build-pfile-from-ali.cc
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// bin/build-pfile-from-ali.cc
// Copyright 2013 Carnegie Mellon University (Author: Yajie Miao)
// Johns Hopkins University (Author: Daniel Povey)
// 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 <string>
using std::string;
#include <vector>
using std::vector;
#include "base/kaldi-common.h"
#include "gmm/am-diag-gmm.h"
#include "hmm/transition-model.h"
#include "hmm/hmm-utils.h"
#include "util/common-utils.h"
/** @brief Build pfiles for Neural Network training from alignment.
* The pfiles contains both the data vectors and their corresponding
* class/state labels (zero-based).
*/
int main(int argc, char *argv[]) {
using namespace kaldi;
typedef kaldi::int32 int32;
try {
const char *usage =
"Build pfiles for neural network training from alignment.\n"
"Usage: build-pfile-from-ali [options] <model> <alignments-rspecifier> <feature-rspecifier> \n"
"<pfile-wspecifier>\n"
"e.g.: \n"
" build-pfile-from-ali 1.mdl ark:1.ali features \n"
" \"|pfile_create -i - -o pfile.1 -f 143 -l 1\" ";
ParseOptions po(usage);
int32 every_nth_frame = 1;
po.Register("every-nth-frame", &every_nth_frame, "This option will cause it to print "
"out only every n'th frame (for subsampling)");
po.Read(argc, argv);
if (po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
std::string model_filename = po.GetArg(1),
alignments_rspecifier = po.GetArg(2),
feature_rspecifier = po.GetArg(3),
pfile_wspecifier = po.GetArg(4);
TransitionModel trans_model;
AmDiagGmm am_gmm;
{
bool binary;
Input ki(model_filename, &binary);
trans_model.Read(ki.Stream(), binary);
am_gmm.Read(ki.Stream(), binary);
}
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessInt32VectorReader ali_reader(alignments_rspecifier);
int32 num_done = 0, num_no_ali = 0, num_other_error = 0;
int32 num_utt = 0;
KALDI_ASSERT(every_nth_frame >= 1);
Output ko(pfile_wspecifier, false);
for (; !feature_reader.Done(); feature_reader.Next()) {
std::string key = feature_reader.Key();
if (!ali_reader.HasKey(key)) {
KALDI_WARN << "Did not find alignment for utterance " << key;
num_no_ali++;
continue;
}
const Matrix<BaseFloat> &feats = feature_reader.Value();
std::vector<int32> alignment = ali_reader.Value(key);
if (static_cast<int32>(feats.NumRows()) != static_cast<int32>(alignment.size())) {
KALDI_WARN << "Alignment vector has wrong size " << (alignment.size())
<< " vs. " << (feats.NumRows());
num_other_error++;
continue;
}
int32 dim = feats.NumCols();
for (size_t i = 0; i < alignment.size(); i++) {
if (i % every_nth_frame == 0) {
std::stringstream ss;
// Output sentence number and frame number
ss << num_utt;
ss << " ";
ss << (i / every_nth_frame);
// Output feature vector
for (int32 d = 0; d < dim; ++d) {
ss << " ";
ss << feats(i, d);
}
// Output the class label
ss << " ";
ss << trans_model.TransitionIdToPdf(alignment[i]);
ko.Stream() << ss.str().c_str();
ko.Stream() << "\n";
}
}
num_done ++; num_utt ++;
}
ko.Close();
KALDI_LOG << "Converted " << num_done << " alignments to pfiles.";
KALDI_LOG << num_no_ali << " utterances have no alignment; "
<< num_other_error << " utterances have other errors.";
} catch(const std::exception& e) {
std::cerr << e.what();
return -1;
}
}