agglomerative-cluster.cc
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// ivectorbin/agglomerative-cluster.cc
// Copyright 2016-2018 David Snyder
// 2017-2018 Matthew Maciejewski
// 2019 Dogan Can
// See ../../COPYING for clarification regarding multiple authors
//
// 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.
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "util/stl-utils.h"
#include "ivector/agglomerative-clustering.h"
int main(int argc, char *argv[]) {
using namespace kaldi;
typedef kaldi::int32 int32;
try {
const char *usage =
"Cluster utterances by similarity score, used in diarization.\n"
"Takes a table of score matrices indexed by recording, with the\n"
"rows/columns corresponding to the utterances of that recording in\n"
"sorted order and a reco2utt file that contains the mapping from\n"
"recordings to utterances, and outputs a list of labels in the form\n"
"<utt> <label>. Clustering is done using agglomerative hierarchical\n"
"clustering with a score threshold as stop criterion. By default, the\n"
"program reads in similarity scores, but with --read-costs=true\n"
"the scores are interpreted as costs (i.e. a smaller value indicates\n"
"utterance similarity).\n"
"Usage: agglomerative-cluster [options] <scores-rspecifier> "
"<reco2utt-rspecifier> <labels-wspecifier>\n"
"e.g.: \n"
" agglomerative-cluster ark:scores.ark ark:reco2utt \n"
" ark,t:labels.txt\n";
ParseOptions po(usage);
std::string reco2num_spk_rspecifier;
BaseFloat threshold = 0.0, max_spk_fraction = 1.0;
bool read_costs = false;
int32 first_pass_max_utterances = std::numeric_limits<int16>::max();
po.Register("reco2num-spk-rspecifier", &reco2num_spk_rspecifier,
"If supplied, clustering creates exactly this many clusters for each"
" recording and the option --threshold is ignored.");
po.Register("threshold", &threshold, "Merge clusters if their distance"
" is less than this threshold.");
po.Register("read-costs", &read_costs, "If true, the first"
" argument is interpreted as a matrix of costs rather than a"
" similarity matrix.");
po.Register("first-pass-max-utterances", &first_pass_max_utterances,
"If the number of utterances is larger than first-pass-max-utterances,"
" then clustering is done in two passes. In the first pass, input points"
" are divided into contiguous subsets of size first-pass-max-utterances"
" and each subset is clustered separately. In the second pass, the first"
" pass clusters are merged into the final set of clusters.");
po.Register("max-spk-fraction", &max_spk_fraction, "Merge clusters if the"
" total fraction of utterances in them is less than this threshold."
" This is active only when reco2num-spk-rspecifier is supplied and"
" 1.0 / num-spk <= max-spk-fraction <= 1.0.");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string scores_rspecifier = po.GetArg(1),
reco2utt_rspecifier = po.GetArg(2),
label_wspecifier = po.GetArg(3);
SequentialBaseFloatMatrixReader scores_reader(scores_rspecifier);
RandomAccessTokenVectorReader reco2utt_reader(reco2utt_rspecifier);
RandomAccessInt32Reader reco2num_spk_reader(reco2num_spk_rspecifier);
Int32Writer label_writer(label_wspecifier);
if (!read_costs)
threshold = -threshold;
for (; !scores_reader.Done(); scores_reader.Next()) {
std::string reco = scores_reader.Key();
Matrix<BaseFloat> costs = scores_reader.Value();
// By default, the scores give the similarity between pairs of
// utterances. We need to multiply the scores by -1 to reinterpet
// them as costs (unless --read-costs=true) as the agglomerative
// clustering code requires.
if (!read_costs)
costs.Scale(-1);
std::vector<std::string> uttlist = reco2utt_reader.Value(reco);
std::vector<int32> spk_ids;
if (reco2num_spk_rspecifier.size()) {
int32 num_speakers = reco2num_spk_reader.Value(reco);
if (1.0 / num_speakers <= max_spk_fraction && max_spk_fraction <= 1.0)
AgglomerativeCluster(costs, std::numeric_limits<BaseFloat>::max(),
num_speakers, first_pass_max_utterances,
max_spk_fraction, &spk_ids);
else
AgglomerativeCluster(costs, std::numeric_limits<BaseFloat>::max(),
num_speakers, first_pass_max_utterances,
1.0, &spk_ids);
} else {
AgglomerativeCluster(costs, threshold, 1, first_pass_max_utterances,
1.0, &spk_ids);
}
for (int32 i = 0; i < spk_ids.size(); i++)
label_writer.Write(uttlist[i], spk_ids[i]);
}
return 0;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}