $dir/num_jobs` || exit 1;
|
||||
sdata=$data/split$nj;
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
|
||||
splice_opts=`cat $dir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
|
||||
silphonelist=`cat $lang/phones/silence.csl` || exit 1;
|
||||
|
||||
for f in $data/feats.scp $dir/final.alimdl $dir/final.mdl $dir/ali.1.gz; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
|
||||
# Set up the unadapted features "$sifeats".
|
||||
if [ -f $dir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type";
|
||||
case $feat_type in
|
||||
delta) sifeats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) sifeats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $dir/final.mat ark:- ark:- |";;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
# Set up the adapted features "$feats" for training set.
|
||||
if [ -f $srcdir/trans.1 ]; then
|
||||
feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$sdata/trans.JOB ark:- ark:- |";
|
||||
else
|
||||
feats="$sifeats";
|
||||
fi
|
||||
|
||||
|
||||
if $per_utt; then
|
||||
spk2utt_opt= # treat each utterance as separate speaker when computing basis.
|
||||
echo "Doing per-utterance adaptation for purposes of computing the basis."
|
||||
else
|
||||
echo "Doing per-speaker adaptation for purposes of computing the basis."
|
||||
[ `cat $sdata/spk2utt | wc -l` -lt $[41*40] ] && \
|
||||
echo "Warning: number of speakers is small, might be better to use --per-utt=true."
|
||||
spk2utt_opt="--spk2utt=ark:$sdata/JOB/spk2utt"
|
||||
fi
|
||||
|
||||
# Note: we get Gaussian level alignments with the "final.mdl" and the
|
||||
# speaker adapted features.
|
||||
$cmd JOB=1:$nj $dir/log/basis_acc.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post $silence_weight $silphonelist $dir/final.mdl ark:- ark:- \| \
|
||||
gmm-post-to-gpost $dir/final.mdl "$feats" ark:- ark:- \| \
|
||||
gmm-basis-fmllr-accs-gpost $spk2utt_opt \
|
||||
$dir/final.mdl "$sifeats" ark,s,cs:- $dir/basis.acc.JOB || exit 1;
|
||||
|
||||
# Compute the basis matrices.
|
||||
$cmd $dir/log/basis_training.log \
|
||||
gmm-basis-fmllr-training $dir/final.mdl $dir/fmllr.basis $dir/basis.acc.* || exit 1;
|
||||
rm $dir/basis.acc.* 2>/dev/null
|
||||
|
||||
exit 0;
|
||||
|
||||
|
|
@ -0,0 +1,225 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2013 Johns Hopkins University (Author: Daniel Povey)
|
||||
# Apache 2.0
|
||||
|
||||
|
||||
# From a training or alignment directory, and an original lexicon.txt and lang/
|
||||
# directory, obtain a new lexicon with pronunciation probabilities.
|
||||
|
||||
|
||||
# Begin configuration section.
|
||||
stage=0
|
||||
smooth_count=1.0 # Amount of count to add corresponding to each original lexicon entry;
|
||||
# this corresponds to add-one smoothing of the pron-probs.
|
||||
max_one=true # If true, normalize the pron-probs so the maximum value for each word is 1.0,
|
||||
# rather than summing to one. This is quite standard.
|
||||
|
||||
# End configuration options.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f path.sh ] && . ./path.sh # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 6 ]; then
|
||||
echo "Usage: steps/get_lexicon_probs.sh <data-dir> <lang-dir> <src-dir|ali-dir> <old-lexicon> <exp-dir> <new-lexicon>"
|
||||
echo "e.g.: steps/get_lexicon_probs.sh data/train data/lang exp/tri5 data/local/lexicon.txt \\"
|
||||
echo " exp/tri5_lexprobs data/local_withprob/lexicon.txt"
|
||||
echo "Note: we assume you ran using word-position-dependent phones but both the old and new lexicon will not have"
|
||||
echo "these markings. We also assume the new lexicon will have pron-probs but the old one does not; this limitation"
|
||||
echo "of the script can be removed later."
|
||||
echo "Main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --stage <stage> # used to control partial re-running."
|
||||
echo " --max-one <true|false> # If true, normalize so max prob of each"
|
||||
echo " # word is one. Default: true"
|
||||
echo " --smooth <smooth-count> # Amount to smooth each count by (default: 1.0)"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
old_lexicon=$4
|
||||
dir=$5
|
||||
new_lexicon=$6
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
nj=`cat $srcdir/num_jobs` || exit 1;
|
||||
|
||||
for f in $data/text $lang/L.fst $lang/phones/word_boundary.int $srcdir/ali.1.gz $old_lexicon; do
|
||||
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
|
||||
done
|
||||
|
||||
mkdir -p $dir/log
|
||||
utils/split_data.sh $data $nj # Make sure split data-dir exists.
|
||||
sdata=$data/split$nj
|
||||
|
||||
|
||||
mkdir -p $dir/log
|
||||
|
||||
if [ $stage -le 0 ]; then
|
||||
|
||||
( ( for n in `seq $nj`; do gunzip -c $srcdir/ali.$n.gz; done ) | \
|
||||
linear-to-nbest ark:- "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $data/text |" '' '' ark:- | \
|
||||
lattice-align-words $lang/phones/word_boundary.int $srcdir/final.mdl ark:- ark:- | \
|
||||
lattice-to-phone-lattice --replace-words=false $srcdir/final.mdl ark:- ark,t:- | \
|
||||
awk '{ if (NF == 4) { word_phones = sprintf("%s %s", $3, $4); count[word_phones]++; } }
|
||||
END { for(key in count) { print count[key], key; } }' | \
|
||||
sed s:0,0,:: | awk '{print $2, $1, $3;}' | sed 's/_/ /g' | \
|
||||
utils/int2sym.pl -f 3- $lang/phones.txt | \
|
||||
sed -E 's/_I( |$)/ /g' | sed -E 's/_E( |$)/ /g' | sed -E 's/_B( |$)/ /g' | sed -E 's/_S( |$)/ /g' | \
|
||||
utils/int2sym.pl -f 1 $lang/words.txt > $dir/lexicon_counts.txt
|
||||
) 2>&1 | tee $dir/log/get_fsts.log
|
||||
|
||||
fi
|
||||
|
||||
cat $old_lexicon | awk '{if (!($2 > 0.0 && $2 < 1.0)) { exit(1); }}' && \
|
||||
echo "Error: old lexicon $old_lexicon appears to have pron-probs; we don't expect this." && \
|
||||
exit 1;
|
||||
|
||||
mkdir -p `dirname $new_lexicon` || exit 1;
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
grep -v -w '^<eps>' $dir/lexicon_counts.txt | \
|
||||
perl -e ' ($old_lexicon, $smooth_count, $max_one) = @ARGV;
|
||||
($smooth_count >= 0) || die "Invalid smooth_count $smooth_count";
|
||||
($max_one eq "true" || $max_one eq "false") || die "Invalid max_one variable $max_one";
|
||||
open(O, "<$old_lexicon")||die "Opening old-lexicon file $old_lexicon";
|
||||
while(<O>) {
|
||||
$_ =~ m/(\S+)\s+(.+)/ || die "Bad old-lexicon line $_";
|
||||
$word = $1;
|
||||
$orig_pron = $2;
|
||||
# Remember the mapping from canonical prons to original prons: in the case of
|
||||
# syllable based systems we want to remember the locations of tabs in
|
||||
# the original lexicon.
|
||||
$pron = join(" ", split(" ", $orig_pron));
|
||||
$orig_pron{$word,$pron} = $orig_pron;
|
||||
$count{$word,$pron} += $smooth_count;
|
||||
$tot_count{$word} += $smooth_count;
|
||||
}
|
||||
while (<STDIN>) {
|
||||
$_ =~ m/(\S+)\s+(\S+)\s+(.+)/ || die "Bad new-lexicon line $_";
|
||||
$word = $1;
|
||||
$this_count = $2;
|
||||
$pron = join(" ", split(" ", $3));
|
||||
$count{$word,$pron} += $this_count;
|
||||
$tot_count{$word} += $this_count;
|
||||
}
|
||||
if ($max_one eq "true") { # replace $tot_count{$word} with max count
|
||||
# of any pron.
|
||||
%tot_count = {}; # set to empty assoc array.
|
||||
foreach $key (keys %count) {
|
||||
($word, $pron) = split($; , $key); # $; is separator for strings that index assoc. arrays.
|
||||
$this_count = $count{$key};
|
||||
if (!defined $tot_count{$word} || $this_count > $tot_count{$word}) {
|
||||
$tot_count{$word} = $this_count;
|
||||
}
|
||||
}
|
||||
}
|
||||
foreach $key (keys %count) {
|
||||
($word, $pron) = split($; , $key); # $; is separator for strings that index assoc. arrays.
|
||||
$this_orig_pron = $orig_pron{$key};
|
||||
if (!defined $this_orig_pron) { die "Word $word and pron $pron did not appear in original lexicon."; }
|
||||
if (!defined $tot_count{$word}) { die "Tot-count not defined for word $word."; }
|
||||
$prob = $count{$key} / $tot_count{$word};
|
||||
print "$word\t$prob\t$this_orig_pron\n"; # Output happens here.
|
||||
} ' $old_lexicon $smooth_count $max_one > $new_lexicon || exit 1;
|
||||
fi
|
||||
|
||||
exit 0;
|
||||
|
||||
echo $nj > $dir/num_jobs
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
|
||||
cp $srcdir/{tree,final.mdl} $dir || exit 1;
|
||||
cp $srcdir/final.occs $dir;
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
|
||||
cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
|
||||
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "$0: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) sifeats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) sifeats="ark,s,cs:apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
cp $srcdir/final.mat $dir
|
||||
;;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
## Set up model and alignment model.
|
||||
mdl=$srcdir/final.mdl
|
||||
if [ -f $srcdir/final.alimdl ]; then
|
||||
alimdl=$srcdir/final.alimdl
|
||||
else
|
||||
alimdl=$srcdir/final.mdl
|
||||
fi
|
||||
[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
|
||||
alimdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $alimdl - |"
|
||||
mdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $mdl - |"
|
||||
|
||||
|
||||
## Work out where we're getting the graphs from.
|
||||
if $use_graphs; then
|
||||
[ "$nj" != "`cat $srcdir/num_jobs`" ] && \
|
||||
echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
|
||||
[ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
|
||||
graphdir=$srcdir
|
||||
else
|
||||
graphdir=$dir
|
||||
if [ $stage -le 0 ]; then
|
||||
echo "$0: compiling training graphs"
|
||||
tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
|
||||
$cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \
|
||||
compile-train-graphs $dir/tree $dir/final.mdl $lang/L.fst "$tra" \
|
||||
"ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
|
||||
if [ $stage -le 1 ]; then
|
||||
echo "$0: aligning data in $data using $alimdl and speaker-independent features."
|
||||
$cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
|
||||
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$alimdl_cmd" \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$sifeats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ]; then
|
||||
echo "$0: computing fMLLR transforms"
|
||||
if [ "$alimdl" != "$mdl" ]; then
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
gmm-post-to-gpost $alimdl "$sifeats" ark:- ark:- \| \
|
||||
gmm-est-fmllr-gpost --fmllr-update-type=$fmllr_update_type \
|
||||
--spk2utt=ark:$sdata/JOB/spk2utt $mdl "$sifeats" \
|
||||
ark,s,cs:- ark:$dir/trans.JOB || exit 1;
|
||||
else
|
||||
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
|
||||
ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
|
||||
weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
|
||||
gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \
|
||||
--spk2utt=ark:$sdata/JOB/spk2utt $mdl "$sifeats" \
|
||||
ark,s,cs:- ark:$dir/trans.JOB || exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
|
||||
|
||||
if [ $stage -le 3 ]; then
|
||||
echo "$0: doing final alignment."
|
||||
$cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
|
||||
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl_cmd" \
|
||||
"ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
|
||||
fi
|
||||
|
||||
rm $dir/pre_ali.*.gz
|
||||
|
||||
echo "$0: done aligning data."
|
||||
|
||||
utils/summarize_warnings.pl $dir/log
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
#!/bin/bash
|
||||
# Copyright Johns Hopkins University (Author: Daniel Povey) 2012. Apache 2.0.
|
||||
|
||||
# This script produces CTM files from a training directory that has alignments
|
||||
# present.
|
||||
|
||||
|
||||
# begin configuration section.
|
||||
cmd=run.pl
|
||||
stage=0
|
||||
use_segments=true # if we have a segments file, use it to convert
|
||||
# the segments to be relative to the original files.
|
||||
#end configuration section.
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# -ne 3 ]; then
|
||||
echo "Usage: local/get_train_ctm.sh [options] <data-dir> <lang-dir> <ali-dir|exp-dir>"
|
||||
echo " Options:"
|
||||
echo " --cmd (run.pl|queue.pl...) # specify how to run the sub-processes."
|
||||
echo " --stage (0|1|2) # start scoring script from part-way through."
|
||||
echo " --use-segments (true|false) # use segments and reco2file_and_channel files "
|
||||
echo " # to produce a ctm relative to the original audio"
|
||||
echo " # files, with channel information (typically needed"
|
||||
echo " # for NIST scoring)."
|
||||
echo "e.g.:"
|
||||
echo "local/get_train_ctm.sh data/train data/lang exp/tri3a_ali"
|
||||
echo "Produces ctm in: exp/tri3a_ali/ctm"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2 # Note: may be graph directory not lang directory, but has the necessary stuff copied.
|
||||
dir=$3
|
||||
|
||||
model=$dir/final.mdl # assume model one level up from decoding dir.
|
||||
|
||||
|
||||
for f in $lang/words.txt $lang/phones/word_boundary.int \
|
||||
$model $dir/ali.1.gz $lang/oov.int; do
|
||||
[ ! -f $f ] && echo "$0: expecting file $f to exist" && exit 1;
|
||||
done
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
|
||||
mkdir -p $dir/scoring/log
|
||||
|
||||
if [ $stage -le 0 ]; then
|
||||
if [ -f $data/segments ]; then
|
||||
f=$data/reco2file_and_channel
|
||||
[ ! -f $f ] && echo "$0: expecting file $f to exist" && exit 1;
|
||||
filter_cmd="utils/convert_ctm.pl $data/segments $data/reco2file_and_channel"
|
||||
else
|
||||
filter_cmd=cat
|
||||
fi
|
||||
|
||||
$cmd $dir/log/get_ctm.log \
|
||||
linear-to-nbest "ark:gunzip -c $dir/ali.*.gz|" \
|
||||
"ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt < $data/text |" \
|
||||
'' '' ark:- \| \
|
||||
lattice-align-words $lang/phones/word_boundary.int $model ark:- ark:- \| \
|
||||
nbest-to-ctm ark:- - \| \
|
||||
utils/int2sym.pl -f 5 $lang/words.txt \| \
|
||||
$filter_cmd '>' $dir/ctm || exit 1;
|
||||
fi
|
||||
|
|
@ -0,0 +1,122 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Begin configuration section.
|
||||
mode=4
|
||||
cmd=run.pl
|
||||
skip_scoring=false
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
for x in `seq 2`; do
|
||||
[ "$1" == "--cmd" ] && cmd=$2 && shift 2;
|
||||
[ "$1" == "--mode" ] && mode=$2 && shift 2;
|
||||
done
|
||||
|
||||
if [ $# != 5 ]; then
|
||||
echo "Do language model rescoring of lattices (remove old LM, add new LM)"
|
||||
echo "Usage: steps/lmrescore.sh [options] <old-lang-dir> <new-lang-dir> <data-dir> <input-decode-dir> <output-decode-dir>"
|
||||
echo "options: [--cmd (run.pl|queue.pl [queue opts])] [--mode (1|2|3|4)]"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
[ -f path.sh ] && . ./path.sh;
|
||||
|
||||
oldlang=$1
|
||||
newlang=$2
|
||||
data=$3
|
||||
indir=$4
|
||||
outdir=$5
|
||||
|
||||
oldlm=$oldlang/G.fst
|
||||
newlm=$newlang/G.fst
|
||||
! cmp $oldlang/words.txt $newlang/words.txt && echo "Warning: vocabularies may be incompatible."
|
||||
[ ! -f $oldlm ] && echo Missing file $oldlm && exit 1;
|
||||
[ ! -f $newlm ] && echo Missing file $newlm && exit 1;
|
||||
! ls $indir/lat.*.gz >/dev/null && echo "No lattices input directory $indir" && exit 1;
|
||||
|
||||
oldlmcommand="fstproject --project_output=true $oldlm |"
|
||||
newlmcommand="fstproject --project_output=true $newlm |"
|
||||
|
||||
mkdir -p $outdir/log
|
||||
|
||||
phi=`grep -w '#0' $newlang/words.txt | awk '{print $2}'`
|
||||
|
||||
if [ "$mode" == 4 ]; then
|
||||
# we have to prepare $outdir/Ldet.fst in this case: determinized
|
||||
# lexicon (determinized on phones), with disambig syms removed.
|
||||
# take L_disambig.fst; get rid of transition with "#0 #0" on it; determinize
|
||||
# with epsilon removal; remove disambiguation symbols.
|
||||
fstprint $newlang/L_disambig.fst | awk '{if($4 != '$phi'){print;}}' | fstcompile | \
|
||||
fstdeterminizestar | fstrmsymbols $newlang/phones/disambig.int >$outdir/Ldet.fst || exit 1;
|
||||
fi
|
||||
|
||||
nj=`cat $indir/num_jobs` || exit 1;
|
||||
cp $indir/num_jobs $outdir
|
||||
|
||||
|
||||
#for lat in $indir/lat.*.gz; do
|
||||
# number=`basename $lat | cut -d. -f2`;
|
||||
# newlat=$outdir/`basename $lat`
|
||||
|
||||
case "$mode" in
|
||||
1) # 1 is inexact, it's the original way of doing it.
|
||||
$cmd JOB=1:$nj $outdir/log/rescorelm.JOB.log \
|
||||
lattice-lmrescore --lm-scale=-1.0 "ark:gunzip -c $indir/lat.JOB.gz|" "$oldlmcommand" ark:- \| \
|
||||
lattice-lmrescore --lm-scale=1.0 ark:- "$newlmcommand" "ark,t:|gzip -c>$outdir/lat.JOB.gz" \
|
||||
|| exit 1;
|
||||
;;
|
||||
2) # 2 is equivalent to 1, but using more basic operations, combined.
|
||||
$cmd JOB=1:$nj $outdir/log/rescorelm.JOB.log \
|
||||
gunzip -c $indir/lat.JOB.gz \| \
|
||||
lattice-scale --acoustic-scale=-1 --lm-scale=-1 ark:- ark:- \| \
|
||||
lattice-compose ark:- "fstproject --project_output=true $oldlm |" ark:- \| \
|
||||
lattice-determinize ark:- ark:- \| \
|
||||
lattice-scale --acoustic-scale=-1 --lm-scale=-1 ark:- ark:- \| \
|
||||
lattice-compose ark:- "fstproject --project_output=true $newlm |" ark:- \| \
|
||||
lattice-determinize ark:- ark:- \| \
|
||||
gzip -c \>$outdir/lat.JOB.gz || exit 1;
|
||||
;;
|
||||
3) # 3 is "exact" in that we remove the old LM scores accepting any path
|
||||
# through G.fst (which is what we want as that happened in lattice
|
||||
# generation), but we add the new one with "phi matcher", only taking
|
||||
# backoff arcs if an explicit arc did not exist.
|
||||
$cmd JOB=1:$nj $outdir/log/rescorelm.JOB.log \
|
||||
gunzip -c $indir/lat.JOB.gz \| \
|
||||
lattice-scale --acoustic-scale=-1 --lm-scale=-1 ark:- ark:- \| \
|
||||
lattice-compose ark:- "fstproject --project_output=true $oldlm |" ark:- \| \
|
||||
lattice-determinize ark:- ark:- \| \
|
||||
lattice-scale --acoustic-scale=-1 --lm-scale=-1 ark:- ark:- \| \
|
||||
lattice-compose --phi-label=$phi ark:- $newlm ark:- \| \
|
||||
lattice-determinize ark:- ark:- \| \
|
||||
gzip -c \>$outdir/lat.JOB.gz || exit 1;
|
||||
;;
|
||||
4) # 4 is also exact (like 3), but instead of subtracting the old LM-scores,
|
||||
# it removes the old graph scores entirely and adds in the lexicon,
|
||||
# grammar and transition weights.
|
||||
mdl=`dirname $indir`/final.mdl
|
||||
[ ! -f $mdl ] && echo No such model $mdl && exit 1;
|
||||
$cmd JOB=1:$nj $outdir/log/rescorelm.JOB.log \
|
||||
gunzip -c $indir/lat.JOB.gz \| \
|
||||
lattice-scale --lm-scale=0.0 ark:- ark:- \| \
|
||||
lattice-to-phone-lattice $mdl ark:- ark:- \| \
|
||||
lattice-compose ark:- $outdir/Ldet.fst ark:- \| \
|
||||
lattice-determinize ark:- ark:- \| \
|
||||
lattice-compose --phi-label=$phi ark:- $newlm ark:- \| \
|
||||
lattice-add-trans-probs --transition-scale=1.0 --self-loop-scale=0.1 \
|
||||
$mdl ark:- ark:- \| \
|
||||
gzip -c \>$outdir/lat.JOB.gz || exit 1;
|
||||
;;
|
||||
esac
|
||||
|
||||
rm $outdir/Ldet.fst 2>/dev/null
|
||||
|
||||
if ! $skip_scoring ; then
|
||||
[ ! -x local/score.sh ] && \
|
||||
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
|
||||
local/score.sh --cmd "$cmd" $data $newlang $outdir
|
||||
else
|
||||
echo "Not scoring because requested so..."
|
||||
fi
|
||||
|
||||
exit 0;
|
||||
|
|
@ -0,0 +1,117 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Copyright 2012 Karel Vesely, Daniel Povey
|
||||
# Apache 2.0
|
||||
# To be run from .. (one directory up from here)
|
||||
# see ../run.sh for example
|
||||
|
||||
# Begin configuration section.
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
remove_last_layers=4 # remove N last components from the nnet
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
if [ -f path.sh ]; then . ./path.sh; fi
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 5 ]; then
|
||||
echo "usage: $0 [options] <tgt-data-dir> <src-data-dir> <nnet-dir> <log-dir> <abs-path-to-bn-feat-dir>";
|
||||
echo "options: "
|
||||
echo " --trim-transforms <N> # number of NNet Components to remove from the end"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
if [ -f path.sh ]; then . path.sh; fi
|
||||
|
||||
data=$1
|
||||
srcdata=$2
|
||||
nndir=$3
|
||||
logdir=$4
|
||||
bnfeadir=$5
|
||||
|
||||
######## CONFIGURATION
|
||||
|
||||
# copy the dataset metadata from srcdata.
|
||||
mkdir -p $data || exit 1;
|
||||
cp $srcdata/* $data 2>/dev/null; rm $data/feats.scp $data/cmvn.scp;
|
||||
|
||||
# make $bnfeadir an absolute pathname.
|
||||
bnfeadir=`perl -e '($dir,$pwd)= @ARGV; if($dir!~m:^/:) { $dir = "$pwd/$dir"; } print $dir; ' $bnfeadir ${PWD}`
|
||||
|
||||
# use "name" as part of name of the archive.
|
||||
name=`basename $data`
|
||||
|
||||
mkdir -p $bnfeadir || exit 1;
|
||||
mkdir -p $data || exit 1;
|
||||
mkdir -p $logdir || exit 1;
|
||||
|
||||
|
||||
srcscp=$srcdata/feats.scp
|
||||
scp=$data/feats.scp
|
||||
|
||||
required="$srcscp $nndir/final.nnet"
|
||||
|
||||
for f in $required; do
|
||||
if [ ! -f $f ]; then
|
||||
echo "$0: no such file $f"
|
||||
exit 1;
|
||||
fi
|
||||
done
|
||||
|
||||
if [ ! -d $srcdata/split$nj -o $srcdata/split$nj -ot $srcdata/feats.scp ]; then
|
||||
utils/split_data.sh $srcdata $nj
|
||||
fi
|
||||
|
||||
|
||||
#cut the MLP
|
||||
nnet=$bnfeadir/feature_extractor.nnet
|
||||
copy-nnet --remove-last-layers=$remove_last_layers --binary=false $nndir/final.nnet $nnet 2>$logdir/feature_extractor.log
|
||||
|
||||
#get the feature transform
|
||||
feature_transform=$nndir/final.feature_transform
|
||||
|
||||
echo "Creating bn-feats into $data"
|
||||
|
||||
###
|
||||
### Prepare feature pipeline
|
||||
feats="ark,s,cs:copy-feats scp:$srcdata/split$nj/JOB/feats.scp ark:- |"
|
||||
# Optionally add cmvn
|
||||
if [ -f $nndir/norm_vars ]; then
|
||||
norm_vars=$(cat $nndir/norm_vars 2>/dev/null)
|
||||
feats="$feats apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$srcdata/utt2spk scp:$srcdata/cmvn.scp ark:- ark:- |"
|
||||
fi
|
||||
# Optionally add deltas
|
||||
if [ -f $nndir/delta_order ]; then
|
||||
delta_order=$(cat $nndir/delta_order)
|
||||
feats="$feats add-deltas --delta-order=$delta_order ark:- ark:- |"
|
||||
fi
|
||||
###
|
||||
###
|
||||
|
||||
#Run the forward pass
|
||||
$cmd JOB=1:$nj $logdir/make_bnfeats.JOB.log \
|
||||
nnet-forward --feature-transform=$feature_transform $nnet "$feats" \
|
||||
ark,scp:$bnfeadir/raw_bnfea_$name.JOB.ark,$bnfeadir/raw_bnfea_$name.JOB.scp \
|
||||
|| exit 1;
|
||||
|
||||
|
||||
N0=$(cat $srcdata/feats.scp | wc -l)
|
||||
N1=$(cat $bnfeadir/raw_bnfea_$name.*.scp | wc -l)
|
||||
if [[ "$N0" != "$N1" ]]; then
|
||||
echo "Error producing bnfea features for $name:"
|
||||
echo "Original feats : $N0 Bottleneck feats : $N1"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
# concatenate the .scp files together.
|
||||
for ((n=1; n<=nj; n++)); do
|
||||
cat $bnfeadir/raw_bnfea_$name.$n.scp >> $data/feats.scp
|
||||
done
|
||||
|
||||
|
||||
echo "Succeeded creating MLP-BN features for $name ($data)"
|
||||
|
||||
|
|
@ -0,0 +1,146 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# Create denominator lattices for MMI/MPE training.
|
||||
# Creates its output in $dir/lat.*.gz
|
||||
|
||||
# Begin configuration section.
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
sub_split=1
|
||||
beam=13.0
|
||||
lattice_beam=7.0
|
||||
acwt=0.1
|
||||
max_active=5000
|
||||
transform_dir=
|
||||
max_mem=20000000 # This will stop the processes getting too large.
|
||||
# This is in bytes, but not "real" bytes-- you have to multiply
|
||||
# by something like 5 or 10 to get real bytes (not sure why so large)
|
||||
num_threads=1
|
||||
parallel_opts=
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "Usage: steps/make_denlats.sh [options] <data-dir> <lang-dir> <src-dir> <exp-dir>"
|
||||
echo " e.g.: steps/make_denlats.sh data/train data/lang exp/tri1 exp/tri1_denlats"
|
||||
echo "Works for (delta|lda) features, and (with --transform-dir option) such features"
|
||||
echo " plus transforms."
|
||||
echo ""
|
||||
echo "Main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --sub-split <n-split> # e.g. 40; use this for "
|
||||
echo " # large databases so your jobs will be smaller and"
|
||||
echo " # will (individually) finish reasonably soon."
|
||||
echo " --transform-dir <transform-dir> # directory to find fMLLR transforms."
|
||||
echo " --num-threads <n> # number of threads per decoding job"
|
||||
echo " --parallel-opts <string> # if >1 thread, add this to 'cmd', e.g. -pe smp 6"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
dir=$4
|
||||
|
||||
sdata=$data/split$nj
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
thread_string=
|
||||
[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
|
||||
mkdir -p $dir
|
||||
|
||||
cp -r $lang $dir/
|
||||
|
||||
# Compute grammar FST which corresponds to unigram decoding graph.
|
||||
new_lang="$dir/"$(basename "$lang")
|
||||
echo "Making unigram grammar FST in $new_lang"
|
||||
cat $data/text | utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt | \
|
||||
awk '{for(n=2;n<=NF;n++){ printf("%s ", $n); } printf("\n"); }' | \
|
||||
utils/make_unigram_grammar.pl | fstcompile > $new_lang/G.fst \
|
||||
|| exit 1;
|
||||
|
||||
# mkgraph.sh expects a whole directory "lang", so put everything in one directory...
|
||||
# it gets L_disambig.fst and G.fst (among other things) from $dir/lang, and
|
||||
# final.mdl from $srcdir; the output HCLG.fst goes in $dir/graph.
|
||||
|
||||
echo "Compiling decoding graph in $dir/dengraph"
|
||||
if [ -s $dir/dengraph/HCLG.fst ] && [ $dir/dengraph/HCLG.fst -nt $srcdir/final.mdl ]; then
|
||||
echo "Graph $dir/dengraph/HCLG.fst already exists: skipping graph creation."
|
||||
else
|
||||
utils/mkgraph.sh $new_lang $srcdir $dir/dengraph || exit 1;
|
||||
fi
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "align_si.sh: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
cp $srcdir/final.mat $dir
|
||||
;;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
if [ ! -z "$transform_dir" ]; then # add transforms to features...
|
||||
echo "$0: using fMLLR transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
|
||||
[ "`cat $transform_dir/num_jobs`" -ne "$nj" ] \
|
||||
&& echo "$0: mismatch in number of jobs with $transform_dir" && exit 1;
|
||||
[ -f $srcdir/final.mat ] && ! cmp $transform_dir/final.mat $srcdir/final.mat && \
|
||||
echo "$0: LDA transforms differ between $srcdir and $transform_dir"
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
else
|
||||
if [ -f $srcdir/final.alimdl ]; then
|
||||
echo "$0: you seem to have a SAT system but you did not supply the --transform-dir option.";
|
||||
exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $sub_split -eq 1 ]; then
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode_den.JOB.log \
|
||||
gmm-latgen-faster$thread_string --beam=$beam --lattice-beam=$lattice_beam --acoustic-scale=$acwt \
|
||||
--max-mem=$max_mem --max-active=$max_active --word-symbol-table=$lang/words.txt $srcdir/final.mdl \
|
||||
$dir/dengraph/HCLG.fst "$feats" "ark:|gzip -c >$dir/lat.JOB.gz" || exit 1;
|
||||
else
|
||||
for n in `seq $nj`; do
|
||||
if [ -f $dir/.done.$n ] && [ $dir/.done.$n -nt $alidir/final.mdl ]; then
|
||||
echo "Not processing subset $n as already done (delete $dir/.done.$n if not)";
|
||||
else
|
||||
sdata2=$data/split$nj/$n/split$sub_split;
|
||||
if [ ! -d $sdata2 ] || [ $sdata2 -ot $sdata/$n/feats.scp ]; then
|
||||
split_data.sh --per-utt $sdata/$n $sub_split || exit 1;
|
||||
fi
|
||||
mkdir -p $dir/log/$n
|
||||
mkdir -p $dir/part
|
||||
feats_subset=`echo $feats | sed "s/trans.JOB/trans.$n/g" | sed s:JOB/:$n/split$sub_split/JOB/:g`
|
||||
$cmd $parallel_opts JOB=1:$sub_split $dir/log/$n/decode_den.JOB.log \
|
||||
gmm-latgen-faster$thread_string --beam=$beam --lattice-beam=$lattice_beam --acoustic-scale=$acwt \
|
||||
--max-mem=$max_mem --max-active=$max_active --word-symbol-table=$lang/words.txt $srcdir/final.mdl \
|
||||
$dir/dengraph/HCLG.fst "$feats_subset" "ark:|gzip -c >$dir/lat.$n.JOB.gz" || exit 1;
|
||||
echo Merging archives for data subset $n
|
||||
rm $dir/.error 2>/dev/null;
|
||||
for k in `seq $sub_split`; do
|
||||
gunzip -c $dir/lat.$n.$k.gz || touch $dir/.error;
|
||||
done | gzip -c > $dir/lat.$n.gz || touch $dir/.error;
|
||||
[ -f $dir/.error ] && echo Merging lattices for subset $n failed && exit 1;
|
||||
rm $dir/lat.$n.*.gz
|
||||
touch $dir/.done.$n
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
||||
|
||||
echo "$0: done generating denominator lattices."
|
||||
|
|
@ -0,0 +1,177 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012-2013 Karel Vesely, Daniel Povey
|
||||
# Apache 2.0.
|
||||
|
||||
# Create denominator lattices for MMI/MPE/sMBR training.
|
||||
# Creates its output in $dir/lat.*.ark,$dir/lat.scp
|
||||
# The lattices are uncompressed, we need random access for DNN training.
|
||||
|
||||
# Begin configuration section.
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
sub_split=1
|
||||
beam=13.0
|
||||
lattice_beam=7.0
|
||||
acwt=0.1
|
||||
max_active=5000
|
||||
nnet=
|
||||
max_mem=20000000 # This will stop the processes getting too large.
|
||||
# This is in bytes, but not "real" bytes-- you have to multiply
|
||||
# by something like 5 or 10 to get real bytes (not sure why so large)
|
||||
# End configuration section.
|
||||
use_gpu_id=-1 # disable gpu
|
||||
parallel_opts="-pe smp 2"
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "Usage: steps/$0 [options] <data-dir> <lang-dir> <src-dir> <exp-dir>"
|
||||
echo " e.g.: steps/$0 data/train data/lang exp/tri1 exp/tri1_denlats"
|
||||
echo "Works for plain features (or CMN, delta), forwarded through feature-transform."
|
||||
echo ""
|
||||
echo "Main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --sub-split <n-split> # e.g. 40; use this for "
|
||||
echo " # large databases so your jobs will be smaller and"
|
||||
echo " # will (individually) finish reasonably soon."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
dir=$4
|
||||
|
||||
sdata=$data/split$nj
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
|
||||
mkdir -p $dir
|
||||
|
||||
cp -r $lang $dir/
|
||||
|
||||
# Compute grammar FST which corresponds to unigram decoding graph.
|
||||
new_lang="$dir/"$(basename "$lang")
|
||||
echo "Making unigram grammar FST in $new_lang"
|
||||
cat $data/text | utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt | \
|
||||
awk '{for(n=2;n<=NF;n++){ printf("%s ", $n); } printf("\n"); }' | \
|
||||
utils/make_unigram_grammar.pl | fstcompile > $new_lang/G.fst \
|
||||
|| exit 1;
|
||||
|
||||
# mkgraph.sh expects a whole directory "lang", so put everything in one directory...
|
||||
# it gets L_disambig.fst and G.fst (among other things) from $dir/lang, and
|
||||
# final.mdl from $srcdir; the output HCLG.fst goes in $dir/graph.
|
||||
|
||||
echo "Compiling decoding graph in $dir/dengraph"
|
||||
if [ -s $dir/dengraph/HCLG.fst ] && [ $dir/dengraph/HCLG.fst -nt $srcdir/final.mdl ]; then
|
||||
echo "Graph $dir/dengraph/HCLG.fst already exists: skipping graph creation."
|
||||
else
|
||||
utils/mkgraph.sh $new_lang $srcdir $dir/dengraph || exit 1;
|
||||
fi
|
||||
|
||||
|
||||
|
||||
#Get the files we will need
|
||||
cp $srcdir/{tree,final.mdl} $dir
|
||||
|
||||
[ -z "$nnet" ] && nnet=$srcdir/final.nnet;
|
||||
[ ! -f "$nnet" ] && echo "Error nnet '$nnet' does not exist!" && exit 1;
|
||||
|
||||
class_frame_counts=$srcdir/ali_train_pdf.counts
|
||||
[ -z "$class_frame_counts" ] && echo "Error class_frame_counts '$class_frame_counts' does not exist!" && exit 1;
|
||||
|
||||
feature_transform=$srcdir/final.feature_transform
|
||||
if [ ! -f $feature_transform ]; then
|
||||
echo "Missing feature_transform '$feature_transform'"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
model=$dir/final.mdl
|
||||
[ -z "$model" ] && echo "Error transition model '$model' does not exist!" && exit 1;
|
||||
|
||||
###
|
||||
### Prepare feature pipeline (same as for decoding)
|
||||
###
|
||||
# Create the feature stream:
|
||||
feats="ark,s,cs:copy-feats scp:$sdata/JOB/feats.scp ark:- |"
|
||||
# Optionally add cmvn
|
||||
if [ -f $srcdir/norm_vars ]; then
|
||||
norm_vars=$(cat $srcdir/norm_vars 2>/dev/null)
|
||||
[ ! -f $sdata/1/cmvn.scp ] && echo "$0: cannot find cmvn stats $sdata/1/cmvn.scp" && exit 1
|
||||
feats="$feats apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp ark:- ark:- |"
|
||||
fi
|
||||
# Optionally add deltas
|
||||
if [ -f $srcdir/delta_order ]; then
|
||||
delta_order=$(cat $srcdir/delta_order)
|
||||
feats="$feats add-deltas --delta-order=$delta_order ark:- ark:- |"
|
||||
fi
|
||||
|
||||
# Finally add feature_transform and the MLP
|
||||
feats="$feats nnet-forward --feature-transform=$feature_transform --no-softmax=true --class-frame-counts=$class_frame_counts --use-gpu-id=$use_gpu_id $nnet ark:- ark:- |"
|
||||
###
|
||||
###
|
||||
###
|
||||
|
||||
|
||||
|
||||
###
|
||||
### We will produce lattices, where the correct path is not necessarily present
|
||||
###
|
||||
|
||||
#1) We don't use reference path here...
|
||||
|
||||
echo "Generating the denlats"
|
||||
#2) Generate the denominator lattices
|
||||
if [ $sub_split -eq 1 ]; then
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode_den.JOB.log \
|
||||
latgen-faster-mapped --beam=$beam --lattice-beam=$lattice_beam --acoustic-scale=$acwt \
|
||||
--max-mem=$max_mem --max-active=$max_active --word-symbol-table=$lang/words.txt $srcdir/final.mdl \
|
||||
$dir/dengraph/HCLG.fst "$feats" "ark,scp:$dir/lat.JOB.ark,$dir/lat.JOB.scp" || exit 1;
|
||||
else
|
||||
for n in `seq $nj`; do
|
||||
if [ -f $dir/.done.$n ] && [ $dir/.done.$n -nt $alidir/final.mdl ]; then
|
||||
echo "Not processing subset $n as already done (delete $dir/.done.$n if not)";
|
||||
else
|
||||
sdata2=$data/split$nj/$n/split$sub_split;
|
||||
if [ ! -d $sdata2 ] || [ $sdata2 -ot $sdata/$n/feats.scp ]; then
|
||||
split_data.sh --per-utt $sdata/$n $sub_split || exit 1;
|
||||
fi
|
||||
mkdir -p $dir/log/$n
|
||||
mkdir -p $dir/part
|
||||
feats_subset=$(echo $feats | sed s:JOB/:$n/split$sub_split/JOB/:g)
|
||||
$cmd $parallel_opts JOB=1:$sub_split $dir/log/$n/decode_den.JOB.log \
|
||||
latgen-faster-mapped --beam=$beam --lattice-beam=$lattice_beam --acoustic-scale=$acwt \
|
||||
--max-mem=$max_mem --max-active=$max_active --word-symbol-table=$lang/words.txt $srcdir/final.mdl \
|
||||
$dir/dengraph/HCLG.fst "$feats_subset" "ark,scp:$dir/lat.$n.JOB.ark,$dir/lat.$n.JOB.scp" || exit 1;
|
||||
echo Merging lists for data subset $n
|
||||
for k in `seq $sub_split`; do
|
||||
cat $dir/lat.$n.$k.scp
|
||||
done > $dir/lat.$n.all.scp
|
||||
echo Merge the ark $n
|
||||
lattice-copy scp:$dir/lat.$n.all.scp ark,scp:$dir/lat.$n.ark,$dir/lat.$n.scp || exit 1;
|
||||
#remove the data
|
||||
rm $dir/lat.$n.*.ark $dir/lat.$n.*.scp $dir/lat.$n.all.scp
|
||||
touch $dir/.done.$n
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
||||
|
||||
|
||||
#3) Merge the SCPs to create full list of lattices (will use random access)
|
||||
echo Merging to single list $dir/lat.scp
|
||||
for ((n=1; n<=nj; n++)); do
|
||||
cat $dir/lat.$n.scp
|
||||
done > $dir/lat.scp
|
||||
|
||||
|
||||
echo "$0: done generating denominator lattices."
|
||||
|
|
@ -0,0 +1,146 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# Create denominator lattices for MMI/MPE training.
|
||||
# Creates its output in $dir/lat.*.gz
|
||||
|
||||
# Begin configuration section.
|
||||
transform_dir= # dir to find fMLLR transforms.
|
||||
nj=4 # number of decoding jobs. If --transform-dir set, must match that number!
|
||||
cmd=run.pl
|
||||
sub_split=1
|
||||
beam=13.0
|
||||
lattice_beam=7.0
|
||||
acwt=0.1
|
||||
max_active=5000
|
||||
transform_dir=
|
||||
max_mem=20000000 # This will stop the processes getting too large.
|
||||
# This is in bytes, but not "real" bytes-- you have to multiply
|
||||
# by something like 5 or 10 to get real bytes (not sure why so large)
|
||||
# End configuration section.
|
||||
num_threads=1 # Number of threads used in nnet-logprob computation. If you set
|
||||
# this to a different value, make sure to also set the appropriate
|
||||
# queue options. If you set this too high it won't use all the
|
||||
# threads as most of the time will be taken in the decoder.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "Usage: steps/make_denlats_nnet_cpu.sh [options] <data-dir> <lang-dir> <src-dir> <exp-dir>"
|
||||
echo " e.g.: steps/make_denlats_nnet_cpu.sh data/train data/lang exp/tri1 exp/tri1_denlats"
|
||||
echo "Works for (delta|lda) features, and (with --transform-dir option) such features"
|
||||
echo " plus transforms."
|
||||
echo ""
|
||||
echo "Main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --sub-split <n-split> # e.g. 40; use this for "
|
||||
echo " # large databases so your jobs will be smaller and"
|
||||
echo " # will (individually) finish reasonably soon."
|
||||
echo " --transform-dir <transform-dir> # directory to find fMLLR transforms."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
srcdir=$3
|
||||
dir=$4
|
||||
|
||||
sdata=$data/split$nj
|
||||
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
|
||||
mkdir -p $dir
|
||||
|
||||
cp -r $lang $dir/
|
||||
|
||||
# Compute grammar FST which corresponds to unigram decoding graph.
|
||||
new_lang="$dir/"$(basename "$lang")
|
||||
echo "Making unigram grammar FST in $new_lang"
|
||||
cat $data/text | utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt | \
|
||||
awk '{for(n=2;n<=NF;n++){ printf("%s ", $n); } printf("\n"); }' | \
|
||||
utils/make_unigram_grammar.pl | fstcompile > $new_lang/G.fst \
|
||||
|| exit 1;
|
||||
|
||||
# mkgraph.sh expects a whole directory "lang", so put everything in one directory...
|
||||
# it gets L_disambig.fst and G.fst (among other things) from $dir/lang, and
|
||||
# final.mdl from $srcdir; the output HCLG.fst goes in $dir/graph.
|
||||
|
||||
echo "Compiling decoding graph in $dir/dengraph"
|
||||
if [ -s $dir/dengraph/HCLG.fst ] && [ $dir/dengraph/HCLG.fst -nt $srcdir/final.mdl ]; then
|
||||
echo "Graph $dir/dengraph/HCLG.fst already exists: skipping graph creation."
|
||||
else
|
||||
utils/mkgraph.sh $new_lang $srcdir $dir/dengraph || exit 1;
|
||||
fi
|
||||
|
||||
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "align_si.sh: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
|
||||
cp $srcdir/final.mat $dir
|
||||
;;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
if [ ! -z "$transform_dir" ]; then # add transforms to features...
|
||||
echo "$0: using fMLLR transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
|
||||
[ "`cat $transform_dir/num_jobs`" -ne "$nj" ] \
|
||||
&& echo "$0: mismatch in number of jobs with $transform_dir" && exit 1;
|
||||
[ -f $srcdir/final.mat ] && ! cmp $transform_dir/final.mat $srcdir/final.mat && \
|
||||
echo "$0: LDA transforms differ between $srcdir and $transform_dir"
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
else
|
||||
if [ -f $srcdir/final.alimdl ]; then
|
||||
echo "$0: you seem to have a SAT system but you did not supply the --transform-dir option.";
|
||||
exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $sub_split -eq 1 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/decode_den.JOB.log \
|
||||
nnet-logprob-parallel --num-threads=$num_threads $srcdir/final.mdl "$feats" ark:- \| \
|
||||
latgen-faster-mapped --beam=$beam --lattice-beam=$lattice_beam --acoustic-scale=$acwt \
|
||||
--max-mem=$max_mem --max-active=$max_active --word-symbol-table=$lang/words.txt $srcdir/final.mdl \
|
||||
$dir/dengraph/HCLG.fst ark:- "ark:|gzip -c >$dir/lat.JOB.gz" || exit 1;
|
||||
else
|
||||
for n in `seq $nj`; do
|
||||
if [ -f $dir/.done.$n ] && [ $dir/.done.$n -nt $alidir/final.mdl ]; then
|
||||
echo "Not processing subset $n as already done (delete $dir/.done.$n if not)";
|
||||
else
|
||||
sdata2=$data/split$nj/$n/split$sub_split;
|
||||
if [ ! -d $sdata2 ] || [ $sdata2 -ot $sdata/$n/feats.scp ]; then
|
||||
split_data.sh --per-utt $sdata/$n $sub_split || exit 1;
|
||||
fi
|
||||
mkdir -p $dir/log/$n
|
||||
mkdir -p $dir/part
|
||||
feats_subset=`echo $feats | sed "s/trans.JOB/trans.$n/g" | sed s:JOB/:$n/split$sub_split/JOB/:g`
|
||||
$cmd JOB=1:$sub_split $dir/log/$n/decode_den.JOB.log \
|
||||
nnet-logprob-parallel --num-threads=$num_threads $srcdir/final.mdl "$feats_subset" ark:- \| \
|
||||
latgen-faster-mapped --beam=$beam --lattice-beam=$lattice_beam --acoustic-scale=$acwt \
|
||||
--max-mem=$max_mem --max-active=$max_active --word-symbol-table=$lang/words.txt $srcdir/final.mdl \
|
||||
$dir/dengraph/HCLG.fst ark:- "ark:|gzip -c >$dir/lat.$n.JOB.gz" || exit 1;
|
||||
echo Merging archives for data subset $n
|
||||
rm $dir/.error 2>/dev/null;
|
||||
for k in `seq $sub_split`; do
|
||||
gunzip -c $dir/lat.$n.$k.gz || touch $dir/.error;
|
||||
done | gzip -c > $dir/lat.$n.gz || touch $dir/.error;
|
||||
[ -f $dir/.error ] && echo Merging lattices for subset $n failed && exit 1;
|
||||
rm $dir/lat.$n.*.gz
|
||||
touch $dir/.done.$n
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
||||
|
||||
echo "$0: done generating denominator lattices."
|
||||
|
|
@ -0,0 +1,159 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# Create denominator lattices for MMI/MPE training, with SGMM models. If the
|
||||
# features have fMLLR transforms you have to supply the --transform-dir option.
|
||||
# It gets any speaker vectors from the "alignment dir" ($alidir). Note: this is
|
||||
# possibly a slight mismatch because the speaker vectors come from supervised
|
||||
# adaptation.
|
||||
|
||||
# Begin configuration section.
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
sub_split=1
|
||||
beam=13.0
|
||||
lattice_beam=7.0
|
||||
acwt=0.1
|
||||
max_active=5000
|
||||
transform_dir=
|
||||
max_mem=20000000 # This will stop the processes getting too large.
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "Usage: steps/make_denlats_sgmm.sh [options] <data-dir> <lang-dir> <src-dir|alidir> <exp-dir>"
|
||||
echo " e.g.: steps/make_denlats_sgmm.sh data/train data/lang exp/sgmm4a_ali exp/sgmm4a_denlats"
|
||||
echo "Works for (delta|lda) features, and (with --transform-dir option) such features"
|
||||
echo " plus transforms."
|
||||
echo ""
|
||||
echo "Main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --sub-split <n-split> # e.g. 40; use this for "
|
||||
echo " # large databases so your jobs will be smaller and"
|
||||
echo " # will (individually) finish reasonably soon."
|
||||
echo " --transform-dir <transform-dir> # directory to find fMLLR transforms."
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
alidir=$3 # could also be $srcdir, but only if no vectors supplied.
|
||||
dir=$4
|
||||
|
||||
sdata=$data/split$nj
|
||||
splice_opts=`cat $alidir/splice_opts 2>/dev/null`
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
|
||||
mkdir -p $dir
|
||||
|
||||
cp -r $lang $dir/
|
||||
|
||||
# Compute grammar FST which corresponds to unigram decoding graph.
|
||||
new_lang="$dir/"$(basename "$lang")
|
||||
echo "Making unigram grammar FST in $new_lang"
|
||||
cat $data/text | utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt | \
|
||||
awk '{for(n=2;n<=NF;n++){ printf("%s ", $n); } printf("\n"); }' | \
|
||||
utils/make_unigram_grammar.pl | fstcompile > $new_lang/G.fst \
|
||||
|| exit 1;
|
||||
|
||||
# mkgraph.sh expects a whole directory "lang", so put everything in one directory...
|
||||
# it gets L_disambig.fst and G.fst (among other things) from $dir/lang, and
|
||||
# final.mdl from $alidir; the output HCLG.fst goes in $dir/graph.
|
||||
|
||||
echo "Compiling decoding graph in $dir/dengraph"
|
||||
if [ -s $dir/dengraph/HCLG.fst ] && [ $dir/dengraph/HCLG.fst -nt $srcdir/final.mdl ]; then
|
||||
echo "Graph $dir/dengraph/HCLG.fst already exists: skipping graph creation."
|
||||
else
|
||||
utils/mkgraph.sh $new_lang $alidir $dir/dengraph || exit 1;
|
||||
fi
|
||||
|
||||
if [ -f $alidir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "align_si.sh: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $alidir/final.mat ark:- ark:- |"
|
||||
cp $alidir/final.mat $dir
|
||||
;;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
if [ ! -z "$transform_dir" ]; then # add transforms to features...
|
||||
echo "$0: using fMLLR transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
|
||||
[ "`cat $transform_dir/num_jobs`" -ne "$nj" ] \
|
||||
&& echo "$0: mismatch in number of jobs with $transform_dir" && exit 1;
|
||||
[ -f $alidir/final.mat ] && ! cmp $transform_dir/final.mat $alidir/final.mat && \
|
||||
echo "$0: LDA transforms differ between $alidir and $transform_dir"
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
else
|
||||
echo "Assuming you don't have a SAT system, since no --transform-dir option supplied "
|
||||
fi
|
||||
|
||||
if [ -f $alidir/gselect.1.gz ]; then
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $alidir/gselect.JOB.gz|"
|
||||
else
|
||||
echo "$0: no such file $alidir/gselect.1.gz" && exit 1;
|
||||
fi
|
||||
|
||||
if [ -f $alidir/vecs.1 ]; then
|
||||
spkvecs_opt="--spk-vecs=ark:$alidir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk"
|
||||
else
|
||||
if [ -f $alidir/final.alimdl ]; then
|
||||
echo "You seem to have an SGMM system with speaker vectors,"
|
||||
echo "yet we can't find speaker vectors. Perhaps you supplied"
|
||||
echo "the model director instead of the alignment directory?"
|
||||
exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $sub_split -eq 1 ]; then
|
||||
$cmd JOB=1:$nj $dir/log/decode_den.JOB.log \
|
||||
sgmm-latgen-faster $spkvecs_opt "$gselect_opt" --beam=$beam \
|
||||
--lattice-beam=$lattice_beam --acoustic-scale=$acwt \
|
||||
--max-mem=$max_mem --max-active=$max_active --word-symbol-table=$lang/words.txt $alidir/final.mdl \
|
||||
$dir/dengraph/HCLG.fst "$feats" "ark:|gzip -c >$dir/lat.JOB.gz" || exit 1;
|
||||
else
|
||||
for n in `seq $nj`; do
|
||||
if [ -f $dir/.done.$n ] && [ $dir/.done.$n -nt $alidir/final.mdl ]; then
|
||||
echo "Not processing subset $n as already done (delete $dir/.done.$n if not)";
|
||||
else
|
||||
sdata2=$data/split$nj/$n/split$sub_split;
|
||||
if [ ! -d $sdata2 ] || [ $sdata2 -ot $sdata/$n/feats.scp ]; then
|
||||
split_data.sh --per-utt $sdata/$n $sub_split || exit 1;
|
||||
fi
|
||||
mkdir -p $dir/log/$n
|
||||
mkdir -p $dir/part
|
||||
feats_subset=`echo $feats | sed "s/trans.JOB/trans.$n/g" | sed s:JOB/:$n/split$sub_split/JOB/:g`
|
||||
spkvecs_opt_subset=`echo $spkvecs_opt | sed "s/JOB/$n/g"`
|
||||
gselect_opt_subset=`echo $gselect_opt | sed "s/JOB/$n/g"`
|
||||
$cmd JOB=1:$sub_split $dir/log/$n/decode_den.JOB.log \
|
||||
sgmm-latgen-faster $spkvecs_opt_subset "$gselect_opt_subset" \
|
||||
--beam=$beam --lattice-beam=$lattice_beam \
|
||||
--acoustic-scale=$acwt --max-mem=$max_mem --max-active=$max_active \
|
||||
--word-symbol-table=$lang/words.txt $alidir/final.mdl \
|
||||
$dir/dengraph/HCLG.fst "$feats_subset" "ark:|gzip -c >$dir/lat.$n.JOB.gz" || exit 1;
|
||||
echo Merging archives for data subset $n
|
||||
rm $dir/.error 2>/dev/null;
|
||||
for k in `seq $sub_split`; do
|
||||
gunzip -c $dir/lat.$n.$k.gz || touch $dir/.error;
|
||||
done | gzip -c > $dir/lat.$n.gz || touch $dir/.error;
|
||||
[ -f $dir/.error ] && echo Merging lattices for subset $n failed && exit 1;
|
||||
rm $dir/lat.$n.*.gz
|
||||
touch $dir/.done.$n
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
||||
|
||||
echo "$0: done generating denominator lattices with SGMMs."
|
||||
|
|
@ -0,0 +1,170 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
|
||||
|
||||
# Create denominator lattices for MMI/MPE training, with SGMM models. If the
|
||||
# features have fMLLR transforms you have to supply the --transform-dir option.
|
||||
# It gets any speaker vectors from the "alignment dir" ($alidir). Note: this is
|
||||
# possibly a slight mismatch because the speaker vectors come from supervised
|
||||
# adaptation.
|
||||
|
||||
# Begin configuration section.
|
||||
nj=4
|
||||
cmd=run.pl
|
||||
sub_split=1
|
||||
beam=13.0
|
||||
lattice_beam=7.0
|
||||
acwt=0.1
|
||||
max_active=5000
|
||||
transform_dir=
|
||||
max_mem=20000000 # This will stop the processes getting too large.
|
||||
num_threads=1
|
||||
parallel_opts=
|
||||
# End configuration section.
|
||||
|
||||
echo "$0 $@" # Print the command line for logging
|
||||
|
||||
[ -f ./path.sh ] && . ./path.sh; # source the path.
|
||||
. parse_options.sh || exit 1;
|
||||
|
||||
if [ $# != 4 ]; then
|
||||
echo "Usage: steps/make_denlats_sgmm2.sh [options] <data-dir> <lang-dir> <src-dir|alidir> <exp-dir>"
|
||||
echo " e.g.: steps/make_denlats_sgmm2.sh data/train data/lang exp/sgmm4a_ali exp/sgmm4a_denlats"
|
||||
echo "Works for (delta|lda) features, and (with --transform-dir option) such features"
|
||||
echo " plus transforms."
|
||||
echo ""
|
||||
echo "Main options (for others, see top of script file)"
|
||||
echo " --config <config-file> # config containing options"
|
||||
echo " --nj <nj> # number of parallel jobs"
|
||||
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
|
||||
echo " --sub-split <n-split> # e.g. 40; use this for "
|
||||
echo " # large databases so your jobs will be smaller and"
|
||||
echo " # will (individually) finish reasonably soon."
|
||||
echo " --transform-dir <transform-dir> # directory to find fMLLR transforms."
|
||||
echo " --num-threads <n> # number of threads per decoding job"
|
||||
echo " --parallel-opts <string> # if >1 thread, add this to 'cmd', e.g. -pe smp 6"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
data=$1
|
||||
lang=$2
|
||||
alidir=$3 # could also be $srcdir, but only if no vectors supplied.
|
||||
dir=$4
|
||||
|
||||
sdata=$data/split$nj
|
||||
splice_opts=`cat $alidir/splice_opts 2>/dev/null`
|
||||
if [ $num_threads -gt 1 ]; then
|
||||
# the -parallel becomes part of the binary name we decode with.
|
||||
thread_string="-parallel --num-threads=$num_threads"
|
||||
fi
|
||||
|
||||
mkdir -p $dir/log
|
||||
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
|
||||
echo $nj > $dir/num_jobs
|
||||
|
||||
oov=`cat $lang/oov.int` || exit 1;
|
||||
|
||||
mkdir -p $dir
|
||||
|
||||
cp -r $lang $dir/
|
||||
|
||||
# Compute grammar FST which corresponds to unigram decoding graph.
|
||||
new_lang="$dir/"$(basename "$lang")
|
||||
echo "Making unigram grammar FST in $new_lang"
|
||||
cat $data/text | utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt | \
|
||||
awk '{for(n=2;n<=NF;n++){ printf("%s ", $n); } printf("\n"); }' | \
|
||||
utils/make_unigram_grammar.pl | fstcompile > $new_lang/G.fst \
|
||||
|| exit 1;
|
||||
|
||||
# mkgraph.sh expects a whole directory "lang", so put everything in one directory...
|
||||
# it gets L_disambig.fst and G.fst (among other things) from $dir/lang, and
|
||||
# final.mdl from $alidir; the output HCLG.fst goes in $dir/graph.
|
||||
|
||||
echo "Compiling decoding graph in $dir/dengraph"
|
||||
if [ -s $dir/dengraph/HCLG.fst ] && [ $dir/dengraph/HCLG.fst -nt $srcdir/final.mdl ]; then
|
||||
echo "Graph $dir/dengraph/HCLG.fst already exists: skipping graph creation."
|
||||
else
|
||||
utils/mkgraph.sh $new_lang $alidir $dir/dengraph || exit 1;
|
||||
fi
|
||||
|
||||
if [ -f $alidir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
|
||||
echo "align_si.sh: feature type is $feat_type"
|
||||
|
||||
case $feat_type in
|
||||
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
|
||||
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $alidir/final.mat ark:- ark:- |"
|
||||
cp $alidir/final.mat $dir
|
||||
;;
|
||||
*) echo "Invalid feature type $feat_type" && exit 1;
|
||||
esac
|
||||
|
||||
if [ ! -z "$transform_dir" ]; then # add transforms to features...
|
||||
echo "$0: using fMLLR transforms from $transform_dir"
|
||||
[ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
|
||||
[ "`cat $transform_dir/num_jobs`" -ne "$nj" ] \
|
||||
&& echo "$0: mismatch in number of jobs with $transform_dir" && exit 1;
|
||||
[ -f $alidir/final.mat ] && ! cmp $transform_dir/final.mat $alidir/final.mat && \
|
||||
echo "$0: LDA transforms differ between $alidir and $transform_dir"
|
||||
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
|
||||
else
|
||||
echo "Assuming you don't have a SAT system, since no --transform-dir option supplied "
|
||||
fi
|
||||
|
||||
if [ -f $alidir/gselect.1.gz ]; then
|
||||
gselect_opt="--gselect=ark,s,cs:gunzip -c $alidir/gselect.JOB.gz|"
|
||||
else
|
||||
echo "$0: no such file $alidir/gselect.1.gz" && exit 1;
|
||||
fi
|
||||
|
||||
if [ -f $alidir/vecs.1 ]; then
|
||||
spkvecs_opt="--spk-vecs=ark:$alidir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk"
|
||||
[ "`cat $alidir/num_jobs`" -ne "$nj" ] \
|
||||
&& echo "$0: mismatch in number of jobs with $alidir" && exit 1;
|
||||
else
|
||||
if [ -f $alidir/final.alimdl ]; then
|
||||
echo "$0: You seem to have an SGMM system with speaker vectors,"
|
||||
echo "yet we can't find speaker vectors. Perhaps you supplied"
|
||||
echo "the model director instead of the alignment directory?"
|
||||
exit 1;
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $sub_split -eq 1 ]; then
|
||||
$cmd $parallel_opts JOB=1:$nj $dir/log/decode_den.JOB.log \
|
||||
sgmm2-latgen-faster$thread_string $spkvecs_opt "$gselect_opt" --beam=$beam \
|
||||
--lattice-beam=$lattice_beam --acoustic-scale=$acwt \
|
||||
--max-mem=$max_mem --max-active=$max_active --word-symbol-table=$lang/words.txt $alidir/final.mdl \
|
||||
$dir/dengraph/HCLG.fst "$feats" "ark:|gzip -c >$dir/lat.JOB.gz" || exit 1;
|
||||
else
|
||||
for n in `seq $nj`; do
|
||||
if [ -f $dir/.done.$n ] && [ $dir/.done.$n -nt $alidir/final.mdl ]; then
|
||||
echo "Not processing subset $n as already done (delete $dir/.done.$n if not)";
|
||||
else
|
||||
sdata2=$data/split$nj/$n/split$sub_split;
|
||||
if [ ! -d $sdata2 ] || [ $sdata2 -ot $sdata/$n/feats.scp ]; then
|
||||
split_data.sh --per-utt $sdata/$n $sub_split || exit 1;
|
||||
fi
|
||||
mkdir -p $dir/log/$n
|
||||
mkdir -p $dir/part
|
||||
feats_subset=`echo $feats | sed "s/trans.JOB/trans.$n/g" | sed s:JOB/:$n/split$sub_split/JOB/:g`
|
||||
spkvecs_opt_subset=`echo $spkvecs_opt | sed "s/JOB/$n/g"`
|
||||
gselect_opt_subset=`echo $gselect_opt | sed "s/JOB/$n/g"`
|
||||
$cmd $parallel_opts JOB=1:$sub_split $dir/log/$n/decode_den.JOB.log \
|
||||
sgmm2-latgen-faster$thread_string $spkvecs_opt_subset "$gselect_opt_subset" \
|
||||
--beam=$beam --lattice-beam=$lattice_beam \
|
||||
--acoustic-scale=$acwt --max-mem=$max_mem --max-active=$max_active \
|
||||
--word-symbol-table=$lang/words.txt $alidir/final.mdl \
|
||||
$dir/dengraph/HCLG.fst "$feats_subset" "ark:|gzip -c >$dir/lat.$n.JOB.gz" || exit 1;
|
||||
echo Merging archives for data subset $n
|
||||
rm $dir/.error 2>/dev/null;
|
||||
for k in `seq $sub_split`; do
|
||||
gunzip -c $dir/lat.$n.$k.gz || touch $dir/.error;
|
||||
done | gzip -c > $dir/lat.$n.gz || touch $dir/.error;
|
||||
[ -f $dir/.error ] && echo Merging lattices for subset $n failed && exit 1;
|
||||
rm $dir/lat.$n.*.gz
|
||||
touch $dir/.done.$n
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
||||
|
||||
echo "$0: done generating denominator lattices with SGMMs."
|
||||
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