sgmm2-est-spkvecs-gpost.cc File Reference

#include <string>
#include <vector>
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "sgmm2/am-sgmm2.h"
#include "sgmm2/estimate-am-sgmm2.h"
#include "hmm/transition-model.h"

Include dependency graph for sgmm2-est-spkvecs-gpost.cc:

Go to the source code of this file.

Namespaces
	kaldi
	This code computes Goodness of Pronunciation (GOP) and extracts phone-level pronunciation feature for mispronunciations detection tasks, the reference:

Functions
void	AccumulateForUtterance (const Matrix< BaseFloat > &feats, const Sgmm2GauPost &gpost, const TransitionModel &trans_model, const AmSgmm2 &am_sgmm, Sgmm2PerSpkDerivedVars *spk_vars, MleSgmm2SpeakerAccs *spk_stats)
int	main (int argc, char *argv[])

Function Documentation

main()

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

Definition at line 57 of file sgmm2-est-spkvecs-gpost.cc.

References kaldi::AccumulateForUtterance(), MleSgmm2SpeakerAccs::Clear(), AmSgmm2::ComputePerSpkDerivedVars(), VectorBase< Real >::CopyFromVec(), SequentialTableReader< Holder >::Done(), ParseOptions::GetArg(), Sgmm2PerSpkDerivedVars::GetSpeakerVector(), RandomAccessTableReader< Holder >::HasKey(), rnnlm::i, RandomAccessTableReader< Holder >::IsOpen(), KALDI_LOG, KALDI_WARN, SequentialTableReader< Holder >::Key(), kaldi::kSetZero, SequentialTableReader< Holder >::Next(), ParseOptions::NumArgs(), MatrixBase< Real >::NumRows(), ParseOptions::PrintUsage(), ParseOptions::Read(), TransitionModel::Read(), AmSgmm2::Read(), ParseOptions::Register(), Sgmm2PerSpkDerivedVars::SetSpeakerVector(), AmSgmm2::SpkSpaceDim(), Input::Stream(), MleSgmm2SpeakerAccs::Update(), RandomAccessTableReader< Holder >::Value(), SequentialTableReader< Holder >::Value(), and TableWriter< Holder >::Write().

                                 {
  try {
    typedef kaldi::int32 int32;
    using namespace kaldi;
    const char *usage =
        "Estimate SGMM speaker vectors, either per utterance or for the "
        "supplied set of speakers (with spk2utt option).\n"
        "Reads Gaussian-level posteriors. Writes to a table of vectors.\n"
        "Usage: sgmm2-est-spkvecs-gpost [options] <model-in> <feature-rspecifier> "
        "<gpost-rspecifier> <vecs-wspecifier>\n";

    ParseOptions po(usage);
    string spk2utt_rspecifier, spkvecs_rspecifier;
    BaseFloat min_count = 100;
    BaseFloat rand_prune = 1.0e-05;

    po.Register("spk2utt", &spk2utt_rspecifier,
        "File to read speaker to utterance-list map from.");
    po.Register("spkvec-min-count", &min_count,
        "Minimum count needed to estimate speaker vectors");
    po.Register("rand-prune", &rand_prune, "Randomized pruning parameter for posteriors (more->faster).");
    po.Register("spk-vecs", &spkvecs_rspecifier, "Speaker vectors to use during aligment (rspecifier)");
    po.Read(argc, argv);

    if (po.NumArgs() != 4) {
      po.PrintUsage();
      exit(1);
    }

    string model_rxfilename = po.GetArg(1),
        feature_rspecifier = po.GetArg(2),
        gpost_rspecifier = po.GetArg(3),
        vecs_wspecifier = po.GetArg(4);

    TransitionModel trans_model;
    AmSgmm2 am_sgmm;
    {
      bool binary;
      Input ki(model_rxfilename, &binary);
      trans_model.Read(ki.Stream(), binary);
      am_sgmm.Read(ki.Stream(), binary);
    }
    MleSgmm2SpeakerAccs spk_stats(am_sgmm, rand_prune);

    RandomAccessSgmm2GauPostReader gpost_reader(gpost_rspecifier);

    RandomAccessBaseFloatVectorReader spkvecs_reader(spkvecs_rspecifier);

    BaseFloatVectorWriter vecs_writer(vecs_wspecifier);

    double tot_impr = 0.0, tot_t = 0.0;
    int32 num_done = 0, num_err = 0;

    if (!spk2utt_rspecifier.empty()) {  // per-speaker adaptation
      SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
      RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);

      for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
        spk_stats.Clear();
        string spk = spk2utt_reader.Key();
        const vector<string> &uttlist = spk2utt_reader.Value();

        Sgmm2PerSpkDerivedVars spk_vars;
        if (spkvecs_reader.IsOpen()) {
          if (spkvecs_reader.HasKey(spk)) {
            spk_vars.SetSpeakerVector(spkvecs_reader.Value(spk));
            am_sgmm.ComputePerSpkDerivedVars(&spk_vars);
          } else {
            KALDI_WARN << "Cannot find speaker vector for " << spk;
          }
        }  // else spk_vars is "empty"

        for (size_t i = 0; i < uttlist.size(); i++) {
          std::string utt = uttlist[i];
          if (!feature_reader.HasKey(utt)) {
            KALDI_WARN << "Did not find features for utterance " << utt;
            continue;
          }
          const Matrix<BaseFloat> &feats = feature_reader.Value(utt);
          if (!gpost_reader.HasKey(utt) ||
              gpost_reader.Value(utt).size() != feats.NumRows()) {
            KALDI_WARN << "Did not find posteriors for utterance " << utt
                       << " (or wrong size).";
            num_err++;
            continue;
          }
          const Sgmm2GauPost &gpost = gpost_reader.Value(utt);
          
          AccumulateForUtterance(feats, gpost, trans_model, am_sgmm,
                                 &spk_vars, &spk_stats);
          num_done++;
        }  // end looping over all utterances of the current speaker

        BaseFloat impr, spk_tot_t;
        {  // Compute the spk_vec and write it out.
          Vector<BaseFloat> spk_vec(am_sgmm.SpkSpaceDim(), kSetZero);
          if (spk_vars.GetSpeakerVector().Dim() != 0)
            spk_vec.CopyFromVec(spk_vars.GetSpeakerVector());
          spk_stats.Update(am_sgmm, min_count, &spk_vec, &impr, &spk_tot_t);
          vecs_writer.Write(spk, spk_vec);
        }
        KALDI_LOG << "For speaker " << spk << ", auxf-impr from speaker vector is "
                  << (impr/spk_tot_t) << ", over " << spk_tot_t << " frames.\n";
        tot_impr += impr;
        tot_t += spk_tot_t;
      }  // end looping over speakers
    } else {  // per-utterance adaptation
      SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
      for (; !feature_reader.Done(); feature_reader.Next()) {
        string utt = feature_reader.Key();
        const Matrix<BaseFloat> &feats = feature_reader.Value();
        if (!gpost_reader.HasKey(utt) ||
            gpost_reader.Value(utt).size() != feats.NumRows()) {
          KALDI_WARN << "Did not find posts for utterance "
                     << utt;
          num_err++;
          continue;
        }
        const Sgmm2GauPost &gpost = gpost_reader.Value(utt);

        Sgmm2PerSpkDerivedVars spk_vars;
        if (spkvecs_reader.IsOpen()) {
          if (spkvecs_reader.HasKey(utt)) {
            spk_vars.SetSpeakerVector(spkvecs_reader.Value(utt));
            am_sgmm.ComputePerSpkDerivedVars(&spk_vars);
          } else {
            KALDI_WARN << "Cannot find speaker vector for " << utt;
          }
        }  // else spk_vars is "empty"
        
        num_done++;
        spk_stats.Clear();

        AccumulateForUtterance(feats, gpost, trans_model, am_sgmm,
                               &spk_vars, &spk_stats);

        BaseFloat impr, utt_tot_t;
        {  // Compute the spk_vec and write it out.
          Vector<BaseFloat> spk_vec(am_sgmm.SpkSpaceDim(), kSetZero);
          if (spk_vars.GetSpeakerVector().Dim() != 0)
            spk_vec.CopyFromVec(spk_vars.GetSpeakerVector());
          spk_stats.Update(am_sgmm, min_count, &spk_vec, &impr, &utt_tot_t);
          vecs_writer.Write(utt, spk_vec);
        }
        KALDI_LOG << "For utterance " << utt << ", auxf-impr from speaker vectors is "
                  << (impr/utt_tot_t) << ", over " << utt_tot_t << " frames.";
        tot_impr += impr;
        tot_t += utt_tot_t;
      }
    }

    KALDI_LOG << "Done " << num_done << " files, " << num_err
              << " with errors.";
    KALDI_LOG << "Overall auxf impr per frame is " << (tot_impr / tot_t)
              << " over " << tot_t << " frames.";
    return (num_done != 0 ? 0 : 1);
  } catch(const std::exception &e) {
    std::cerr << e.what();
    return -1;
  }
}