// bin/decode-faster-mapped.cc
  
  // Copyright 2009-2011  Microsoft Corporation
  //                2013  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 "tree/context-dep.h"
  #include "hmm/transition-model.h"
  #include "fstext/fstext-lib.h"
  #include "decoder/faster-decoder.h"
  #include "decoder/decodable-matrix.h"
  #include "base/timer.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::StdArc;
  
      const char *usage =
          "Decode, reading log-likelihoods as matrices
  "
          " (model is needed only for the integer mappings in its transition-model)
  "
          "Usage:   decode-faster-mapped [options] <model-in> <fst-in> "
          "<loglikes-rspecifier> <words-wspecifier> [<alignments-wspecifier>]
  ";
      ParseOptions po(usage);
      bool binary = true;
      BaseFloat acoustic_scale = 0.1;
      bool allow_partial = true;
      std::string word_syms_filename;
      FasterDecoderOptions decoder_opts;
      decoder_opts.Register(&po, true);  // true == include obscure settings.
      po.Register("binary", &binary, "Write output in binary mode");
      po.Register("acoustic-scale", &acoustic_scale, "Scaling factor for acoustic likelihoods");
      po.Register("allow-partial", &allow_partial, "Produce output even when final state was not reached");
      po.Register("word-symbol-table", &word_syms_filename, "Symbol table for words [for debug output]");
  
      po.Read(argc, argv);
  
      if (po.NumArgs() < 4 || po.NumArgs() > 5) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string model_in_filename = po.GetArg(1),
          fst_in_filename = po.GetArg(2),
          loglikes_rspecifier = po.GetArg(3),
          words_wspecifier = po.GetArg(4),
          alignment_wspecifier = po.GetOptArg(5);
  
      TransitionModel trans_model;
      ReadKaldiObject(model_in_filename, &trans_model);
  
      Int32VectorWriter words_writer(words_wspecifier);
  
      Int32VectorWriter alignment_writer(alignment_wspecifier);
  
      fst::SymbolTable *word_syms = NULL;
      if (word_syms_filename != "") {
        word_syms = fst::SymbolTable::ReadText(word_syms_filename);
        if (!word_syms)
          KALDI_ERR << "Could not read symbol table from file "<<word_syms_filename;
      }
  
      SequentialBaseFloatMatrixReader loglikes_reader(loglikes_rspecifier);
  
      // It's important that we initialize decode_fst after loglikes_reader, as it
      // can prevent crashes on systems installed without enough virtual memory.
      // It has to do with what happens on UNIX systems if you call fork() on a
      // large process: the page-table entries are duplicated, which requires a
      // lot of virtual memory.
      VectorFst<StdArc> *decode_fst = fst::ReadFstKaldi(fst_in_filename);
  
      BaseFloat tot_like = 0.0;
      kaldi::int64 frame_count = 0;
      int num_success = 0, num_fail = 0;
      FasterDecoder decoder(*decode_fst, decoder_opts);
  
      Timer timer;
  
      for (; !loglikes_reader.Done(); loglikes_reader.Next()) {
        std::string key = loglikes_reader.Key();
        const Matrix<BaseFloat> &loglikes (loglikes_reader.Value());
  
        if (loglikes.NumRows() == 0) {
          KALDI_WARN << "Zero-length utterance: " << key;
          num_fail++;
          continue;
        }
  
        DecodableMatrixScaledMapped decodable(trans_model, loglikes, acoustic_scale);
        decoder.Decode(&decodable);
  
        VectorFst<LatticeArc> decoded;  // linear FST.
  
        if ( (allow_partial || decoder.ReachedFinal())
             && decoder.GetBestPath(&decoded) ) {
          num_success++;
          if (!decoder.ReachedFinal())
            KALDI_WARN << "Decoder did not reach end-state, outputting partial traceback.";
  
          std::vector<int32> alignment;
          std::vector<int32> words;
          LatticeWeight weight;
          frame_count += loglikes.NumRows();
  
          GetLinearSymbolSequence(decoded, &alignment, &words, &weight);
  
          words_writer.Write(key, words);
          if (alignment_writer.IsOpen())
            alignment_writer.Write(key, alignment);
          if (word_syms != NULL) {
            std::cerr << key << ' ';
            for (size_t i = 0; i < words.size(); i++) {
              std::string s = word_syms->Find(words[i]);
              if (s == "")
                KALDI_ERR << "Word-id " << words[i] <<" not in symbol table.";
              std::cerr << s << ' ';
            }
            std::cerr << '
  ';
          }
          BaseFloat like = -weight.Value1() -weight.Value2();
          tot_like += like;
          KALDI_LOG << "Log-like per frame for utterance " << key << " is "
                    << (like / loglikes.NumRows()) << " over "
                    << loglikes.NumRows() << " frames.";
  
        } else {
          num_fail++;
          KALDI_WARN << "Did not successfully decode utterance " << key
                     << ", len = " << loglikes.NumRows();
        }
      }
  
      double elapsed = timer.Elapsed();
      KALDI_LOG << "Time taken [excluding initialization] "<< elapsed
                << "s: real-time factor assuming 100 frames/sec is "
                << (elapsed*100.0/frame_count);
      KALDI_LOG << "Done " << num_success << " utterances, failed for "
                << num_fail;
      KALDI_LOG << "Overall log-likelihood per frame is " << (tot_like/frame_count)
                << " over " << frame_count << " frames.";
  
      delete word_syms;
      delete decode_fst;
      if (num_success != 0) return 0;
      else return 1;
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }