// gmmbin/gmm-acc-stats-ali.cc // Copyright 2009-2012 Microsoft Corporation 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 "base/kaldi-common.h" #include "util/common-utils.h" #include "gmm/am-diag-gmm.h" #include "hmm/transition-model.h" #include "gmm/mle-am-diag-gmm.h" int main(int argc, char *argv[]) { using namespace kaldi; typedef kaldi::int32 int32; try { const char *usage = "Accumulate stats for GMM training.\n" "Usage: gmm-acc-stats-ali [options] " " \n" "e.g.:\n gmm-acc-stats-ali 1.mdl scp:train.scp ark:1.ali 1.acc\n"; ParseOptions po(usage); bool binary = true; po.Register("binary", &binary, "Write output in binary mode"); po.Read(argc, argv); if (po.NumArgs() != 4) { po.PrintUsage(); exit(1); } std::string model_filename = po.GetArg(1), feature_rspecifier = po.GetArg(2), alignments_rspecifier = po.GetArg(3), accs_wxfilename = po.GetArg(4); AmDiagGmm am_gmm; TransitionModel trans_model; { bool binary; Input ki(model_filename, &binary); trans_model.Read(ki.Stream(), binary); am_gmm.Read(ki.Stream(), binary); } Vector transition_accs; trans_model.InitStats(&transition_accs); AccumAmDiagGmm gmm_accs; gmm_accs.Init(am_gmm, kGmmAll); double tot_like = 0.0; kaldi::int64 tot_t = 0; SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier); RandomAccessInt32VectorReader alignments_reader(alignments_rspecifier); int32 num_done = 0, num_err = 0; for (; !feature_reader.Done(); feature_reader.Next()) { std::string key = feature_reader.Key(); if (!alignments_reader.HasKey(key)) { KALDI_WARN << "No alignment for utterance " << key; num_err++; } else { const Matrix &mat = feature_reader.Value(); const std::vector &alignment = alignments_reader.Value(key); if (alignment.size() != mat.NumRows()) { KALDI_WARN << "Alignments has wrong size " << (alignment.size()) << " vs. " << (mat.NumRows()); num_err++; continue; } num_done++; BaseFloat tot_like_this_file = 0.0; for (size_t i = 0; i < alignment.size(); i++) { int32 tid = alignment[i], // transition identifier. pdf_id = trans_model.TransitionIdToPdf(tid); trans_model.Accumulate(1.0, tid, &transition_accs); tot_like_this_file += gmm_accs.AccumulateForGmm(am_gmm, mat.Row(i), pdf_id, 1.0); } tot_like += tot_like_this_file; tot_t += alignment.size(); if (num_done % 50 == 0) { KALDI_LOG << "Processed " << num_done << " utterances; for utterance " << key << " avg. like is " << (tot_like_this_file/alignment.size()) << " over " << alignment.size() <<" frames."; } } } KALDI_LOG << "Done " << num_done << " files, " << num_err << " with errors."; KALDI_LOG << "Overall avg like per frame (Gaussian only) = " << (tot_like/tot_t) << " over " << tot_t << " frames."; { Output ko(accs_wxfilename, binary); transition_accs.Write(ko.Stream(), binary); gmm_accs.Write(ko.Stream(), binary); } KALDI_LOG << "Written accs."; if (num_done != 0) return 0; else return 1; } catch(const std::exception &e) { std::cerr << e.what(); return -1; } }