// sgmm2bin/sgmm2-est-fmllr.cc
  
  // Copyright 2009-2012  Saarland University   Microsoft Corporation  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 <string>
  using std::string;
  #include <vector>
  using std::vector;
  
  #include "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "sgmm2/am-sgmm2.h"
  #include "sgmm2/fmllr-sgmm2.h"
  #include "hmm/transition-model.h"
  #include "hmm/posterior.h"
  
  namespace kaldi {
  
  void AccumulateForUtterance(const Matrix<BaseFloat> &feats,
                              const Matrix<BaseFloat> &transformed_feats, // if already fMLLR
                              const std::vector<std::vector<int32> > &gselect,
                              const Posterior &post,
                              const TransitionModel &trans_model,
                              const AmSgmm2 &am_sgmm,
                              BaseFloat logdet,
                              Sgmm2PerSpkDerivedVars *spk_vars,
                              FmllrSgmm2Accs *spk_stats) {
    kaldi::Sgmm2PerFrameDerivedVars per_frame_vars;
  
    Posterior pdf_post;
    ConvertPosteriorToPdfs(trans_model, post, &pdf_post);
    for (size_t t = 0; t < post.size(); t++) {
      // per-frame vars only used for computing posteriors... use the
      // transformed feats for this, if available.
      am_sgmm.ComputePerFrameVars(transformed_feats.Row(t), gselect[t],
                                  *spk_vars, &per_frame_vars);
      
  
      for (size_t j = 0; j < pdf_post[t].size(); j++) {
        int32 pdf_id = pdf_post[t][j].first;
        Matrix<BaseFloat> posteriors;
        am_sgmm.ComponentPosteriors(per_frame_vars, pdf_id,
                                    spk_vars, &posteriors);
        posteriors.Scale(pdf_post[t][j].second);
        spk_stats->AccumulateFromPosteriors(am_sgmm, *spk_vars, feats.Row(t),
                                            gselect[t], posteriors, pdf_id);
      }
    }
  }
  
  }  // end namespace kaldi
  
  int main(int argc, char *argv[]) {
    try {
      typedef kaldi::int32 int32;
      using namespace kaldi;
      const char *usage =
          "Estimate FMLLR transform for SGMMs, either per utterance or for the "
          "supplied set of speakers (with spk2utt option).
  "
          "Reads state-level posteriors. Writes to a table of matrices.
  "
          "--gselect option is mandatory.
  "
          "Usage: sgmm2-est-fmllr [options] <model-in> <feature-rspecifier> "
          "<post-rspecifier> <mats-wspecifier>
  ";
      
      ParseOptions po(usage);
      string spk2utt_rspecifier, spkvecs_rspecifier, fmllr_rspecifier,
          gselect_rspecifier;
      BaseFloat min_count = 100;
      Sgmm2FmllrConfig fmllr_opts;
      
      po.Register("spk2utt", &spk2utt_rspecifier,
                  "File to read speaker to utterance-list map from.");
      po.Register("spkvec-min-count", &min_count,
                  "Minimum count needed to estimate speaker vectors");
      po.Register("spk-vecs", &spkvecs_rspecifier,
                  "Speaker vectors to use during aligment (rspecifier)");
      po.Register("input-fmllr", &fmllr_rspecifier,
                  "Initial FMLLR transform per speaker (rspecifier)");
      po.Register("gselect", &gselect_rspecifier,
                  "Precomputed Gaussian indices (rspecifier)");
      fmllr_opts.Register(&po);
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 4) {
        po.PrintUsage();
        exit(1);
      }
  
      string model_rxfilename = po.GetArg(1),
          feature_rspecifier = po.GetArg(2),
          post_rspecifier = po.GetArg(3),
          fmllr_wspecifier = po.GetArg(4);
  
      TransitionModel trans_model;
      AmSgmm2 am_sgmm;
      Sgmm2FmllrGlobalParams fmllr_globals;
      {
        bool binary;
        Input ki(model_rxfilename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_sgmm.Read(ki.Stream(), binary);
        fmllr_globals.Read(ki.Stream(), binary);
      }
      if (gselect_rspecifier == "")
        KALDI_ERR << "--gselect option is required.";
      
      RandomAccessPosteriorReader post_reader(post_rspecifier);
      RandomAccessBaseFloatVectorReader spkvecs_reader(spkvecs_rspecifier);
      RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
      RandomAccessBaseFloatMatrixReader fmllr_reader(fmllr_rspecifier);
  
      BaseFloatMatrixWriter fmllr_writer(fmllr_wspecifier);
  
      int32 dim = am_sgmm.FeatureDim();
      FmllrSgmm2Accs spk_stats;
      spk_stats.Init(dim, am_sgmm.NumGauss());
      Matrix<BaseFloat> fmllr_xform(dim, dim + 1);
      BaseFloat logdet = 0.0;
      double tot_impr = 0.0, tot_t = 0.0;
      int32 num_done = 0, num_err = 0;
      std::vector<std::vector<int32> > empty_gselect;
  
      if (!spk2utt_rspecifier.empty()) {  // per-speaker adaptation
        SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
        RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
  
        for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
          spk_stats.SetZero();
          string spk = spk2utt_reader.Key();
          const vector<string> &uttlist = spk2utt_reader.Value();
  
