text: // bin/align-mapped.cc
  
  // Copyright 2009-2012  Microsoft Corporation, Karel Vesely
  //           2013-2014  Johns Hopkins University (author: Daniel Povey)
  
  // 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 "util/common-utils.h"
  #include "hmm/transition-model.h"
  #include "fstext/fstext-lib.h"
  #include "decoder/decoder-wrappers.h"
  #include "decoder/training-graph-compiler.h"
  #include "decoder/decodable-matrix.h"
  #include "lat/kaldi-lattice.h" // for {Compact}LatticeArc
  
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      typedef kaldi::int32 int32;
      using fst::SymbolTable;
      using fst::VectorFst;
      using fst::Fst;
      using fst::StdArc;
  
      const char *usage =
          "Generate alignments, reading log-likelihoods as matrices.\n"
          " (model is needed only for the integer mappings in its transition-model)\n"
          "Usage:   align-mapped [options] <tree-in> <trans-model-in> <lexicon-fst-in> "
          "<feature-rspecifier> <transcriptions-rspecifier> <alignments-wspecifier>\n"
          "e.g.: \n"
          " align-mapped tree trans.mdl lex.fst scp:train.scp ark:train.tra ark:nnet.ali\n";
      ParseOptions po(usage);
      AlignConfig align_config;
      BaseFloat acoustic_scale = 1.0;
      std::string disambig_rxfilename;
      TrainingGraphCompilerOptions gopts;
  
      align_config.Register(&po);
      gopts.Register(&po);
      po.Register("acoustic-scale", &acoustic_scale, "Scaling factor for acoustic likelihoods");
      po.Register("read-disambig-syms", &disambig_rxfilename, "File containing "
                  "list of disambiguation symbols in phone symbol table");
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 6) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string tree_in_filename = po.GetArg(1),
          model_in_filename = po.GetArg(2),
          lex_in_filename = po.GetArg(3),
          feature_rspecifier = po.GetArg(4),
          transcript_rspecifier = po.GetArg(5),
          alignment_wspecifier = po.GetArg(6);
  
      ContextDependency ctx_dep;
      ReadKaldiObject(tree_in_filename, &ctx_dep);
  
      TransitionModel trans_model;
      ReadKaldiObject(model_in_filename, &trans_model);
  
      Fst<StdArc> *lex_fst = fst::ReadFstKaldiGeneric(lex_in_filename);
      // ownership will be taken by gc.
  
      std::vector<int32> disambig_syms;    
      if (disambig_rxfilename != "")
        if (!ReadIntegerVectorSimple(disambig_rxfilename, &disambig_syms))
          KALDI_ERR << "fstcomposecontext: Could not read disambiguation symbols from "
                    << disambig_rxfilename;
      
      TrainingGraphCompiler gc(trans_model, ctx_dep, lex_fst, disambig_syms,
                               gopts);
  
      lex_fst = NULL;  // we gave ownership to gc.
      
      SequentialBaseFloatMatrixReader loglikes_reader(feature_rspecifier);
      RandomAccessInt32VectorReader transcript_reader(transcript_rspecifier);
      Int32VectorWriter alignment_writer(alignment_wspecifier);
  
      int num_done = 0, num_err = 0, num_retry = 0;
      double tot_like = 0.0;
      kaldi::int64 frame_count = 0;
  
      for (; !loglikes_reader.Done(); loglikes_reader.Next()) {
        std::string utt = loglikes_reader.Key();
        if (!transcript_reader.HasKey(utt)) {
          KALDI_WARN << "No transcript for utterance " << utt;
          num_err++;
          continue;
        }
        const Matrix<BaseFloat> &loglikes = loglikes_reader.Value();
        const std::vector<int32> &transcript = transcript_reader.Value(utt);
  
        VectorFst<StdArc> decode_fst;
        if (!gc.CompileGraphFromText(transcript, &decode_fst)) {
          KALDI_WARN << "Problem creating decoding graph for utterance " <<
              utt <<" [serious error]";
          num_err++;
          continue;
        }
        if (loglikes.NumRows() == 0) {
          KALDI_WARN << "Empty loglikes matrix for utterance: " << utt;
          num_err++;
          continue;
        }
  
        DecodableMatrixScaledMapped decodable(trans_model, loglikes, acoustic_scale);
  
        AlignUtteranceWrapper(align_config, utt,
                              acoustic_scale, &decode_fst, &decodable,
                              &alignment_writer, NULL,
                              &num_done, &num_err, &num_retry,
                              &tot_like, &frame_count);
      }
      KALDI_LOG << "Overall log-likelihood per frame is " << (tot_like/frame_count)
                << " over " << frame_count<< " frames.";
      KALDI_LOG << "Retried " << num_retry << " out of "
                << (num_done + num_err) << " utterances.";
      KALDI_LOG << "Done " << num_done << ", errors on " << num_err;
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }
  
  
  