lattice-lmrescore-kaldi-rnnlm-pruned.cc

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// latbin/lattice-lmrescore-kaldi-rnnlm-pruned.cc

// Copyright 2017 Johns Hopkins University (author: Daniel Povey)
//           2017 Hainan Xu

// See ../../COPYING for clarification regarding multiple authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//  http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.


#include "base/kaldi-common.h"
#include "fstext/fstext-lib.h"
#include "rnnlm/rnnlm-lattice-rescoring.h"
#include "lm/const-arpa-lm.h"
#include "util/common-utils.h"
#include "nnet3/nnet-utils.h"
#include "lat/kaldi-lattice.h"
#include "lat/lattice-functions.h"
#include "lat/compose-lattice-pruned.h"

int main(int argc, char *argv[]) {
  try {
    using namespace kaldi;
    typedef kaldi::int32 int32;
    typedef kaldi::int64 int64;
    using fst::SymbolTable;
    using fst::VectorFst;
    using fst::StdArc;
    using fst::ReadFstKaldi;

    const char *usage =
        "Rescores lattice with kaldi-rnnlm. This script is called from \n"
        "scripts/rnnlm/lmrescore_pruned.sh. An example for rescoring \n"
        "lattices is at egs/swbd/s5c/local/rnnlm/run_lstm.sh \n"
        "\n"
        "Usage: lattice-lmrescore-kaldi-rnnlm-pruned [options] \\\n"
        "             <old-lm-rxfilename> <embedding-file> \\\n"
        "             <raw-rnnlm-rxfilename> \\\n"
        "             <lattice-rspecifier> <lattice-wspecifier>\n"
        " e.g.: lattice-lmrescore-kaldi-rnnlm-pruned --lm-scale=-1.0 fst_words.txt \\\n"
        "              --bos-symbol=1 --eos-symbol=2 \\\n"
        "              data/lang_test/G.fst word_embedding.mat \\\n"
        "              final.raw ark:in.lats ark:out.lats\n\n"
        "       lattice-lmrescore-kaldi-rnnlm-pruned --lm-scale=-1.0 fst_words.txt \\\n"
        "              --bos-symbol=1 --eos-symbol=2 \\\n"
        "              data/lang_test_fg/G.carpa word_embedding.mat \\\n"
        "              final.raw ark:in.lats ark:out.lats\n";

    ParseOptions po(usage);
    rnnlm::RnnlmComputeStateComputationOptions opts;
    ComposeLatticePrunedOptions compose_opts;

    int32 max_ngram_order = 3;
    BaseFloat lm_scale = 0.5;
    BaseFloat acoustic_scale = 0.1;
    bool use_carpa = false;

    po.Register("lm-scale", &lm_scale, "Scaling factor for <lm-to-add>; its negative "
                "will be applied to <lm-to-subtract>.");
    po.Register("acoustic-scale", &acoustic_scale, "Scaling factor for acoustic "
                "probabilities (e.g. 0.1 for non-chain systems); important because "
                "of its effect on pruning.");
    po.Register("max-ngram-order", &max_ngram_order,
        "If positive, allow RNNLM histories longer than this to be identified "
        "with each other for rescoring purposes (an approximation that "
        "saves time and reduces output lattice size).");
    po.Register("use-const-arpa", &use_carpa, "If true, read the old-LM file "
                "as a const-arpa file as opposed to an FST file");

    opts.Register(&po);
    compose_opts.Register(&po);

    po.Read(argc, argv);

    if (po.NumArgs() != 5) {
      po.PrintUsage();
      exit(1);
    }

    if (opts.bos_index == -1 || opts.eos_index == -1) {
      KALDI_ERR << "must set --bos-symbol and --eos-symbol options";
    }

    std::string lm_to_subtract_rxfilename, lats_rspecifier,
                word_embedding_rxfilename, rnnlm_rxfilename, lats_wspecifier;

    lm_to_subtract_rxfilename = po.GetArg(1),
    word_embedding_rxfilename = po.GetArg(2);
    rnnlm_rxfilename = po.GetArg(3);
    lats_rspecifier = po.GetArg(4);
    lats_wspecifier = po.GetArg(5);

    // for G.fst
    fst::ScaleDeterministicOnDemandFst *lm_to_subtract_det_scale = NULL;
    fst::BackoffDeterministicOnDemandFst<StdArc> *lm_to_subtract_det_backoff = NULL;
    VectorFst<StdArc> *lm_to_subtract_fst = NULL;

    // for G.carpa
    ConstArpaLm* const_arpa = NULL;
    fst::DeterministicOnDemandFst<StdArc> *carpa_lm_to_subtract_fst = NULL;

    KALDI_LOG << "Reading old LMs...";
    if (use_carpa) {
      const_arpa = new ConstArpaLm();
      ReadKaldiObject(lm_to_subtract_rxfilename, const_arpa);
      carpa_lm_to_subtract_fst = new ConstArpaLmDeterministicFst(*const_arpa);
      lm_to_subtract_det_scale
        = new fst::ScaleDeterministicOnDemandFst(-lm_scale,
                                                 carpa_lm_to_subtract_fst);
    } else {
      lm_to_subtract_fst = fst::ReadAndPrepareLmFst(
          lm_to_subtract_rxfilename);
      lm_to_subtract_det_backoff =
        new fst::BackoffDeterministicOnDemandFst<StdArc>(*lm_to_subtract_fst);
      lm_to_subtract_det_scale =
           new fst::ScaleDeterministicOnDemandFst(-lm_scale,
                                                  lm_to_subtract_det_backoff);
    }

    kaldi::nnet3::Nnet rnnlm;
    ReadKaldiObject(rnnlm_rxfilename, &rnnlm);

    KALDI_ASSERT(IsSimpleNnet(rnnlm));

    CuMatrix<BaseFloat> word_embedding_mat;
    ReadKaldiObject(word_embedding_rxfilename, &word_embedding_mat);

    const rnnlm::RnnlmComputeStateInfo info(opts, rnnlm, word_embedding_mat);

    // Reads and writes as compact lattice.
    SequentialCompactLatticeReader compact_lattice_reader(lats_rspecifier);
    CompactLatticeWriter compact_lattice_writer(lats_wspecifier);

    int32 num_done = 0, num_err = 0;

    rnnlm::KaldiRnnlmDeterministicFst* lm_to_add_orig = 
         new rnnlm::KaldiRnnlmDeterministicFst(max_ngram_order, info);

    for (; !compact_lattice_reader.Done(); compact_lattice_reader.Next()) {
      fst::DeterministicOnDemandFst<StdArc> *lm_to_add =
         new fst::ScaleDeterministicOnDemandFst(lm_scale, lm_to_add_orig);

      std::string key = compact_lattice_reader.Key();
      CompactLattice clat = compact_lattice_reader.Value();
      compact_lattice_reader.FreeCurrent();

      // Before composing with the LM FST, we scale the lattice weights
      // by the inverse of "lm_scale".  We'll later scale by "lm_scale".
      // We do it this way so we can determinize and it will give the
      // right effect (taking the "best path" through the LM) regardless
      // of the sign of lm_scale.
      if (acoustic_scale != 1.0) {
        fst::ScaleLattice(fst::AcousticLatticeScale(acoustic_scale), &clat);
      }
      TopSortCompactLatticeIfNeeded(&clat);

      fst::ComposeDeterministicOnDemandFst<StdArc> combined_lms(
          lm_to_subtract_det_scale, lm_to_add);

      // Composes lattice with language model.
      CompactLattice composed_clat;
      ComposeCompactLatticePruned(compose_opts, clat,
                                  &combined_lms, &composed_clat);

      lm_to_add_orig->Clear();

      if (composed_clat.NumStates() == 0) {
        // Something went wrong.  A warning will already have been printed.
        num_err++;
      } else {
        if (acoustic_scale != 1.0) {
          if (acoustic_scale == 0.0)
            KALDI_ERR << "Acoustic scale cannot be zero.";
          fst::ScaleLattice(fst::AcousticLatticeScale(1.0 / acoustic_scale),
                            &composed_clat);
        }
        compact_lattice_writer.Write(key, composed_clat);
        num_done++;
      }
      delete lm_to_add;
    }

    delete lm_to_subtract_fst;
    delete lm_to_add_orig;
    delete lm_to_subtract_det_backoff;
    delete lm_to_subtract_det_scale;

    delete const_arpa;
    delete carpa_lm_to_subtract_fst;

    KALDI_LOG << "Overall, succeeded for " << num_done
              << " lattices, failed for " << num_err;
    return (num_done != 0 ? 0 : 1);
  } catch(const std::exception &e) {
    std::cerr << e.what();
    return -1;
  }
}