          Sgmm2PerSpkDerivedVars spk_vars;
          if (spkvecs_reader.IsOpen()) {
            if (spkvecs_reader.HasKey(spk)) {
              spk_vars.SetSpeakerVector(spkvecs_reader.Value(spk));
              am_sgmm.ComputePerSpkDerivedVars(&spk_vars);
            } else {
              KALDI_WARN << "Cannot find speaker vector for " << spk;
              num_err++;
              continue;
            }
          }  // else spk_vars is "empty"
  
          if (fmllr_reader.IsOpen()) {
            if (fmllr_reader.HasKey(spk)) {
              fmllr_xform.CopyFromMat(fmllr_reader.Value(spk));
              logdet = fmllr_xform.Range(0, dim, 0, dim).LogDet();
            } else {
              KALDI_WARN << "Cannot find FMLLR transform for " << spk;
              fmllr_xform.SetUnit();
              logdet = 0.0;
            }
          } else {
            fmllr_xform.SetUnit();
            logdet = 0.0;
          }
  
          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;
              num_err++;
              continue;
            }
            const Matrix<BaseFloat> &feats = feature_reader.Value(utt);
            if (!post_reader.HasKey(utt) ||
                post_reader.Value(utt).size() != feats.NumRows()) {
              KALDI_WARN << "Did not find posteriors for utterance " << utt
                         << " (or wrong size).";
              num_err++;
              continue;
            }
            const Posterior &post = post_reader.Value(utt);
            if (!gselect_reader.HasKey(utt) ||
                gselect_reader.Value(utt).size() != feats.NumRows()) {
              KALDI_WARN << "Did not find gselect info for utterance " << utt
                         << " (or wrong size).";
              num_err++;
              continue;
            }
            const std::vector<std::vector<int32> > &gselect =
                gselect_reader.Value(utt);
            
            Matrix<BaseFloat> transformed_feats(feats);
            for (int32 r = 0; r < transformed_feats.NumRows(); r++) {
              SubVector<BaseFloat> row(transformed_feats, r);
              ApplyAffineTransform(fmllr_xform, &row);
            }
            AccumulateForUtterance(feats, transformed_feats, gselect,
                                   post, trans_model, am_sgmm,
                                   logdet, &spk_vars, &spk_stats);
            num_done++;
          }  // end looping over all utterances of the current speaker
          
          BaseFloat impr, spk_frame_count;
          // Compute the FMLLR transform and write it out.
          spk_stats.Update(am_sgmm, fmllr_globals, fmllr_opts, &fmllr_xform,
                           &spk_frame_count, &impr);
          fmllr_writer.Write(spk, fmllr_xform);
          tot_impr += impr;
          tot_t += spk_frame_count;
        }  // 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();
  
          if (!post_reader.HasKey(utt) ||
              post_reader.Value(utt).size() != feats.NumRows()) {
            KALDI_WARN << "Did not find posteriors for utterance " << utt
                       << " (or wrong size).";
            num_err++;
            continue;
          }
          const Posterior &post = post_reader.Value(utt);
          if (!gselect_reader.HasKey(utt) ||
              gselect_reader.Value(utt).size() != feats.NumRows()) {
            KALDI_WARN << "Did not find gselect info for utterance " << utt
                       << " (or wrong size).";
            num_err++;
            continue;
          }
          const std::vector<std::vector<int32> > &gselect =
              gselect_reader.Value(utt);
          
          if (fmllr_reader.IsOpen()) {
            if (fmllr_reader.HasKey(utt)) {
              fmllr_xform.CopyFromMat(fmllr_reader.Value(utt));
              logdet = fmllr_xform.Range(0, dim, 0, dim).LogDet();
            } else {
              KALDI_WARN << "Cannot find FMLLR transform for " << utt;
              fmllr_xform.SetUnit();
              logdet = 0.0;
            }
          } else {
            fmllr_xform.SetUnit();
            logdet = 0.0;
          }
          
          Matrix<BaseFloat> transformed_feats(feats);
          for (int32 r = 0; r < transformed_feats.NumRows(); r++) {
            SubVector<BaseFloat> row(transformed_feats, r);
            ApplyAffineTransform(fmllr_xform, &row);
          }
          
          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"
  
          spk_stats.SetZero();
  
          AccumulateForUtterance(feats, transformed_feats, gselect,
                                 post, trans_model, am_sgmm,
                                 logdet, &spk_vars, &spk_stats);
          num_done++;
          
          BaseFloat impr, spk_frame_count;
          // Compute the FMLLR transform and write it out.
          spk_stats.Update(am_sgmm, fmllr_globals, fmllr_opts, &fmllr_xform,
                           &spk_frame_count, &impr);
          fmllr_writer.Write(utt, fmllr_xform);
          tot_impr += impr;
          tot_t += spk_frame_count;
        }
      }
  
      KALDI_LOG << "Done " << num_done << " files, " << num_err << " with errors.";
      KALDI_LOG << "Overall auxf impr per frame is " << (tot_impr / tot_t)
                << " per frame, over " << tot_t << " frames.";
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }