acc-lda.cc File Reference

#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "hmm/transition-model.h"
#include "hmm/posterior.h"
#include "transform/lda-estimate.h"

Include dependency graph for acc-lda.cc:

Go to the source code of this file.

Functions
int	main (int argc, char *argv[])
	Accumulate LDA statistics based on pdf-ids. More...

Function Documentation

main()

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

Accumulate LDA statistics based on pdf-ids.

Inputs are the source models, that serve as the input (and may potentially contain the current transformation), the un-transformed features and state posterior probabilities

Definition at line 32 of file acc-lda.cc.

References LdaEstimate::Accumulate(), kaldi::ConvertPosteriorToPdfs(), LdaEstimate::Dim(), SequentialTableReader< Holder >::Done(), ParseOptions::GetArg(), RandomAccessTableReader< Holder >::HasKey(), rnnlm::i, LdaEstimate::Init(), rnnlm::j, KALDI_LOG, KALDI_WARN, SequentialTableReader< Holder >::Key(), SequentialTableReader< Holder >::Next(), ParseOptions::NumArgs(), TransitionModel::NumPdfs(), ParseOptions::PrintUsage(), kaldi::RandPrune(), ParseOptions::Read(), TransitionModel::Read(), ParseOptions::Register(), Output::Stream(), Input::Stream(), RandomAccessTableReader< Holder >::Value(), SequentialTableReader< Holder >::Value(), and LdaEstimate::Write().

                                 {
  using namespace kaldi;
  typedef kaldi::int32 int32;
  try {
    const char *usage =
        "Accumulate LDA statistics based on pdf-ids.\n"
        "Usage:  acc-lda [options] <transition-gmm/model> <features-rspecifier> <posteriors-rspecifier> <lda-acc-out>\n"
        "Typical usage:\n"
        " ali-to-post ark:1.ali ark:- | acc-lda 1.mdl \"ark:splice-feats scp:train.scp|\"  ark:- ldaacc.1\n";

    bool binary = true;
    BaseFloat rand_prune = 0.0;
    ParseOptions po(usage);
    po.Register("binary", &binary, "Write accumulators in binary mode.");
    po.Register("rand-prune", &rand_prune,
                "Randomized pruning threshold for posteriors");
    po.Read(argc, argv);

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

    std::string model_rxfilename = po.GetArg(1);
    std::string features_rspecifier = po.GetArg(2);
    std::string posteriors_rspecifier = po.GetArg(3);
    std::string acc_wxfilename = po.GetArg(4);

    TransitionModel trans_model;
    {
      bool binary_read;
      Input ki(model_rxfilename, &binary_read);
      trans_model.Read(ki.Stream(), binary_read);
      // discard rest of file.
    }

    LdaEstimate lda;

    SequentialBaseFloatMatrixReader feature_reader(features_rspecifier);
    RandomAccessPosteriorReader posterior_reader(posteriors_rspecifier);

    int32 num_done = 0, num_fail = 0;
    for (;!feature_reader.Done(); feature_reader.Next()) {
      std::string utt = feature_reader.Key();
      if (!posterior_reader.HasKey(utt)) {
        KALDI_WARN << "No posteriors for utterance " << utt;
        num_fail++;
        continue;
      }
      const Posterior &post (posterior_reader.Value(utt));
      const Matrix<BaseFloat> &feats(feature_reader.Value());

      if (lda.Dim() == 0)
        lda.Init(trans_model.NumPdfs(), feats.NumCols());

      if (feats.NumRows() != static_cast<int32>(post.size())) {
        KALDI_WARN << "Posterior vs. feats size mismatch "
                   << post.size() << " vs. " << feats.NumRows();
        num_fail++;
        continue;
      }
      if (lda.Dim() != 0 && lda.Dim() != feats.NumCols()) {
        KALDI_WARN << "Feature dimension mismatch " << lda.Dim()
                   << " vs. " << feats.NumCols();
        num_fail++;
        continue;
      }

      Posterior pdf_post;
      ConvertPosteriorToPdfs(trans_model, post, &pdf_post);
      for (int32 i = 0; i < feats.NumRows(); i++) {
        SubVector<BaseFloat> feat(feats, i);
        for (size_t j = 0; j < pdf_post[i].size(); j++) {
          int32 pdf_id = pdf_post[i][j].first;
          BaseFloat weight = RandPrune(pdf_post[i][j].second, rand_prune);
          if (weight != 0.0) {
            lda.Accumulate(feat, pdf_id, weight);
          }
        }
      }
      num_done++;
      if (num_done % 100 == 0)
        KALDI_LOG << "Done " << num_done << " utterances.";
    }

    KALDI_LOG << "Done " << num_done << " files, failed for "
              << num_fail;

    Output ko(acc_wxfilename, binary);
    lda.Write(ko.Stream(), binary);
    KALDI_LOG << "Written statistics.";
    return (num_done != 0 ? 0 : 1);
  } catch(const std::exception &e) {
    std::cerr << e.what();
    return -1;
  }
}