// gmmbin/gmm-global-est-fmllr.cc
  
  // Copyright 2009-2011  Microsoft Corporation;  Saarland University
  
  // 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/fmllr-diag-gmm.h"
  
  namespace kaldi {
  bool AccumulateForUtterance(const Matrix<BaseFloat> &feats,
                              const DiagGmm &gmm,
                              const std::string &key,
                              RandomAccessBaseFloatVectorReader *weights_reader,
                              RandomAccessInt32VectorVectorReader *gselect_reader,
                              AccumFullGmm *fullcov_stats) {
    Vector<BaseFloat> weights;
    if (weights_reader->IsOpen()) {
      if (!weights_reader->HasKey(key)) {
        KALDI_WARN << "No weights present for utterance " << key;
        return false;
      }
      weights = weights_reader->Value(key);
    }
    int32 num_frames = feats.NumRows();
    if (gselect_reader->IsOpen()) {
      if (!gselect_reader->HasKey(key)) {
        KALDI_WARN << "No gselect information present for utterance " << key;
        return false;
      }
      const std::vector<std::vector<int32> > &gselect(gselect_reader->Value(key));
      if (gselect.size() != num_frames) {
        KALDI_WARN << "gselect information has wrong size for utterance " << key;
        return false;
      }
      for (int32 t = 0; t < num_frames; t++) {
        const std::vector<int32> &this_gselect(gselect[t]);
        BaseFloat weight = (weights.Dim() != 0 ? weights(t) : 1.0);
        if (weight != 0.0) {
          Vector<BaseFloat> post(this_gselect.size());
          gmm.LogLikelihoodsPreselect(feats.Row(t), this_gselect, &post);
          post.ApplySoftMax(); // get posteriors.
          post.Scale(weight); // scale by the weight for this frame.
          for (size_t i = 0; i < this_gselect.size(); i++)
            fullcov_stats->AccumulateForComponent(feats.Row(t),
                                                  this_gselect[i], post(i));
        }
      }
    } else {
      for (int32 t = 0; t < num_frames; t++) {
        BaseFloat weight = (weights.Dim() != 0 ? weights(t) : 1.0);
        if (weight != 0.0)
          fullcov_stats->AccumulateFromDiag(gmm, feats.Row(t), weight);
      }
    }
    return true;
  }
        
  
  }
  
  int main(int argc, char *argv[]) {
    try {
      typedef kaldi::int32 int32;
      using namespace kaldi;
      const char *usage =
          "Estimate global fMLLR transforms, either per utterance or for the supplied
  "
          "set of speakers (spk2utt option).  Reads features, and (with --weights option)
  "
          "weights for each frame (also see --gselect option)
  "
          "Usage: gmm-global-est-fmllr [options] <gmm-in> <feature-rspecifier> <transform-wspecifier>
  ";
  
      ParseOptions po(usage);
      FmllrOptions fmllr_opts;
      string spk2utt_rspecifier, gselect_rspecifier, weights_rspecifier,
          alignment_model;
          
  
      po.Register("spk2utt", &spk2utt_rspecifier, "rspecifier for speaker to "
                  "utterance-list map");
      po.Register("gselect", &gselect_rspecifier, "rspecifier for gselect objects "
                  "to limit the #Gaussians accessed on each frame.");
      po.Register("weights", &weights_rspecifier, "rspecifier for a vector of floats "
                  "for each utterance, that's a per-frame weight.");
      po.Register("align-model", &alignment_model, "rxfilename for a model in the "
                  "speaker-independent space, to get Gaussian alignments from");
      
      fmllr_opts.Register(&po);
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 3) {
        po.PrintUsage();
        exit(1);
      }
  
      string gmm_rxfilename = po.GetArg(1),
          feature_rspecifier = po.GetArg(2),
          trans_wspecifier = po.GetArg(3);
  
      DiagGmm gmm;
      ReadKaldiObject(gmm_rxfilename, &gmm);
      DiagGmm ali_gmm_read;
      if (alignment_model != "") {
        bool binary;
        Input ki(gmm_rxfilename, &binary);
        ali_gmm_read.Read(ki.Stream(), binary);
      }
      DiagGmm &ali_gmm = (alignment_model != "" ? ali_gmm_read : gmm);
      
      RandomAccessBaseFloatVectorReader weights_reader(weights_rspecifier);
      RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
  
      double tot_impr = 0.0, tot_t = 0.0;
  
      BaseFloatMatrixWriter transform_writer(trans_wspecifier);
  
      int32 num_done = 0, num_err = 0;
  
      if (spk2utt_rspecifier != "") {  // per-speaker adaptation
        SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
        RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
  
        for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
          AccumFullGmm fullcov_stats(gmm.NumGauss(), gmm.Dim(), kGmmAll);
          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;
            }
            const Matrix<BaseFloat> &feats = feature_reader.Value(utt);
  
            if (AccumulateForUtterance(feats, ali_gmm, utt, &weights_reader,
                                       &gselect_reader, &fullcov_stats)) num_done++;
            else num_err++;
          }  // end looping over all utterances of the current speaker
          
          BaseFloat impr, spk_tot_t;
          {  // Compute the transform and write it out.
            Matrix<BaseFloat> transform(gmm.Dim(), gmm.Dim()+1);
            transform.SetUnit();
            FmllrDiagGmmAccs spk_stats(gmm, fullcov_stats);
            spk_stats.Update(fmllr_opts, &transform, &impr, &spk_tot_t);
            transform_writer.Write(spk, transform);
          }
          KALDI_LOG << "For speaker " << spk << ", auxf-impr from fMLLR is "
                    << (impr/spk_tot_t) << ", over " << spk_tot_t << " frames.";
          tot_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();
  
          const Matrix<BaseFloat> &feats = feature_reader.Value();
  
          AccumFullGmm fullcov_stats(gmm.NumGauss(), gmm.Dim(), kGmmAll);
  
          if (AccumulateForUtterance(feats, ali_gmm, utt, &weights_reader,
                                     &gselect_reader, &fullcov_stats)) {
            BaseFloat impr, utt_tot_t;
            {  // Compute the transform and write it out.
              Matrix<BaseFloat> transform(gmm.Dim(), gmm.Dim()+1);
              transform.SetUnit();
              FmllrDiagGmmAccs spk_stats(gmm, fullcov_stats);
              spk_stats.Update(fmllr_opts, &transform, &impr, &utt_tot_t);
              transform_writer.Write(utt, transform);
            }
            KALDI_LOG << "For utterance " << utt << ", auxf-impr from fMLLR is "
                      << (impr/utt_tot_t) << ", over " << utt_tot_t << " frames.";
            tot_impr += impr;
            tot_t += utt_tot_t;
            num_done++;
          } else num_err++;
          
        }
      }
  
      KALDI_LOG << "Done " << num_done << " files, " << num_err
                << " with errors.";
      KALDI_LOG << "Overall fMLLR auxf impr per frame is "
                << (tot_impr / tot_t) << " over " << tot_t << " frames.";
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }