// gmmbin/gmm-global-est-lvtln-trans.cc
  
  // Copyright 2009-2011  Microsoft Corporation;  Saarland University
  //                2014  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 <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 "transform/lvtln.h"
  #include "hmm/posterior.h"
  
  namespace kaldi {
  void AccumulateForUtterance(const Matrix<BaseFloat> &feats,
                              const Posterior &post,
                              const DiagGmm &gmm,
                              FmllrDiagGmmAccs *spk_stats) {
    KALDI_ASSERT(static_cast<int32>(post.size()) == feats.NumRows());
    for (size_t i = 0; i < post.size(); i++) {
      std::vector<int32> gselect(post[i].size());
      Vector<BaseFloat> this_post(post[i].size());
      for (size_t j = 0; j < post[i].size(); j++) {
        int32 g = post[i][j].first;
        BaseFloat weight = post[i][j].second;
        gselect[j] = g;
        this_post(j) = weight;
      }
      spk_stats->AccumulateFromPosteriorsPreselect(gmm, gselect,
                                                   feats.Row(i),
                                                   this_post);
    }
  }
  
  
  }
  
  int main(int argc, char *argv[]) {
    try {
      typedef kaldi::int32 int32;
      using namespace kaldi;
      const char *usage =
          "Estimate linear-VTLN transforms, either per utterance or for "
          "the supplied set of speakers (spk2utt option); this version
  "
          "is for a global diagonal GMM (also known as a UBM).  Reads posteriors
  "
          "indicating Gaussian indexes in the UBM.
  "
          "
  "
          "Usage: gmm-global-est-lvtln-trans [options] <gmm-in> <lvtln-in> "
          "<feature-rspecifier> <gpost-rspecifier> <lvtln-trans-wspecifier> [<warp-wspecifier>]
  "
          "e.g.: gmm-global-est-lvtln-trans 0.ubm 0.lvtln '$feats' ark,s,cs:- ark:1.trans ark:1.warp
  "
          "(where the <gpost-rspecifier> will likely come from gmm-global-get-post or
  "
          "gmm-global-gselect-to-post
  ";
      
      ParseOptions po(usage);
      string spk2utt_rspecifier;
      BaseFloat logdet_scale = 1.0;
      std::string norm_type = "offset";
      po.Register("norm-type", &norm_type, "type of fMLLR applied (\"offset\"|\"none\"|\"diag\")");
      po.Register("spk2utt", &spk2utt_rspecifier, "rspecifier for speaker to "
                  "utterance-list map");
      po.Register("logdet-scale", &logdet_scale, "Scale on log-determinant term in auxiliary function");
  
      po.Read(argc, argv);
  
      if (po.NumArgs() < 5 || po.NumArgs() > 6) {
        po.PrintUsage();
        exit(1);
      }
  
      string
          model_rxfilename = po.GetArg(1),
          lvtln_rxfilename = po.GetArg(2),
          feature_rspecifier = po.GetArg(3),
          post_rspecifier = po.GetArg(4),
          trans_wspecifier = po.GetArg(5),
          warp_wspecifier = po.GetOptArg(6);
  
      DiagGmm gmm;
      ReadKaldiObject(model_rxfilename, &gmm);
      LinearVtln lvtln;
      ReadKaldiObject(lvtln_rxfilename, &lvtln);
  
  
      RandomAccessPosteriorReader post_reader(post_rspecifier);
  
      double tot_lvtln_impr = 0.0, tot_t = 0.0;
  
      BaseFloatMatrixWriter transform_writer(trans_wspecifier);
  
      BaseFloatWriter warp_writer(warp_wspecifier);
  
      std::vector<int32> class_counts(lvtln.NumClasses(), 0);
      int32 num_done = 0, num_no_post = 0, num_other_error = 0;
      if (spk2utt_rspecifier != "") {  // per-speaker adaptation
        SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
        RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
  
        for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
          FmllrDiagGmmAccs spk_stats(lvtln.Dim());
          string spk = spk2utt_reader.Key();
          const vector<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 << "Did not find features for utterance " << utt;
              continue;
            }
            if (!post_reader.HasKey(utt)) {
              KALDI_WARN << "Did not find posteriors for utterance " << utt;
              num_no_post++;
              continue;
            }
            const Matrix<BaseFloat> &feats = feature_reader.Value(utt);
            const Posterior &post = post_reader.Value(utt);
            if (static_cast<int32>(post.size()) != feats.NumRows()) {
              KALDI_WARN << "Posterior vector has wrong size " << post.size()
                         << " vs. " << feats.NumRows();
              num_other_error++;
              continue;
            }
  
            AccumulateForUtterance(feats, post, gmm, &spk_stats);
  
            num_done++;
          }  // end looping over all utterances of the current speaker
  
          BaseFloat impr, spk_tot_t;
          {  // Compute the transform and write it out.
            Matrix<BaseFloat> transform(lvtln.Dim(), lvtln.Dim()+1);
            int32 class_idx;
            lvtln.ComputeTransform(spk_stats,
                                   norm_type,
                                   logdet_scale,
                                   &transform,
                                   &class_idx,
                                   NULL,
                                   &impr,
                                   &spk_tot_t);
            class_counts[class_idx]++;
            transform_writer.Write(spk, transform);
            if (warp_wspecifier != "")
              warp_writer.Write(spk, lvtln.GetWarp(class_idx));
          }
          KALDI_LOG << "For speaker " << spk << ", auxf-impr from LVTLN is "
                    << (impr/spk_tot_t) << ", over " << spk_tot_t << " frames.";
          tot_lvtln_impr += impr;
          tot_t += spk_tot_t;
        }  // end looping over speakers
      } else {  // per-utterance adaptation
        SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
        for (; !feature_reader.Done(); feature_reader.Next()) {
          string utt = feature_reader.Key();
          if (!post_reader.HasKey(utt)) {
            KALDI_WARN << "Did not find posterior for utterance "
                       << utt;
            num_no_post++;
            continue;
          }
          const Matrix<BaseFloat> &feats = feature_reader.Value();
          const Posterior &post = post_reader.Value(utt);
  
          if (static_cast<int32>(post.size()) != feats.NumRows()) {
            KALDI_WARN << "Posterior has wrong size " << post.size()
                << " vs. " << feats.NumRows();
            num_other_error++;
            continue;
          }
          num_done++;
  
          FmllrDiagGmmAccs spk_stats(lvtln.Dim());
  
          AccumulateForUtterance(feats, post, gmm,
                                 &spk_stats);
          BaseFloat impr, utt_tot_t = spk_stats.beta_;
          {  // Compute the transform and write it out.
            Matrix<BaseFloat> transform(lvtln.Dim(), lvtln.Dim()+1);
            int32 class_idx;
            lvtln.ComputeTransform(spk_stats,
                                   norm_type,
                                   logdet_scale,
                                   &transform,
                                   &class_idx,
                                   NULL,
                                   &impr,
                                   &utt_tot_t);
            class_counts[class_idx]++;
            transform_writer.Write(utt, transform);
            if (warp_wspecifier != "")
              warp_writer.Write(utt, lvtln.GetWarp(class_idx));
          }
  
          KALDI_LOG << "For utterance " << utt << ", auxf-impr from LVTLN is "
                    << (impr/utt_tot_t) << ", over " << utt_tot_t << " frames.";
          tot_lvtln_impr += impr;
          tot_t += utt_tot_t;
        }
      }
  
      {
        std::ostringstream s;
        for (size_t i = 0; i < class_counts.size(); i++)
          s << ' ' << class_counts[i];
        KALDI_LOG << "Distribution of classes is: " << s.str();
      }
  
      KALDI_LOG << "Done " << num_done << " files, " << num_no_post
                << " with no posteriors, " << num_other_error << " with other errors.";
      KALDI_LOG << "Overall LVTLN auxf impr per frame is "
                << (tot_lvtln_impr / tot_t) << " over " << tot_t << " frames.";
      return (num_done == 0 ? 1 : 0);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }