lattice-to-kws-index.cc File Reference

#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "fstext/fstext-utils.h"
#include "lat/kaldi-lattice.h"
#include "lat/lattice-functions.h"
#include "kws/kaldi-kws.h"
#include "kws/kws-functions.h"
#include "fstext/epsilon-property.h"

Include dependency graph for lattice-to-kws-index.cc:

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

Function Documentation

main()

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

Definition at line 31 of file lattice-to-kws-index.cc.

References kaldi::ClusterLattice(), kaldi::CompactLatticeStateTimes(), kaldi::CreateFactorTransducer(), kaldi::DoFactorDisambiguation(), kaldi::DoFactorMerging(), SequentialTableReader< Holder >::Done(), fst::EnsureEpsilonProperty(), SequentialTableReader< Holder >::FreeCurrent(), ParseOptions::GetArg(), ParseOptions::GetOptArg(), RandomAccessTableReader< Holder >::HasKey(), KALDI_LOG, KALDI_VLOG, KALDI_WARN, SequentialTableReader< Holder >::Key(), kaldi::MaybeDoSanityCheck(), SequentialTableReader< Holder >::Next(), ParseOptions::NumArgs(), kaldi::OptimizeFactorTransducer(), ParseOptions::PrintUsage(), ParseOptions::Read(), ParseOptions::Register(), kaldi::RemoveLongSilences(), RandomAccessTableReader< Holder >::Value(), SequentialTableReader< Holder >::Value(), and TableWriter< Holder >::Write().

                                 {
  try {
    using namespace kaldi;
    using fst::VectorFst;
    typedef kaldi::int32 int32;
    typedef kaldi::uint64 uint64;

    const char *usage =
        "Create an inverted index of the given lattices. The output index is \n"
        "in the T*T*T semiring. For details for the semiring, please refer to\n"
        "Dogan Can and Murat Saraclar's paper named "
        "\"Lattice Indexing for Spoken Term Detection\"\n"
        "\n"
        "Usage: lattice-to-kws-index [options]  "
        " <utter-symtab-rspecifier> <lattice-rspecifier> <index-wspecifier>\n"
        "e.g.: \n"
        " lattice-to-kws-index ark:utter.symtab ark:1.lats ark:global.idx\n";

    ParseOptions po(usage);

    int32 frame_subsampling_factor = 1;
    int32 max_silence_frames = 50;
    bool strict = true;
    bool allow_partial = true;
    BaseFloat max_states_scale = 4;
    po.Register("frame-subsampling-factor", &frame_subsampling_factor,
                "Frame subsampling factor. (Default value 1)");
    po.Register("max-silence-frames", &max_silence_frames,
                "If --frame-subsampling-factor is used, --max-silence-frames "
                "is relative to the the input, not the output frame rate "
                "(we divide by frame-subsampling-factor and round to "
                "the closest integer, to get the number of symbols in the "
                "lattice).");
    po.Register("strict", &strict, "Setting --strict=false will cause "
                "successful termination even if we processed no lattices.");
    po.Register("max-states-scale", &max_states_scale, "Number of states in the"
                " original lattice times this scale is the number of states "
                "allowed when optimizing the index. Negative number means no "
                "limit on the number of states.");
    po.Register("allow-partial", &allow_partial, "Allow partial output if fails"
                " to determinize, otherwise skip determinization if it fails.");

    po.Read(argc, argv);

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

    max_silence_frames = 0.5 +
            max_silence_frames / static_cast<float>(frame_subsampling_factor);
    std::string usymtab_rspecifier = po.GetOptArg(1),
        lats_rspecifier = po.GetArg(2),
        index_wspecifier = po.GetArg(3);

    // We use RandomAccessInt32Reader to read the utterance symtab table.
    RandomAccessInt32Reader usymtab_reader(usymtab_rspecifier);

    // We read the lattice in as CompactLattice; We need the CompactLattice
    // structure for the rest of the work
    SequentialCompactLatticeReader clat_reader(lats_rspecifier);

    TableWriter< fst::VectorFstTplHolder<KwsLexicographicArc> >
                                                index_writer(index_wspecifier);

    int32 n_done = 0;
    int32 n_fail = 0;

    int32 max_states = -1;

    for (; !clat_reader.Done(); clat_reader.Next()) {
      std::string key = clat_reader.Key();
      CompactLattice clat = clat_reader.Value();
      clat_reader.FreeCurrent();
      KALDI_LOG << "Processing lattice " << key;

      if (max_states_scale > 0) {
        max_states = static_cast<int32>(
            max_states_scale * static_cast<BaseFloat>(clat.NumStates()));
      }

      // Check if we have the corresponding utterance id.
      if (!usymtab_reader.HasKey(key)) {
        KALDI_WARN << "Cannot find utterance id for " << key;
        n_fail++;
        continue;
      }

      // Topologically sort the lattice, if not already sorted.
      uint64 props = clat.Properties(fst::kFstProperties, false);
      if (!(props & fst::kTopSorted)) {
        if (fst::TopSort(&clat) == false) {
          KALDI_WARN << "Cycles detected in lattice " << key;
          n_fail++;
          continue;
        }
      }

      // Get the alignments
      std::vector<int32> state_times;
      CompactLatticeStateTimes(clat, &state_times);

      // Cluster the arcs in the CompactLattice, write the cluster_id on the
      // output label side.
      // ClusterLattice() corresponds to the second part of the preprocessing in
      // Dogan and Murat's paper -- clustering. Note that we do the first part
      // of preprocessing (the weight pushing step) later when generating the
      // factor transducer.
      KALDI_VLOG(1) << "Arc clustering...";
      bool success = false;
      success = kaldi::ClusterLattice(&clat, state_times);
      if (!success) {
        KALDI_WARN << "State id's and alignments do not match for lattice "
                   << key;
        n_fail++;
        continue;
      }

      // The next part is something new, not in the Dogan and Can paper.  It is
      // necessary because we have epsilon arcs, due to silences, in our
      // lattices.  We modify the factor transducer, while maintaining
      // equivalence, to ensure that states don't have both epsilon *and*
      // non-epsilon arcs entering them.  (and the same, with "entering"
      // replaced with "leaving").  Later we will find out which states have
      // non-epsilon arcs leaving/entering them and use it to be more selective
      // in adding arcs to connect them with the initial/final states.  The goal
      // here is to disallow silences at the beginning or ending of a keyword
      // occurrence.
      if (true) {
        EnsureEpsilonProperty(&clat);
        fst::TopSort(&clat);
        // We have to recompute the state times because they will have changed.
        CompactLatticeStateTimes(clat, &state_times);
      }

      // Generate factor transducer
      // CreateFactorTransducer() corresponds to the "Factor Generation" part of
      // Dogan and Murat's paper. But we also move the weight pushing step to
      // this function as we have to compute the alphas and betas anyway.
      KALDI_VLOG(1) << "Generating factor transducer...";
      KwsProductFst factor_transducer;
      int32 utterance_id = usymtab_reader.Value(key);
      success = kaldi::CreateFactorTransducer(clat,
                                              state_times,
                                              utterance_id,
                                              &factor_transducer);
      if (!success) {
        KALDI_WARN << "Cannot generate factor transducer for lattice " << key;
        n_fail++;
      }

      MaybeDoSanityCheck(factor_transducer);

      // Remove long silence arc
      // We add the filtering step in our implementation. This is because gap
      // between two successive words in a query term should be less than 0.5s
      KALDI_VLOG(1) << "Removing long silence...";
      RemoveLongSilences(max_silence_frames, state_times, &factor_transducer);

      MaybeDoSanityCheck(factor_transducer);

      // Do factor merging, and return a transducer in T*T*T semiring. This step
      // corresponds to the "Factor Merging" part in Dogan and Murat's paper.
      KALDI_VLOG(1) << "Merging factors...";
      KwsLexicographicFst index_transducer;
      DoFactorMerging(&factor_transducer, &index_transducer);

      MaybeDoSanityCheck(index_transducer);

      // Do factor disambiguation. It corresponds to the "Factor Disambiguation"
      // step in Dogan and Murat's paper.
      KALDI_VLOG(1) << "Doing factor disambiguation...";
      DoFactorDisambiguation(&index_transducer);

      MaybeDoSanityCheck(index_transducer);

      // Optimize the above factor transducer. It corresponds to the
      // "Optimization" step in the paper.
      KALDI_VLOG(1) << "Optimizing factor transducer...";
      OptimizeFactorTransducer(&index_transducer, max_states, allow_partial);

      MaybeDoSanityCheck(index_transducer);

      // Write result
      index_writer.Write(key, index_transducer);

      n_done++;
    }

    KALDI_LOG << "Done " << n_done << " lattices, failed for " << n_fail;
    if (strict == true)
      return (n_done != 0 ? 0 : 1);
    else
      return 0;
  } catch(const std::exception &e) {
    std::cerr << e.what();
    return -1;
  }
}