// gmmbin/gmm-est-regtree-fmllr.cc
  
  // Copyright 2009-2011  Saarland University;  Microsoft Corporation
  //                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 <string>
  using std::string;
  #include <vector>
  using std::vector;
  
  #include "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "gmm/am-diag-gmm.h"
  #include "hmm/transition-model.h"
  #include "hmm/posterior.h"
  #include "transform/regtree-fmllr-diag-gmm.h"
  
  int main(int argc, char *argv[]) {
    try {
      typedef kaldi::int32 int32;
      using namespace kaldi;
      const char *usage =
          "Compute FMLLR transforms per-utterance (default) or per-speaker for "
          "the supplied set of speakers (spk2utt option).  Note: writes RegtreeFmllrDiagGmm objects
  "
          "Usage: gmm-est-regtree-fmllr  [options] <model-in> <feature-rspecifier> "
          "<posteriors-rspecifier> <regression-tree> <transforms-wspecifier>
  ";
  
      ParseOptions po(usage);
      string spk2utt_rspecifier;
      bool binary = true;
      po.Register("spk2utt", &spk2utt_rspecifier, "rspecifier for speaker to "
                  "utterance-list map");
      po.Register("binary", &binary, "Write output in binary mode");
      // register other modules
      RegtreeFmllrOptions opts;
      opts.Register(&po);
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 5) {
        po.PrintUsage();
        exit(1);
      }
  
      string model_filename = po.GetArg(1),
          feature_rspecifier = po.GetArg(2),
          posteriors_rspecifier = po.GetArg(3),
          regtree_filename = po.GetArg(4),
          xforms_wspecifier = po.GetArg(5);
  
      RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
      RegtreeFmllrDiagGmmWriter fmllr_writer(xforms_wspecifier);
  
      AmDiagGmm am_gmm;
      TransitionModel trans_model;
      {
        bool binary;
        Input ki(model_filename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_gmm.Read(ki.Stream(), binary);
      }
      RegressionTree regtree;
      {
        bool binary;
        Input in(regtree_filename, &binary);
        regtree.Read(in.Stream(), binary, am_gmm);
      }
  
      RegtreeFmllrDiagGmm fmllr_xforms;
      RegtreeFmllrDiagGmmAccs fmllr_accs;
      fmllr_accs.Init(regtree.NumBaseclasses(), am_gmm.Dim());
  
      double tot_like = 0.0, tot_t = 0;
  
      int32 num_done = 0, num_no_posterior = 0, num_other_error = 0;
      double tot_objf_impr = 0.0, tot_t_objf = 0.0;
      if (spk2utt_rspecifier != "") {  // per-speaker adaptation
        SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
        RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
        for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
          string spk = spk2utt_reader.Key();
          fmllr_accs.SetZero();
          const vector<string> &uttlist = spk2utt_reader.Value();
          for (vector<string>::const_iterator utt_itr = uttlist.begin(),
              itr_end = uttlist.end(); utt_itr != itr_end; ++utt_itr) {
            if (!feature_reader.HasKey(*utt_itr)) {
              KALDI_WARN << "Did not find features for utterance " << *utt_itr;
              continue;
            }
            if (!posteriors_reader.HasKey(*utt_itr)) {
              KALDI_WARN << "Did not find posteriors for utterance "
                  << *utt_itr;
              num_no_posterior++;
              continue;
            }
            const Matrix<BaseFloat> &feats = feature_reader.Value(*utt_itr);
            const Posterior &posterior = posteriors_reader.Value(*utt_itr);
            if (static_cast<int32>(posterior.size()) != feats.NumRows()) {
              KALDI_WARN << "Posteriors has wrong size " << (posterior.size())
                  << " vs. " << (feats.NumRows());
              num_other_error++;
              continue;
            }
  
            BaseFloat file_like = 0.0, file_t = 0.0;
            Posterior pdf_posterior;
            ConvertPosteriorToPdfs(trans_model, posterior, &pdf_posterior);
            for (size_t i = 0; i < posterior.size(); i++) {
              for (size_t j = 0; j < pdf_posterior[i].size(); j++) {
                int32 pdf_id = pdf_posterior[i][j].first;
                BaseFloat prob = pdf_posterior[i][j].second;
                file_like += fmllr_accs.AccumulateForGmm(regtree, am_gmm,
                                                         feats.Row(i), pdf_id,
                                                         prob);
                file_t += prob;
              }
            }
            KALDI_VLOG(2) << "Average like for this file is " << (file_like/file_t)
                          << " over " << file_t << " frames.";
            tot_like += file_like;
            tot_t += file_t;
            num_done++;
            if (num_done % 10 == 0)
              KALDI_VLOG(1) << "Avg like per frame so far is "
                            << (tot_like / tot_t);
          }  // end looping over all utterances of the current speaker
          BaseFloat objf_impr, t;
          fmllr_accs.Update(regtree, opts, &fmllr_xforms, &objf_impr, &t);
          KALDI_LOG << "fMLLR objf improvement for speaker " << spk << " is "
                    << (objf_impr/(t+1.0e-10)) << " per frame over " << t
                    << " frames.";
          tot_objf_impr += objf_impr;
          tot_t_objf += t;
          fmllr_writer.Write(spk, fmllr_xforms);
        }  // end looping over speakers
      } else {  // per-utterance adaptation
        SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
        for (; !feature_reader.Done(); feature_reader.Next()) {
          string key = feature_reader.Key();
          if (!posteriors_reader.HasKey(key)) {
            KALDI_WARN << "Did not find posteriors for utterance "
                << key;
            num_no_posterior++;
            continue;
          }
          const Matrix<BaseFloat> &feats = feature_reader.Value();
          const Posterior &posterior = posteriors_reader.Value(key);
  
          if (static_cast<int32>(posterior.size()) != feats.NumRows()) {
            KALDI_WARN << "Posteriors has wrong size " << (posterior.size())
                << " vs. " << (feats.NumRows());
            num_other_error++;
            continue;
          }
  
          num_done++;
          BaseFloat file_like = 0.0, file_t = 0.0;
          fmllr_accs.SetZero();
          Posterior pdf_posterior;
          ConvertPosteriorToPdfs(trans_model, posterior, &pdf_posterior);
          for (size_t i = 0; i < posterior.size(); i++) {
            for (size_t j = 0; j < pdf_posterior[i].size(); j++) {
              int32 pdf_id = pdf_posterior[i][j].first;
              BaseFloat prob = pdf_posterior[i][j].second;
              file_like += fmllr_accs.AccumulateForGmm(regtree, am_gmm,
                                                       feats.Row(i), pdf_id,
                                                       prob);
              file_t += prob;
            }
          }
          KALDI_VLOG(2) << "Average like for this file is " << (file_like/file_t)
                        << " over " << file_t << " frames.";
          tot_like += file_like;
          tot_t += file_t;
          if (num_done % 10 == 0)
            KALDI_VLOG(1) << "Avg like per frame so far is "
                          << (tot_like / tot_t);
          BaseFloat objf_impr, t;
          fmllr_accs.Update(regtree, opts, &fmllr_xforms, &objf_impr, &t);
          KALDI_LOG << "fMLLR objf improvement for utterance " << key << " is "
                    << (objf_impr/(t+1.0e-10)) << " per frame over " << t
                    << " frames.";
          tot_objf_impr += objf_impr;
          tot_t_objf += t;
          fmllr_writer.Write(feature_reader.Key(), fmllr_xforms);
        }
      }
      KALDI_LOG << "Done " << num_done << " files, " << num_no_posterior
                << " with no posteriors, " << num_other_error
                << " with other errors.";
      KALDI_LOG << "Overall objf improvement from MLLR is " << (tot_objf_impr/tot_t_objf)
                << " per frame " << " over " << tot_t_objf << " frames.";
      KALDI_LOG << "Overall acoustic likelihood was " << (tot_like/tot_t)
                << " over " << tot_t << " frames.";
      return 0;
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }