// gmmbin/gmm-decode-biglm-faster.cc
  
  // Copyright 2009-2011  Gilles Boulianne  Microsoft Corporation
  
  // 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 "gmm/am-diag-gmm.h"
  #include "hmm/transition-model.h"
  #include "fstext/fstext-lib.h"
  #include "decoder/biglm-faster-decoder.h"
  #include "gmm/decodable-am-diag-gmm.h"
  #include "base/timer.h"
  
  namespace kaldi {
  
  fst::Fst<fst::StdArc> *ReadNetwork(std::string filename) {
    // read decoding network FST
    Input ki(filename); // use ki.Stream() instead of is.
    if (!ki.Stream().good()) KALDI_ERR << "Could not open decoding-graph FST "
                                        << filename;
  
    fst::FstHeader hdr;
    if (!hdr.Read(ki.Stream(), "<unknown>")) {
      KALDI_ERR << "Reading FST: error reading FST header.";
    }
    if (hdr.ArcType() != fst::StdArc::Type()) {
      KALDI_ERR << "FST with arc type " << hdr.ArcType() << " not supported.";
    }
    fst::FstReadOptions ropts("<unspecified>", &hdr);
  
    fst::Fst<fst::StdArc> *decode_fst = NULL;
  
    if (hdr.FstType() == "vector") {
      decode_fst = fst::VectorFst<fst::StdArc>::Read(ki.Stream(), ropts);
    } else if (hdr.FstType() == "const") {
      decode_fst = fst::ConstFst<fst::StdArc>::Read(ki.Stream(), ropts);
    } else {
      KALDI_ERR << "Reading FST: unsupported FST type: " << hdr.FstType();
    }
    if (decode_fst == NULL) { // fst code will warn.
      KALDI_ERR << "Error reading FST (after reading header).";
      return NULL;
    } else {
      return decode_fst;
    }
  }
  
  }
  
  
  
  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;
      using fst::ReadFstKaldi;
  
      const char *usage =
          "Decode features using GMM-based model.
  "
          "User supplies LM used to generate decoding graph, and desired LM;
  "
          "this decoder applies the difference during decoding
  "
          "Usage:  gmm-decode-biglm-faster [options] model-in fst-in oldlm-fst-in newlm-fst-in features-rspecifier words-wspecifier [alignments-wspecifier [lattice-wspecifier]]
  ";
      ParseOptions po(usage);
      bool allow_partial = true;    
      BaseFloat acoustic_scale = 0.1;
      
      std::string word_syms_filename;
      BiglmFasterDecoderOptions decoder_opts;
      decoder_opts.Register(&po, true);  // true == include obscure settings.
      po.Register("acoustic-scale", &acoustic_scale,
                  "Scaling factor for acoustic likelihoods");
      po.Register("word-symbol-table", &word_syms_filename,
                  "Symbol table for words [for debug output]");
      po.Register("allow-partial", &allow_partial,
                  "Produce output even when final state was not reached");
  
      po.Read(argc, argv);
  
      if (po.NumArgs() < 6 || po.NumArgs() > 8) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string model_rxfilename = po.GetArg(1),
          fst_rxfilename = po.GetArg(2),
          old_lm_fst_rxfilename = po.GetArg(3),
          new_lm_fst_rxfilename = po.GetArg(4),
          feature_rspecifier = po.GetArg(5),
          words_wspecifier = po.GetArg(6),
          alignment_wspecifier = po.GetOptArg(7),
          lattice_wspecifier = po.GetOptArg(8);
  
      TransitionModel trans_model;
      AmDiagGmm am_gmm;
      {
        bool binary;
        Input ki(model_rxfilename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_gmm.Read(ki.Stream(), binary);
      }
  
      Int32VectorWriter words_writer(words_wspecifier);
  
      Int32VectorWriter alignment_writer(alignment_wspecifier);
  
      CompactLatticeWriter clat_writer(lattice_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 feature_reader(feature_rspecifier);
  
  
      // It's important that we initialize decode_fst after feature_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.
      Fst<StdArc> *decode_fst = ReadNetwork(fst_rxfilename);
      
      VectorFst<StdArc> *old_lm_fst = ReadFstKaldi(old_lm_fst_rxfilename);
      ApplyProbabilityScale(-1.0, old_lm_fst); // Negate old LM probs...
      
      VectorFst<StdArc> *new_lm_fst = ReadFstKaldi(new_lm_fst_rxfilename);
  
  
      BaseFloat tot_like = 0.0;
      kaldi::int64 frame_count = 0;
      int num_success = 0, num_fail = 0;
  
      Timer timer;
      
      for (; !feature_reader.Done(); feature_reader.Next()) {
        std::string key = feature_reader.Key();
        Matrix<BaseFloat> features (feature_reader.Value());
        feature_reader.FreeCurrent();
        if (features.NumRows() == 0) {
          KALDI_WARN << "Zero-length utterance: " << key;
          num_fail++;
          continue;
        }
        fst::BackoffDeterministicOnDemandFst<StdArc> old_lm_dfst(*old_lm_fst);
        fst::BackoffDeterministicOnDemandFst<StdArc> new_lm_dfst(*new_lm_fst);
        fst::ComposeDeterministicOnDemandFst<StdArc> compose_dfst(&old_lm_dfst,
                                                                  &new_lm_dfst);
        fst::CacheDeterministicOnDemandFst<StdArc> cache_dfst(&compose_dfst);
        
        BiglmFasterDecoder decoder(*decode_fst, decoder_opts, &cache_dfst);
        
        DecodableAmDiagGmmScaled gmm_decodable(am_gmm, trans_model, features,
                                               acoustic_scale);
        decoder.Decode(&gmm_decodable);
  
        std::cerr << "Length of file is "<<features.NumRows()<<'
  ';
  
        fst::VectorFst<LatticeArc> decoded;  // linear FST.
  
        if ( (allow_partial || decoder.ReachedFinal())
             && decoder.GetBestPath(&decoded) ) {
          if (!decoder.ReachedFinal())
            KALDI_WARN << "Decoder did not reach end-state, "
                       << "outputting partial traceback since --allow-partial=true";
          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 += features.NumRows();
  
          GetLinearSymbolSequence(decoded, &alignment, &words, &weight);
  
          words_writer.Write(key, words);
          if (alignment_writer.IsOpen())
            alignment_writer.Write(key, alignment);
            
          if (lattice_wspecifier != "") {
            if (acoustic_scale != 0.0) // We'll write the lattice without acoustic scaling
              fst::ScaleLattice(fst::AcousticLatticeScale(1.0 / acoustic_scale), &decoded);
            fst::VectorFst<CompactLatticeArc> clat;
            ConvertLattice(decoded, &clat, true);
            clat_writer.Write(key, clat);
          }
            
          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 / features.NumRows()) << " over "
                    << features.NumRows() << " frames.";
          KALDI_VLOG(2) << "Cost for utterance " << key << " is "
                        << weight.Value1() << " + " << weight.Value2();
        } else {
          num_fail++;
          KALDI_WARN << "Did not successfully decode utterance " << key
                     << ", len = " << features.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;
      delete old_lm_fst;
      delete new_lm_fst;
      return (num_success != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }