sgmm2-gselect.cc File Reference

#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "sgmm2/am-sgmm2.h"
#include "hmm/transition-model.h"

Include dependency graph for sgmm2-gselect.cc:

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

Function Documentation

main()

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

Definition at line 27 of file sgmm2-gselect.cc.

References SequentialTableReader< Holder >::Done(), AmSgmm2::GaussianSelection(), ParseOptions::GetArg(), rnnlm::i, KALDI_LOG, SequentialTableReader< Holder >::Key(), SequentialTableReader< Holder >::Next(), ParseOptions::NumArgs(), MatrixBase< Real >::NumRows(), ParseOptions::PrintUsage(), ParseOptions::Read(), TransitionModel::Read(), AmSgmm2::Read(), ParseOptions::Register(), Sgmm2GselectConfig::Register(), MatrixBase< Real >::Row(), Input::Stream(), SequentialTableReader< Holder >::Value(), and TableWriter< Holder >::Write().

                                 {
  try {
    using namespace kaldi;
    const char *usage =
        "Precompute Gaussian indices for SGMM training "
        "Usage: sgmm2-gselect [options] <model-in> <feature-rspecifier> <gselect-wspecifier>\n"
        "e.g.: sgmm2-gselect 1.sgmm \"ark:feature-command |\" ark:1.gs\n"
        "Note: you can do the same thing by combining the programs sgmm2-write-ubm, fgmm-global-to-gmm,\n"
        "gmm-gselect and fgmm-gselect\n";

    ParseOptions po(usage);
    kaldi::Sgmm2GselectConfig sgmm_opts;
    std::string preselect_rspecifier;
    std::string likelihood_wspecifier;
    po.Register("write-likes", &likelihood_wspecifier, "Wspecifier for likelihoods per "
                "utterance");
    sgmm_opts.Register(&po);
    po.Read(argc, argv);

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

    std::string model_filename = po.GetArg(1),
        feature_rspecifier = po.GetArg(2),
        gselect_wspecifier = po.GetArg(3);

    using namespace kaldi;
    typedef kaldi::int32 int32;

    AmSgmm2 am_sgmm;
    {
      bool binary;
      Input ki(model_filename, &binary);
      TransitionModel trans_model;
      trans_model.Read(ki.Stream(), binary);
      am_sgmm.Read(ki.Stream(), binary);
    }

    double tot_like = 0.0;
    kaldi::int64 tot_t = 0;

    SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
    Int32VectorVectorWriter gselect_writer(gselect_wspecifier);
    BaseFloatWriter likelihood_writer(likelihood_wspecifier);

    int32 num_done = 0, num_err = 0;
    for (; !feature_reader.Done(); feature_reader.Next()) {
      int32 tot_t_this_file = 0; double tot_like_this_file = 0;
      std::string utt = feature_reader.Key();
      const Matrix<BaseFloat> &mat = feature_reader.Value();
      std::vector<std::vector<int32> > gselect_vec(mat.NumRows());
      tot_t_this_file += mat.NumRows();
      for (int32 i = 0; i < mat.NumRows(); i++)
        tot_like_this_file += am_sgmm.GaussianSelection(sgmm_opts, mat.Row(i), &(gselect_vec[i]));

      gselect_writer.Write(utt, gselect_vec);
      if (num_done % 10 == 0)
        KALDI_LOG << "For " << num_done << "'th file, average UBM likelihood over "
                  << tot_t_this_file << " frames is "
                  << (tot_like_this_file/tot_t_this_file);
      tot_t += tot_t_this_file;
      tot_like += tot_like_this_file;

      if(likelihood_wspecifier != "")
        likelihood_writer.Write(utt, tot_like_this_file);
      num_done++;
    }

    KALDI_LOG << "Done " << num_done << " files, " << num_err
              << " with errors, average UBM log-likelihood is "
              << (tot_like/tot_t) << " over " << tot_t << " frames.";


    if (num_done != 0) return 0;
    else return 1;
  } catch(const std::exception &e) {
    std::cerr << e.what();
    return -1;
  }
}