// sgmm2bin/sgmm2-align-compiled.cc
  
  // Copyright 2009-2012  Microsoft Corporation;  Saarland University
  //           2012-2014 Johns Hopkins University (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 "sgmm2/am-sgmm2.h"
  #include "hmm/transition-model.h"
  #include "hmm/hmm-utils.h"
  #include "fstext/fstext-lib.h"
  #include "decoder/decoder-wrappers.h"
  #include "decoder/training-graph-compiler.h"
  #include "sgmm2/decodable-am-sgmm2.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 =
          "Align features given [SGMM-based] models.
  "
          "Usage: sgmm2-align-compiled [options] <model-in> <graphs-rspecifier> "
          "<feature-rspecifier> <alignments-wspecifier>
  "
          "e.g.: sgmm2-align-compiled 1.mdl ark:graphs.fsts scp:train.scp ark:1.ali
  ";
  
      ParseOptions po(usage);
      bool binary = true;
      AlignConfig align_config;
      BaseFloat acoustic_scale = 1.0;
      BaseFloat transition_scale = 1.0;
      BaseFloat self_loop_scale = 1.0;
      BaseFloat log_prune = 5.0;
      std::string gselect_rspecifier, spkvecs_rspecifier, utt2spk_rspecifier;
      std::string per_frame_acwt_wspecifier;
  
      align_config.Register(&po);
      po.Register("binary", &binary, "Write output in binary mode");
      po.Register("log-prune", &log_prune, "Pruning beam used to reduce number "
                  "of exp() evaluations.");
      po.Register("spk-vecs", &spkvecs_rspecifier, "Speaker vectors (rspecifier)");
      po.Register("utt2spk", &utt2spk_rspecifier,
                  "rspecifier for utterance to speaker map");
      po.Register("acoustic-scale", &acoustic_scale, "Scaling factor for acoustic "
                  "likelihoods");
      po.Register("transition-scale", &transition_scale, "Scaling factor for "
                  "some transition probabilities [see also self-loop-scale].");
      po.Register("self-loop-scale", &self_loop_scale, "Scaling factor for "
                  "self-loop versus non-self-loop probability mass [controls "
                  "most transition probabilities.]");
      po.Register("write-per-frame-acoustic-loglikes", &per_frame_acwt_wspecifier,
                  "Wspecifier for table of vectors containing the acoustic log-likelihoods "
                  "per frame for each utterance. E.g. ark:foo/per_frame_logprobs.1.ark");
      po.Register("gselect", &gselect_rspecifier, "Precomputed Gaussian indices "
                  "(rspecifier)");
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 4) {
        po.PrintUsage();
        exit(1);
      }
  
      if (gselect_rspecifier == "")
        KALDI_ERR << "--gselect option is mandatory.";
  
      std::string model_in_filename = po.GetArg(1),
          fst_rspecifier = po.GetArg(2),
          feature_rspecifier = po.GetArg(3),
          alignment_wspecifier = po.GetArg(4);
  
      TransitionModel trans_model;
      AmSgmm2 am_sgmm;
      {
        bool binary;
        Input ki(model_in_filename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_sgmm.Read(ki.Stream(), binary);
      }
  
      SequentialTableReader<fst::VectorFstHolder> fst_reader(fst_rspecifier);
      RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
      RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
  
      RandomAccessBaseFloatVectorReaderMapped spkvecs_reader(spkvecs_rspecifier,
                                                             utt2spk_rspecifier);
  
      Int32VectorWriter alignment_writer(alignment_wspecifier);
      BaseFloatVectorWriter per_frame_acwt_writer(per_frame_acwt_wspecifier);
  
      int num_done = 0, num_err = 0, num_retry = 0;
      double tot_like = 0.0;
      kaldi::int64 frame_count = 0;
  
      for (; !fst_reader.Done(); fst_reader.Next()) {
        std::string utt = fst_reader.Key();
        if (!feature_reader.HasKey(utt)) {
          KALDI_WARN << "No feature found for utterance " << utt;
          num_err++;
          continue;
        }
        VectorFst<StdArc> decode_fst(fst_reader.Value());
        // stops copy-on-write of the fst by deleting the fst inside the reader,
        // since we're about to mutate the fst by adding transition probs.
        fst_reader.FreeCurrent();
  
        const Matrix<BaseFloat> &features = feature_reader.Value(utt);
        if (features.NumRows() == 0) {
          KALDI_WARN << "Zero-length utterance: " << utt;
          num_err++;
          continue;
        }
  
        Sgmm2PerSpkDerivedVars spk_vars;
        if (spkvecs_reader.IsOpen()) {
          if (spkvecs_reader.HasKey(utt)) {
            spk_vars.SetSpeakerVector(spkvecs_reader.Value(utt));
            am_sgmm.ComputePerSpkDerivedVars(&spk_vars);
          } else {
            KALDI_WARN << "Cannot find speaker vector for " << utt;
            num_err++;
            continue;
          }
        }  // else spk_vars is "empty"
  
        if (!gselect_reader.HasKey(utt)
            && gselect_reader.Value(utt).size() != features.NumRows()) {
          KALDI_WARN << "No Gaussian-selection info available for utterance "
                     << utt << " (or wrong size)";
          num_err++;
        }
        const std::vector<std::vector<int32> > &gselect =
            gselect_reader.Value(utt);
  
        {  // Add transition-probs to the FST.
          std::vector<int32> disambig_syms;  // empty.
          AddTransitionProbs(trans_model, disambig_syms,
                             transition_scale, self_loop_scale,
                             &decode_fst);
        }
  
        DecodableAmSgmm2Scaled sgmm_decodable(am_sgmm, trans_model, features, gselect,
                                              log_prune, acoustic_scale, &spk_vars);
  
        AlignUtteranceWrapper(align_config, utt,
                              acoustic_scale, &decode_fst, &sgmm_decodable,
                              &alignment_writer, NULL,
                              &num_done, &num_err, &num_retry,
                              &tot_like, &frame_count, &per_frame_acwt_writer);
  
      }
  
      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;
    }
  }