compare-feats.cc File Reference

#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "matrix/kaldi-matrix.h"
#include "matrix/kaldi-vector.h"

Include dependency graph for compare-feats.cc:

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Functions
int	main (int argc, char *argv[])

Function Documentation

main()

int main	(	int	argc,
		char *	argv[]
	)

Definition at line 28 of file compare-feats.cc.

References VectorBase< Real >::AddVec(), VectorBase< Real >::AddVecDivVec(), VectorBase< Real >::CopyColFromMat(), SequentialTableReader< Holder >::Done(), ParseOptions::GetArg(), RandomAccessTableReader< Holder >::HasKey(), rnnlm::i, KALDI_LOG, KALDI_WARN, SequentialTableReader< Holder >::Key(), SequentialTableReader< Holder >::Next(), ParseOptions::NumArgs(), ParseOptions::PrintUsage(), ParseOptions::Read(), ParseOptions::Register(), Vector< Real >::Resize(), VectorBase< Real >::Sum(), RandomAccessTableReader< Holder >::Value(), SequentialTableReader< Holder >::Value(), and kaldi::VecVec().

                                 {
  try {
    using namespace kaldi;

    const char *usage =
        "Computes relative difference between two sets of features\n"
        "per dimension and an average difference\n"
        "Can be used to figure out how different two sets of features are.\n"
        "Inputs must have same dimension.  Prints to stdout a similarity\n"
        "metric vector that is 1.0 per dimension if the features identical,\n"
        "and <1.0 otherwise, and an average overall similarity value.\n"
        "\n"
    "Usage: compare-feats [options] <in-rspecifier1> <in-rspecifier2>\n"
        "e.g.: compare-feats ark:1.ark ark:2.ark\n";

    ParseOptions po(usage);

    BaseFloat threshold = 0.99;
    po.Register("threshold", &threshold, "Similarity threshold, affects "
                "return status");
    
    po.Read(argc, argv);

    if (po.NumArgs() != 2) {
      po.PrintUsage();
      exit(1);
    }

    std::string rspecifier1 = po.GetArg(1), rspecifier2 = po.GetArg(2);
    
    int32 num_done = 0, num_err = 0, Dim = 0;
    Vector<double> prod1, prod2, cross_prod, similarity_metric;
    double overall_similarity = 0;

    SequentialBaseFloatMatrixReader feat_reader1(rspecifier1);
    RandomAccessBaseFloatMatrixReader feat_reader2(rspecifier2);

    for (; !feat_reader1.Done(); feat_reader1.Next()) {
      std::string utt = feat_reader1.Key();
      Matrix<BaseFloat> feat1 (feat_reader1.Value());


      if (!feat_reader2.HasKey(utt)) {
        KALDI_WARN << "Second table has no feature for utterance "
                   << utt;
        num_err++;
        continue;
      }
      Matrix<BaseFloat> feat2 (feat_reader2.Value(utt));
      if (feat1.NumCols() != feat2.NumCols()) {
        KALDI_WARN << "Feature dimensions differ for utterance "
                   << utt << ", " << feat1.NumCols() << " vs. "
                   << feat2.NumCols() << ", skipping  utterance."
                   << utt;
        num_err++;
        continue;
      }
      
      if (num_done == 0){
        Dim=feat1.NumCols();
        prod1.Resize(Dim);
        prod2.Resize(Dim);
        cross_prod.Resize(Dim);
        similarity_metric.Resize(Dim);
      }
      
      Vector<BaseFloat> feat1_col(feat1.NumRows()), feat2_col(feat2.NumRows());
      for (MatrixIndexT i = 0; i < feat1.NumCols(); i++){
        feat1_col.CopyColFromMat(feat1, i);
        feat2_col.CopyColFromMat(feat2, i);
        prod1(i) += VecVec(feat1_col, feat1_col);
        prod2(i) += VecVec(feat2_col, feat2_col);
        cross_prod(i) += VecVec(feat1_col, feat2_col);
      }
      num_done++;
    }

    KALDI_LOG << "self-product of 1st features for each column dimension: " << prod1;
    KALDI_LOG << "self-product of 2nd features for each column dimension: " << prod2;
    KALDI_LOG << "cross-product for each column dimension: " << cross_prod;

    prod1.AddVec(1.0, prod2);
    similarity_metric.AddVecDivVec(2.0, cross_prod, prod1, 0.0);
    KALDI_LOG << "Similarity metric for each dimension " << similarity_metric
              << " (1.0 means identical, the smaller the more different)";

    overall_similarity = similarity_metric.Sum() / static_cast<double>(Dim);

    KALDI_LOG << "Overall similarity for the two feats is:" << overall_similarity
              << " (1.0 means identical, the smaller the more different)";

    KALDI_LOG << "Processed " << num_done << " feature files, "
              << num_err << " had errors.";

    bool similar = (overall_similarity >= threshold);

    if (num_done > 0) {
      if (similar) {
        KALDI_LOG << "Features are considered similar since "
                  << overall_similarity << " >= " << threshold;
      } else {
        KALDI_LOG << "Features are considered dissimilar since "
                  << overall_similarity << " < " << threshold;
      }
    }

    return (num_done > 0 && similar) ? 0 : 1;
  } catch(const std::exception &e) {
    std::cerr << e.what();
    return -1;
  }
}