// gmmbin/gmm-est-lvtln-trans.cc // Copyright 2009-2011 Microsoft Corporation; Saarland University // 2014 Johns Hopkins University (author: 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 using std::string; #include 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 &feats, const GaussPost &gpost, const AmDiagGmm &am_gmm, FmllrDiagGmmAccs *spk_stats) { for (size_t i = 0; i < gpost.size(); i++) { for (size_t j = 0; j < gpost[i].size(); j++) { int32 pdf_id = gpost[i][j].first; const Vector &posterior(gpost[i][j].second); spk_stats->AccumulateFromPosteriors(am_gmm.GetPdf(pdf_id), feats.Row(i), posterior); } } } } 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). Reads posteriors. \n" "Usage: gmm-est-lvtln-trans [options] " " []\n"; 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), gpost_rspecifier = po.GetArg(4), trans_wspecifier = po.GetArg(5), warp_wspecifier = po.GetOptArg(6); AmDiagGmm am_gmm; { bool binary; Input ki(model_rxfilename, &binary); TransitionModel trans_model; trans_model.Read(ki.Stream(), binary); am_gmm.Read(ki.Stream(), binary); } LinearVtln lvtln; ReadKaldiObject(lvtln_rxfilename, &lvtln); RandomAccessGaussPostReader gpost_reader(gpost_rspecifier); double tot_lvtln_impr = 0.0, tot_t = 0.0; BaseFloatMatrixWriter transform_writer(trans_wspecifier); BaseFloatWriter warp_writer(warp_wspecifier); std::vector class_counts(lvtln.NumClasses(), 0); int32 num_done = 0, num_no_gpost = 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 &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 (!gpost_reader.HasKey(utt)) { KALDI_WARN << "Did not find posteriors for utterance " << utt; num_no_gpost++; continue; } const Matrix &feats = feature_reader.Value(utt); const GaussPost &gpost = gpost_reader.Value(utt); if (static_cast(gpost.size()) != feats.NumRows()) { KALDI_WARN << "GauPost vector has wrong size " << gpost.size() << " vs. " << feats.NumRows(); num_other_error++; continue; } AccumulateForUtterance(feats, gpost, am_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 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 (!gpost_reader.HasKey(utt)) { KALDI_WARN << "Did not find gposts for utterance " << utt; num_no_gpost++; continue; } const Matrix &feats = feature_reader.Value(); const GaussPost &gpost = gpost_reader.Value(utt); if (static_cast(gpost.size()) != feats.NumRows()) { KALDI_WARN << "GauPost has wrong size " << gpost.size() << " vs. " << feats.NumRows(); num_other_error++; continue; } num_done++; FmllrDiagGmmAccs spk_stats(lvtln.Dim()); AccumulateForUtterance(feats, gpost, am_gmm, &spk_stats); BaseFloat impr, utt_tot_t = spk_stats.beta_; { // Compute the transform and write it out. Matrix 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_gpost << " with no gposts, " << 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; } }