// sgmm2bin/sgmm2-est-spkvecs.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/estimate-am-sgmm2.h"
  #include "hmm/transition-model.h"
  #include "hmm/posterior.h"
  
  namespace kaldi {
  
  void AccumulateForUtterance(const Matrix<BaseFloat> &feats,
                              const Posterior &post,
                              const TransitionModel &trans_model,
                              const AmSgmm2 &am_sgmm,
                              const vector< vector<int32> > &gselect,
                              Sgmm2PerSpkDerivedVars *spk_vars,
                              MleSgmm2SpeakerAccs *spk_stats) {
    kaldi::Sgmm2PerFrameDerivedVars per_frame_vars;
  
    KALDI_ASSERT(gselect.size() == feats.NumRows());
    Posterior pdf_post;
    ConvertPosteriorToPdfs(trans_model, post, &pdf_post);
    for (size_t i = 0; i < post.size(); i++) {
      am_sgmm.ComputePerFrameVars(feats.Row(i), gselect[i],
                                  *spk_vars, &per_frame_vars);
      
      for (size_t j = 0; j < pdf_post[i].size(); j++) {
        int32 pdf_id = pdf_post[i][j].first;
        spk_stats->Accumulate(am_sgmm, per_frame_vars, pdf_id,
                              pdf_post[i][j].second, spk_vars);
      }
    }
  }
  
  }  // end namespace kaldi
  
  int main(int argc, char *argv[]) {
    try {
      typedef kaldi::int32 int32;
      using namespace kaldi;
      const char *usage =
          "Estimate SGMM speaker vectors, either per utterance or for the "
          "supplied set of speakers (with spk2utt option).
  "
          "Reads Gaussian-level posteriors. Writes to a table of vectors.
  "
          "Usage: sgmm2-est-spkvecs [options] <model-in> <feature-rspecifier> "
          "<post-rspecifier> <vecs-wspecifier>
  "
          "note: --gselect option is required.";
      
      ParseOptions po(usage);
      string gselect_rspecifier, spk2utt_rspecifier, spkvecs_rspecifier;
      BaseFloat min_count = 100;
      BaseFloat rand_prune = 1.0e-05;
  
      po.Register("gselect", &gselect_rspecifier,
                  "rspecifier for precomputed per-frame Gaussian indices from.");
      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("rand-prune", &rand_prune, "Pruning threshold for posteriors");
      po.Register("spk-vecs", &spkvecs_rspecifier, "Speaker vectors to use during aligment (rspecifier)");
      po.Read(argc, argv);
  
      if (po.NumArgs() != 4) {
        po.PrintUsage();
        exit(1);
      }
      if (gselect_rspecifier == "")
        KALDI_ERR << "--gselect option is mandatory.";
      
      string model_rxfilename = po.GetArg(1),
          feature_rspecifier = po.GetArg(2),
          post_rspecifier = po.GetArg(3),
          vecs_wspecifier = po.GetArg(4);
  
      TransitionModel trans_model;
      AmSgmm2 am_sgmm;
      {
        bool binary;
        Input ki(model_rxfilename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_sgmm.Read(ki.Stream(), binary);
      }
      MleSgmm2SpeakerAccs spk_stats(am_sgmm, rand_prune);
  
      RandomAccessPosteriorReader post_reader(post_rspecifier);
      RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
      RandomAccessBaseFloatVectorReader spkvecs_reader(spkvecs_rspecifier);
  
      BaseFloatVectorWriter vecs_writer(vecs_wspecifier);
  
      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.Clear();
          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 speaker " << spk
                         << ", not processing this speaker.";
              num_err++; // standard Kaldi behavior is to not process data
              // when errors like this happen, as it's generally a script error;
              continue;
            }
          }  // else spk_vars is "empty"
  
          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_err++;
              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_err++;
              continue;
            }
            if (!gselect_reader.HasKey(utt) ||
                gselect_reader.Value(utt).size() != feats.NumRows()) {
              KALDI_WARN << "No Gaussian-selection info available for utterance "
                         << utt << " (or wrong size)";
              num_err++;
              continue;
            }
            const std::vector<std::vector<int32> > &gselect =
                gselect_reader.Value(utt);
            
            AccumulateForUtterance(feats, post, trans_model, am_sgmm,
                                   gselect, &spk_vars, &spk_stats);
            num_done++;
          }  // end looping over all utterances of the current speaker
  
          BaseFloat impr, spk_tot_t;
          {  // Compute the spk_vec and write it out.
            Vector<BaseFloat> spk_vec(am_sgmm.SpkSpaceDim(), kSetZero);
            if (spk_vars.GetSpeakerVector().Dim() != 0)
              spk_vec.CopyFromVec(spk_vars.GetSpeakerVector());
            spk_stats.Update(am_sgmm, min_count, &spk_vec, &impr, &spk_tot_t);
            vecs_writer.Write(spk, spk_vec);
          }
          KALDI_LOG << "For speaker " << spk << ", auxf-impr from speaker vector 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();
          if (!post_reader.HasKey(utt) ||
              post_reader.Value(utt).size() != feats.NumRows()) {
            KALDI_WARN << "Did not find posts for utterance "
                       << utt << " (or wrong size).";
            num_err++;
            continue;
          }
          const Posterior &post = post_reader.Value(utt);
  
          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 utterance " << utt
                         << ", not processing it.";
              num_err++;
              continue;
            }
          }  // else spk_vars is "empty"
          
          num_done++;
  
          if (!gselect_reader.HasKey(utt) ||
              gselect_reader.Value(utt).size() != feats.NumRows()) {
            KALDI_WARN << "No Gaussian-selection info available for utterance "
                       << utt << " (or wrong size)";
            num_err++;
            continue;
          }
          const std::vector<std::vector<int32> > &gselect =
              gselect_reader.Value(utt);
  
          spk_stats.Clear();
          
          AccumulateForUtterance(feats, post, trans_model, am_sgmm,
                                 gselect, &spk_vars, &spk_stats);
  
          BaseFloat impr, utt_tot_t;
          {  // Compute the spk_vec and write it out.
            Vector<BaseFloat> spk_vec(am_sgmm.SpkSpaceDim(), kSetZero);
            if (spk_vars.GetSpeakerVector().Dim() != 0)
              spk_vec.CopyFromVec(spk_vars.GetSpeakerVector());
            spk_stats.Update(am_sgmm, min_count, &spk_vec, &impr, &utt_tot_t);
            vecs_writer.Write(utt, spk_vec);
          }
          KALDI_LOG << "For utterance " << utt << ", auxf-impr from speaker vectors is "
                    << (impr/utt_tot_t) << ", over " << utt_tot_t << " frames.";
          tot_impr += impr;
          tot_t += utt_tot_t;
        }
      }
  
      KALDI_LOG << "Overall auxf impr per frame is "
                << (tot_impr / tot_t) << " over " << tot_t << " frames.";
      KALDI_LOG << "Done " << num_done << " files, " << num_err << " with errors.";
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }