зеркало из https://github.com/mozilla/kaldi.git
trunk: various script extensions relating to vad.scp; extensions to gmm-global-init-from-feats.cc for UBM initialization (multi-threaded operation; mixture splitting enabled)
git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@3229 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8
This commit is contained in:
Dan Povey
Родитель
38c0e03b4b
Коммит
feefd031ab
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@ -43,7 +43,7 @@ texts=""
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nu=`cat $data/utt2spk | wc -l`
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nf=`cat $data/feats.scp | wc -l`
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nt=`cat $data/text | wc -l`
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nt=`cat $data/text 2>/dev/null | wc -l` # take it as zero if no such file
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if [ $nu -ne $nf ]; then
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echo "split_data.sh: warning, #lines is (utt2spk,feats.scp) is ($nu,$nf); this script "
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echo " may produce incorrectly split data."
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@ -61,7 +61,7 @@ if [ ! -d $s1 ]; then
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else
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need_to_split=false
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for f in utt2spk spk2utt feats.scp text wav.scp cmvn.scp spk2gender \
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segments reco2file_and_channel; do
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vad.scp segments reco2file_and_channel; do
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if [[ -f $data/$f && ( ! -f $s1/$f || $s1/$f -ot $data/$f ) ]]; then
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need_to_split=true
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fi
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@ -75,6 +75,7 @@ fi
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for n in `seq $numsplit`; do
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mkdir -p $data/split$numsplit/$n
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feats="$feats $data/split$numsplit/$n/feats.scp"
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vads="$vads $data/split$numsplit/$n/vad.scp"
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texts="$texts $data/split$numsplit/$n/text"
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utt2spks="$utt2spks $data/split$numsplit/$n/utt2spk"
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done
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@ -88,8 +89,10 @@ fi
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utils/split_scp.pl $utt2spk_opt $data/utt2spk $utt2spks || exit 1
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utils/split_scp.pl $utt2spk_opt $data/feats.scp $feats || exit 1
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[ -f $data/text ] && \
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utils/split_scp.pl $utt2spk_opt $data/text $texts
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[ -f $data/text ] && utils/split_scp.pl $utt2spk_opt $data/text $texts
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[ -f $data/vad.scp ] && utils/split_scp.pl $utt2spk_opt $data/vad.scp $vads
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# If lockfile is not installed, just don't lock it. It's not a big deal.
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which lockfile >&/dev/null && lockfile -l 60 $data/.split_lock
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@ -1,5 +1,6 @@
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#!/bin/bash
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# Copyright 2010-2012 Microsoft Corporation Johns Hopkins University (Author: Daniel Povey)
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# Copyright 2010-2011 Microsoft Corporation
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# 2012-2013 Johns Hopkins University (Author: Daniel Povey)
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# Apache 2.0
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@ -89,6 +90,7 @@ fi
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function do_filtering {
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# assumes the utt2spk and spk2utt files already exist.
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[ -f $srcdir/feats.scp ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/feats.scp >$destdir/feats.scp
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[ -f $srcdir/vad.scp ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/vad.scp >$destdir/vad.scp
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[ -f $srcdir/wav.scp ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/wav.scp >$destdir/wav.scp
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[ -f $srcdir/text ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/text >$destdir/text
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[ -f $srcdir/spk2gender ] && utils/filter_scp.pl $destdir/spk2utt <$srcdir/spk2gender >$destdir/spk2gender
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@ -14,8 +14,8 @@ OBJFILES = diag-gmm.o diag-gmm-normal.o mle-diag-gmm.o am-diag-gmm.o \
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LIBNAME = kaldi-gmm
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ADDLIBS = ../tree/kaldi-tree.a ../util/kaldi-util.a ../matrix/kaldi-matrix.a \
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../base/kaldi-base.a
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ADDLIBS = ../tree/kaldi-tree.a ../thread/kaldi-thread.a ../util/kaldi-util.a \
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../matrix/kaldi-matrix.a ../base/kaldi-base.a
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@ -372,6 +372,31 @@ UnitTestEstimateDiagGmm() {
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test_io(*gmm, est_gmm, true, feats); // Binary mode
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}
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{ // Test multi-threaded update.
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GmmFlagsType flags_all = kGmmAll;
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est_gmm.Resize(gmm->NumGauss(),
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gmm->Dim(), flags_all);
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est_gmm.SetZero(flags_all);
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Vector<BaseFloat> weights(counter);
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for (size_t i = 0; i < counter; i++)
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weights(i) = 0.5 + 0.1 * (rand() % 10);
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float loglike = 0.0;
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for (size_t i = 0; i < counter; i++) {
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loglike += weights(i) *
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est_gmm.AccumulateFromDiag(*gmm, feats.Row(i), weights(i));
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}
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AccumDiagGmm est_gmm2(*gmm, flags_all);
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int32 num_threads = 2;
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float loglike2 =
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est_gmm2.AccumulateFromDiagMultiThreaded(*gmm, feats, weights, num_threads);
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AssertEqual(loglike, loglike2);
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est_gmm.AssertEqual(est_gmm2);
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}
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delete gmm;
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}
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@ -1,6 +1,6 @@
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// gmm/mle-diag-gmm.cc
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// Copyright 2009-2012 Saarland University; Georg Stemmer; Jan Silovsky;
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// Copyright 2009-2013 Saarland University; Georg Stemmer; Jan Silovsky;
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// Microsoft Corporation; Yanmin Qian;
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// Johns Hopkins University (author: Daniel Povey);
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// Cisco Systems (author: Neha Agrawal)
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@ -26,6 +26,7 @@
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#include "gmm/diag-gmm.h"
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#include "gmm/mle-diag-gmm.h"
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#include "thread/kaldi-thread.h"
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namespace kaldi {
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@ -202,7 +203,6 @@ BaseFloat AccumDiagGmm::AccumulateFromDiag(const DiagGmm &gmm,
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return log_like;
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}
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// Careful: this wouldn't be valid if it were used to update the
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// Gaussian weights.
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void AccumDiagGmm::SmoothStats(BaseFloat tau) {
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@ -478,5 +478,84 @@ void MapDiagGmmUpdate(const MapDiagGmmOptions &config,
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}
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class AccumulateMultiThreadedClass: public MultiThreadable {
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public:
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AccumulateMultiThreadedClass(const DiagGmm &diag_gmm,
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const MatrixBase<BaseFloat> &data,
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const VectorBase<BaseFloat> &frame_weights,
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AccumDiagGmm *accum,
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double *tot_like):
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diag_gmm_(diag_gmm), data_(data),
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frame_weights_(frame_weights), dest_accum_(accum),
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tot_like_ptr_(tot_like), tot_like_(0.0) { }
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AccumulateMultiThreadedClass(const AccumulateMultiThreadedClass &other):
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diag_gmm_(other.diag_gmm_), data_(other.data_),
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frame_weights_(other.frame_weights_), dest_accum_(other.dest_accum_),
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accum_(diag_gmm_, dest_accum_->Flags()), tot_like_ptr_(other.tot_like_ptr_),
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tot_like_(0.0) {
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KALDI_ASSERT(data_.NumRows() == frame_weights_.Dim());
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}
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void operator () () {
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int32 num_frames = data_.NumRows(), num_threads = num_threads_,
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block_size = (num_frames + num_threads - 1) / num_threads,
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block_start = block_size * thread_id_,
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block_end = std::min(num_frames, block_start + block_size);
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tot_like_ = 0.0;
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double tot_weight = 0.0;
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for (int32 t = block_start; t < block_end; t++) {
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tot_like_ += frame_weights_(t) *
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accum_.AccumulateFromDiag(diag_gmm_, data_.Row(t), frame_weights_(t));
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tot_weight += frame_weights_(t);
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}
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KALDI_VLOG(3) << "Thread " << thread_id_ << " saw average likeliood/frame "
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<< (tot_like_ / tot_weight) << " over " << tot_weight
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<< " (weighted) frames.";
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}
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~AccumulateMultiThreadedClass() {
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if (accum_.Dim() != 0) { // if our accumulator is set up (this is not true
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// for the single object we use to initialize the others)
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dest_accum_->Add(1.0, accum_);
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*tot_like_ptr_ += tot_like_;
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}
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}
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private:
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const DiagGmm &diag_gmm_;
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const MatrixBase<BaseFloat> &data_;
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const VectorBase<BaseFloat> &frame_weights_;
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AccumDiagGmm *dest_accum_;
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AccumDiagGmm accum_;
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double *tot_like_ptr_;
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double tot_like_;
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};
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BaseFloat AccumDiagGmm::AccumulateFromDiagMultiThreaded(
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const DiagGmm &gmm,
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const MatrixBase<BaseFloat> &data,
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const VectorBase<BaseFloat> &frame_weights,
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int32 num_threads) {
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double tot_like = 0.0;
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AccumulateMultiThreadedClass accumulator(gmm, data, frame_weights,
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this, &tot_like);
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{
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// Note: everything happens in the constructor and destructor of
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// the object created below.
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MultiThreader<AccumulateMultiThreadedClass> threader(num_threads,
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accumulator);
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// we need to make sure it's destroyed before we access the
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// value of tot_like.
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}
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return tot_like;
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}
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void AccumDiagGmm::AssertEqual(const AccumDiagGmm &other) {
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KALDI_ASSERT(dim_ == other.dim_ && num_comp_ == other.num_comp_ &&
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flags_ == other.flags_);
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KALDI_ASSERT(occupancy_.ApproxEqual(other.occupancy_));
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KALDI_ASSERT(mean_accumulator_.ApproxEqual(other.mean_accumulator_));
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KALDI_ASSERT(variance_accumulator_.ApproxEqual(other.variance_accumulator_));
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}
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} // End of namespace kaldi
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@ -142,6 +142,16 @@ class AccumDiagGmm {
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const VectorBase<BaseFloat> &data,
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BaseFloat frame_posterior);
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/// This does the same job as AccumulateFromDiag, but using
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/// multiple threads. Returns sum of (log-likelihood times
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/// frame weight) over all frames.
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BaseFloat AccumulateFromDiagMultiThreaded(
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const DiagGmm &gmm,
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const MatrixBase<BaseFloat> &data,
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const VectorBase<BaseFloat> &frame_weights,
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int32 num_threads);
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/// Increment the stats for this component by the specified amount
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/// (not all parts may be taken, depending on flags).
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/// Note: x_stats and x2_stats are assumed to already be multiplied by "occ"
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@ -173,7 +183,9 @@ class AccumDiagGmm {
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const VectorBase<double> &occupancy() const { return occupancy_; }
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const MatrixBase<double> &mean_accumulator() const { return mean_accumulator_; }
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const MatrixBase<double> &variance_accumulator() const { return variance_accumulator_; }
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// used in testing.
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void AssertEqual(const AccumDiagGmm &other);
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private:
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int32 dim_;
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int32 num_comp_;
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@ -61,13 +61,16 @@ void InitGmmFromRandomFrames(const Matrix<BaseFloat> &feats, DiagGmm *gmm) {
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void TrainOneIter(const Matrix<BaseFloat> &feats,
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const MleDiagGmmOptions &gmm_opts,
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int32 iter,
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int32 num_threads,
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DiagGmm *gmm) {
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AccumDiagGmm gmm_acc(*gmm, kGmmAll);
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double tot_like = 0.0;
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for (int32 t = 0; t < feats.NumRows(); t++)
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tot_like += gmm_acc.AccumulateFromDiag(*gmm, feats.Row(t), 1.0);
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Vector<BaseFloat> frame_weights(feats.NumRows(), kUndefined);
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frame_weights.Set(1.0);
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double tot_like;
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tot_like = gmm_acc.AccumulateFromDiagMultiThreaded(*gmm, feats, frame_weights,
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num_threads);
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KALDI_LOG << "Likelihood per frame on iteration " << iter
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<< " was " << (tot_like / feats.NumRows()) << " over "
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@ -97,17 +100,24 @@ int main(int argc, char *argv[]) {
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bool binary = true;
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int32 num_gauss = 100;
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int32 num_gauss_init = 0;
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int32 num_iters = 50;
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int32 num_frames = 200000;
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int32 srand_seed = 0;
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int32 num_threads = 4;
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po.Register("binary", &binary, "Write output in binary mode");
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po.Register("num-gauss", &num_gauss, "Number of Gaussians in the model");
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po.Register("num-gauss-init", &num_gauss_init, "Number of Gaussians in "
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"the model initially (if nonzero and less than num_gauss, "
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"we'll do mixture splitting)");
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po.Register("num-iters", &num_iters, "Number of iterations of training");
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po.Register("num-frames", &num_frames, "Number of feature vectors to store in "
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"memory and train on (randomly chosen from the input features)");
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po.Register("srand", &srand_seed, "Seed for random number generator ");
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po.Register("num-threads", &num_threads, "Number of threads used for "
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"statistics accumulation");
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gmm_opts.Register(&po);
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po.Read(argc, argv);
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@ -132,7 +142,7 @@ int main(int argc, char *argv[]) {
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int64 num_read = 0, dim = 0;
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KALDI_LOG << "Reading features (will keep " << num_frames << " frames.)";
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for (; !feature_reader.Done(); feature_reader.Next()) {
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const Matrix<BaseFloat> &this_feats = feature_reader.Value();
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for (int32 t = 0; t < this_feats.NumRows(); t++) {
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@ -160,15 +170,36 @@ int main(int argc, char *argv[]) {
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KALDI_WARN << "Number of frames read " << num_read << " was less than "
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<< "target number " << num_frames << ", using all we read.";
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feats.Resize(num_read, dim, kCopyData);
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} else {
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BaseFloat percent = num_frames * 100.0 / num_read;
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KALDI_LOG << "Kept " << num_frames << " out of " << num_read
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<< " input frames = " << percent << "%.";
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}
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DiagGmm gmm(num_gauss, dim);
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KALDI_LOG << "Initializing GMM means from random frames";
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InitGmmFromRandomFrames(feats, &gmm);
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if (num_gauss_init <= 0 || num_gauss_init > num_gauss)
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num_gauss_init = num_gauss;
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for (int32 iter = 0; iter < num_iters; iter++)
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TrainOneIter(feats, gmm_opts, iter, &gmm);
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DiagGmm gmm(num_gauss_init, dim);
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KALDI_LOG << "Initializing GMM means from random frames to "
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<< num_gauss_init << " Gaussians.";
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InitGmmFromRandomFrames(feats, &gmm);
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// we'll increase the #Gaussians by splitting,
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// till halfway through training.
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int32 cur_num_gauss = num_gauss_init,
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gauss_inc = (num_gauss - num_gauss_init) / (num_iters / 2);
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for (int32 iter = 0; iter < num_iters; iter++) {
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TrainOneIter(feats, gmm_opts, iter, num_threads, &gmm);
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int32 next_num_gauss = std::min(num_gauss, cur_num_gauss + gauss_inc);
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if (next_num_gauss > cur_num_gauss) {
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KALDI_LOG << "Splitting to " << next_num_gauss << " Gaussians.";
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gmm.Split(next_num_gauss, 0.1);
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cur_num_gauss = next_num_gauss;
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}
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}
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WriteKaldiObject(gmm, model_wxfilename, binary);
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KALDI_LOG << "Wrote model to " << model_wxfilename;
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