gmm-compute-likes.cc File Reference

#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "gmm/am-diag-gmm.h"
#include "hmm/transition-model.h"
#include "fstext/fstext-lib.h"
#include "base/timer.h"

Include dependency graph for gmm-compute-likes.cc:

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

Function Documentation

main()

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

Definition at line 30 of file gmm-compute-likes.cc.

References SequentialTableReader< Holder >::Done(), ParseOptions::GetArg(), rnnlm::i, rnnlm::j, KALDI_LOG, SequentialTableReader< Holder >::Key(), AmDiagGmm::LogLikelihood(), SequentialTableReader< Holder >::Next(), ParseOptions::NumArgs(), AmDiagGmm::NumPdfs(), ParseOptions::PrintUsage(), AmDiagGmm::Read(), ParseOptions::Read(), TransitionModel::Read(), Input::Stream(), SequentialTableReader< Holder >::Value(), and TableWriter< Holder >::Write().

                                 {
  try {
    using namespace kaldi;
    typedef kaldi::int32 int32;
    using fst::SymbolTable;
    using fst::VectorFst;
    using fst::StdArc;

    const char *usage =
        "Compute log-likelihoods from GMM-based model\n"
        "(outputs matrices of log-likelihoods indexed by (frame, pdf)\n"
        "Usage: gmm-compute-likes [options] model-in features-rspecifier likes-wspecifier\n";
    ParseOptions po(usage);

    po.Read(argc, argv);

    if (po.NumArgs() != 3) {
      po.PrintUsage();
      exit(1);
    }

    std::string model_in_filename = po.GetArg(1),
        feature_rspecifier = po.GetArg(2),
        loglikes_wspecifier = po.GetArg(3);

    AmDiagGmm am_gmm;
    {
      bool binary;
      TransitionModel trans_model;  // not needed.
      Input ki(model_in_filename, &binary);
      trans_model.Read(ki.Stream(), binary);
      am_gmm.Read(ki.Stream(), binary);
    }

    BaseFloatMatrixWriter loglikes_writer(loglikes_wspecifier);
    SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);

    int32 num_done = 0;
    for (; !feature_reader.Done(); feature_reader.Next()) {
      std::string key = feature_reader.Key();
      const Matrix<BaseFloat> &features (feature_reader.Value());
      Matrix<BaseFloat> loglikes(features.NumRows(), am_gmm.NumPdfs());
      for (int32 i = 0; i < features.NumRows(); i++) {
        for (int32 j = 0; j < am_gmm.NumPdfs(); j++) {
          SubVector<BaseFloat> feat_row(features, i);
          loglikes(i, j) = am_gmm.LogLikelihood(j, feat_row);
        }
      }
      loglikes_writer.Write(key, loglikes);
      num_done++;
    }

    KALDI_LOG << "gmm-compute-likes: computed likelihoods for " << num_done
              << " utterances.";
    return 0;
  } catch(const std::exception &e) {
    std::cerr << e.what();
    return -1;
  }
}