// gmmbin/gmm-est-fmllr-raw-gpost.cc
  
  // Copyright 2013  Johns Hopkins University (author: Daniel Povey)
  //           2014  Guoguo Chen
  
  // 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 "transform/fmllr-raw.h"
  #include "gmm/am-diag-gmm.h"
  #include "hmm/transition-model.h"
  #include "util/common-utils.h"
  #include "hmm/posterior.h"
  
  namespace kaldi {
  
  
  void AccStatsForUtterance(const TransitionModel &trans_model,
                            const AmDiagGmm &am_gmm,
                            const GaussPost &gpost,
                            const Matrix<BaseFloat> &feats,
                            FmllrRawAccs *accs) {
    for (size_t t = 0; t < gpost.size(); t++) {
      for (size_t i = 0; i < gpost[t].size(); i++) {
        int32 pdf = gpost[t][i].first;
        const Vector<BaseFloat> &posterior(gpost[t][i].second);      
        accs->AccumulateFromPosteriors(am_gmm.GetPdf(pdf),
                                       feats.Row(t), posterior);
      }
    }
  }
  
  
  }
  
  int main(int argc, char *argv[]) {
    try {
      typedef kaldi::int32 int32;
      using namespace kaldi;
      const char *usage =
          "Estimate fMLLR transforms in the space before splicing and linear transforms
  "
          "such as LDA+MLLT, but using models in the space transformed by these transforms
  "
          "Requires the original spliced features, and the full LDA+MLLT (or similar) matrix
  "
          "including the 'rejected' rows (see the program get-full-lda-mat).  Reads in
  "
          "Gaussian-level posteriors.
  "
          "Usage: gmm-est-fmllr-raw-gpost [options] <model-in> <full-lda-mat-in> "
          "<feature-rspecifier> <gpost-rspecifier> <transform-wspecifier>
  ";
  
  
      int32 raw_feat_dim = 13;
      ParseOptions po(usage);
      FmllrRawOptions opts;
      std::string spk2utt_rspecifier;
      po.Register("spk2utt", &spk2utt_rspecifier, "rspecifier for speaker to "
                  "utterance-list map");
      po.Register("raw-feat-dim", &raw_feat_dim, "Dimension of raw features "
                  "prior to splicing");
      opts.Register(&po);
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 5) {
        po.PrintUsage();
        exit(1);
      }
      
      std::string model_rxfilename = po.GetArg(1),
          full_lda_mat_rxfilename = po.GetArg(2),
          feature_rspecifier = po.GetArg(3),
          gpost_rspecifier = po.GetArg(4),
          transform_wspecifier = po.GetArg(5);
  
      AmDiagGmm am_gmm;
      TransitionModel trans_model;
      {
        bool binary;
        Input ki(model_rxfilename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_gmm.Read(ki.Stream(), binary);
      }
  
      Matrix<BaseFloat> full_lda_mat;
      ReadKaldiObject(full_lda_mat_rxfilename, &full_lda_mat);
      
      RandomAccessGaussPostReader gpost_reader(gpost_rspecifier);
      BaseFloatMatrixWriter transform_writer(transform_wspecifier);
      
      double tot_auxf_impr = 0.0, tot_count = 0.0;
      
      int32 num_done = 0, num_err = 0;
      if (!spk2utt_rspecifier.empty()) { // Adapting per speaker
        SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
        RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
        
        for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
          FmllrRawAccs accs(raw_feat_dim, am_gmm.Dim(), full_lda_mat);
          std::string spk = spk2utt_reader.Key();
          const std::vector<std::string> &uttlist = spk2utt_reader.Value();
          for (size_t i = 0; i < uttlist.size(); i++) {
            std::string utt = uttlist[i];
            if (!feature_reader.HasKey(utt)) {
              KALDI_WARN << "Features not found for utterance " << utt;
              num_err++;
              continue;
            }
            if (!gpost_reader.HasKey(utt)) {
              KALDI_WARN << "Gaussian-level posteriors not found for utterance " << utt;
              num_err++;
              continue;
            }
            const Matrix<BaseFloat> &feats = feature_reader.Value(utt);
            const GaussPost &gpost = gpost_reader.Value(utt);
            if (static_cast<int32>(gpost.size()) != feats.NumRows()) {
              KALDI_WARN << "Size mismatch between gposteriors " << gpost.size()
                         << " and features " << feats.NumRows();
              num_err++;
              continue;
            }
  
            AccStatsForUtterance(trans_model, am_gmm, gpost, feats, &accs);
            num_done++;
          }
          
          BaseFloat auxf_impr, count;
          {
            Matrix<BaseFloat> transform(raw_feat_dim, raw_feat_dim + 1);
            transform.SetUnit();
            accs.Update(opts, &transform, &auxf_impr, &count);
            transform_writer.Write(spk, transform);
          }
          KALDI_LOG << "For speaker " << spk << ", auxf-impr from raw fMLLR is "
                    << (auxf_impr/count) << " over " << count << " frames.";
          tot_auxf_impr += auxf_impr;
          tot_count += count;
        }
      } else {  // --spk2utt option not given -> adapt per utterance.
        SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
        for (; !feature_reader.Done(); feature_reader.Next()) {
          std::string utt = feature_reader.Key();
          if (!gpost_reader.HasKey(utt)) {
            KALDI_WARN << "Gaussian-level posteriors not found for utterance " << utt;
            num_err++;
            continue;
          }
          const Matrix<BaseFloat> &feats = feature_reader.Value();
          const GaussPost &gpost = gpost_reader.Value(utt);
  
          if (static_cast<int32>(gpost.size()) != feats.NumRows()) {
            KALDI_WARN << "Size mismatch between posteriors " << gpost.size()
                       << " and features " << feats.NumRows();
            num_err++;
            continue;
          }
  
          FmllrRawAccs accs(raw_feat_dim, am_gmm.Dim(), full_lda_mat);
  
          AccStatsForUtterance(trans_model, am_gmm, gpost, feats, &accs);
          
          BaseFloat auxf_impr, count;        
          {
            Matrix<BaseFloat> transform(raw_feat_dim, raw_feat_dim + 1);
            transform.SetUnit();
            accs.Update(opts, &transform, &auxf_impr, &count);
            transform_writer.Write(utt, transform);
          }
          KALDI_LOG << "For utterance " << utt << ", auxf-impr from raw fMLLR is "
                    << (auxf_impr/count) << " over " << count << " frames.";
          tot_auxf_impr += auxf_impr;
          tot_count += count;
          num_done++;
        }
      }
  
      KALDI_LOG << "Processed " << num_done << " utterances, "
                << num_err << " had errors.";
      KALDI_LOG << "Overall raw-fMLLR auxf impr per frame is "
                << (tot_auxf_impr / tot_count) << " over " << tot_count
                << " frames.";
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }