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gmm-est-fmllr-raw-gpost.cc File Reference
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Namespaces

 kaldi
 Relabels neural network egs with the read pdf-id alignments.
 

Functions

void AccStatsForUtterance (const TransitionModel &trans_model, const AmDiagGmm &am_gmm, const GaussPost &gpost, const Matrix< BaseFloat > &feats, FmllrRawAccs *accs)
 
int main (int argc, char *argv[])
 

Function Documentation

int main ( int  argc,
char *  argv[] 
)

Definition at line 49 of file gmm-est-fmllr-raw-gpost.cc.

References kaldi::AccStatsForUtterance(), count, AmDiagGmm::Dim(), SequentialTableReader< Holder >::Done(), ParseOptions::GetArg(), RandomAccessTableReader< Holder >::HasKey(), rnnlm::i, KALDI_LOG, KALDI_WARN, SequentialTableReader< Holder >::Key(), SequentialTableReader< Holder >::Next(), ParseOptions::NumArgs(), MatrixBase< Real >::NumRows(), ParseOptions::PrintUsage(), AmDiagGmm::Read(), ParseOptions::Read(), TransitionModel::Read(), kaldi::ReadKaldiObject(), FmllrRawOptions::Register(), ParseOptions::Register(), MatrixBase< Real >::SetUnit(), Input::Stream(), RandomAccessTableReader< Holder >::Value(), SequentialTableReader< Holder >::Value(), and TableWriter< Holder >::Write().

49  {
50  try {
51  typedef kaldi::int32 int32;
52  using namespace kaldi;
53  const char *usage =
54  "Estimate fMLLR transforms in the space before splicing and linear transforms\n"
55  "such as LDA+MLLT, but using models in the space transformed by these transforms\n"
56  "Requires the original spliced features, and the full LDA+MLLT (or similar) matrix\n"
57  "including the 'rejected' rows (see the program get-full-lda-mat). Reads in\n"
58  "Gaussian-level posteriors.\n"
59  "Usage: gmm-est-fmllr-raw-gpost [options] <model-in> <full-lda-mat-in> "
60  "<feature-rspecifier> <gpost-rspecifier> <transform-wspecifier>\n";
61 
62 
63  int32 raw_feat_dim = 13;
64  ParseOptions po(usage);
65  FmllrRawOptions opts;
66  std::string spk2utt_rspecifier;
67  po.Register("spk2utt", &spk2utt_rspecifier, "rspecifier for speaker to "
68  "utterance-list map");
69  po.Register("raw-feat-dim", &raw_feat_dim, "Dimension of raw features "
70  "prior to splicing");
71  opts.Register(&po);
72 
73  po.Read(argc, argv);
74 
75  if (po.NumArgs() != 5) {
76  po.PrintUsage();
77  exit(1);
78  }
79 
80  std::string model_rxfilename = po.GetArg(1),
81  full_lda_mat_rxfilename = po.GetArg(2),
82  feature_rspecifier = po.GetArg(3),
83  gpost_rspecifier = po.GetArg(4),
84  transform_wspecifier = po.GetArg(5);
85 
86  AmDiagGmm am_gmm;
87  TransitionModel trans_model;
88  {
89  bool binary;
90  Input ki(model_rxfilename, &binary);
91  trans_model.Read(ki.Stream(), binary);
92  am_gmm.Read(ki.Stream(), binary);
93  }
94 
95  Matrix<BaseFloat> full_lda_mat;
96  ReadKaldiObject(full_lda_mat_rxfilename, &full_lda_mat);
97 
98  RandomAccessGaussPostReader gpost_reader(gpost_rspecifier);
99  BaseFloatMatrixWriter transform_writer(transform_wspecifier);
100 
101  double tot_auxf_impr = 0.0, tot_count = 0.0;
102 
103  int32 num_done = 0, num_err = 0;
104  if (!spk2utt_rspecifier.empty()) { // Adapting per speaker
105  SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
106  RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
107 
108  for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
109  FmllrRawAccs accs(raw_feat_dim, am_gmm.Dim(), full_lda_mat);
110  std::string spk = spk2utt_reader.Key();
111  const std::vector<std::string> &uttlist = spk2utt_reader.Value();
112  for (size_t i = 0; i < uttlist.size(); i++) {
113  std::string utt = uttlist[i];
114  if (!feature_reader.HasKey(utt)) {
115  KALDI_WARN << "Features not found for utterance " << utt;
116  num_err++;
117  continue;
118  }
119  if (!gpost_reader.HasKey(utt)) {
120  KALDI_WARN << "Gaussian-level posteriors not found for utterance " << utt;
121  num_err++;
122  continue;
123  }
124  const Matrix<BaseFloat> &feats = feature_reader.Value(utt);
125  const GaussPost &gpost = gpost_reader.Value(utt);
126  if (static_cast<int32>(gpost.size()) != feats.NumRows()) {
127  KALDI_WARN << "Size mismatch between gposteriors " << gpost.size()
128  << " and features " << feats.NumRows();
129  num_err++;
130  continue;
131  }
132 
133  AccStatsForUtterance(trans_model, am_gmm, gpost, feats, &accs);
134  num_done++;
135  }
136 
137  BaseFloat auxf_impr, count;
138  {
139  Matrix<BaseFloat> transform(raw_feat_dim, raw_feat_dim + 1);
140  transform.SetUnit();
141  accs.Update(opts, &transform, &auxf_impr, &count);
142  transform_writer.Write(spk, transform);
143  }
144  KALDI_LOG << "For speaker " << spk << ", auxf-impr from raw fMLLR is "
145  << (auxf_impr/count) << " over " << count << " frames.";
146  tot_auxf_impr += auxf_impr;
147  tot_count += count;
148  }
149  } else { // --spk2utt option not given -> adapt per utterance.
150  SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
151  for (; !feature_reader.Done(); feature_reader.Next()) {
152  std::string utt = feature_reader.Key();
153  if (!gpost_reader.HasKey(utt)) {
154  KALDI_WARN << "Gaussian-level posteriors not found for utterance " << utt;
155  num_err++;
156  continue;
157  }
158  const Matrix<BaseFloat> &feats = feature_reader.Value();
159  const GaussPost &gpost = gpost_reader.Value(utt);
160 
161  if (static_cast<int32>(gpost.size()) != feats.NumRows()) {
162  KALDI_WARN << "Size mismatch between posteriors " << gpost.size()
163  << " and features " << feats.NumRows();
164  num_err++;
165  continue;
166  }
167 
168  FmllrRawAccs accs(raw_feat_dim, am_gmm.Dim(), full_lda_mat);
169 
170  AccStatsForUtterance(trans_model, am_gmm, gpost, feats, &accs);
171 
172  BaseFloat auxf_impr, count;
173  {
174  Matrix<BaseFloat> transform(raw_feat_dim, raw_feat_dim + 1);
175  transform.SetUnit();
176  accs.Update(opts, &transform, &auxf_impr, &count);
177  transform_writer.Write(utt, transform);
178  }
179  KALDI_LOG << "For utterance " << utt << ", auxf-impr from raw fMLLR is "
180  << (auxf_impr/count) << " over " << count << " frames.";
181  tot_auxf_impr += auxf_impr;
182  tot_count += count;
183  num_done++;
184  }
185  }
186 
187  KALDI_LOG << "Processed " << num_done << " utterances, "
188  << num_err << " had errors.";
189  KALDI_LOG << "Overall raw-fMLLR auxf impr per frame is "
190  << (tot_auxf_impr / tot_count) << " over " << tot_count
191  << " frames.";
192  return (num_done != 0 ? 0 : 1);
193  } catch(const std::exception &e) {
194  std::cerr << e.what();
195  return -1;
196  }
197 }
Relabels neural network egs with the read pdf-id alignments.
Definition: chain.dox:20
A templated class for writing objects to an archive or script file; see The Table concept...
Definition: kaldi-table.h:366
void ReadKaldiObject(const std::string &filename, Matrix< float > *m)
Definition: kaldi-io.cc:818
Allows random access to a collection of objects in an archive or script file; see The Table concept...
Definition: kaldi-table.h:233
const size_t count
float BaseFloat
Definition: kaldi-types.h:29
The class ParseOptions is for parsing command-line options; see Parsing command-line options for more...
Definition: parse-options.h:36
int32 Dim() const
Definition: am-diag-gmm.h:79
void Read(std::istream &is, bool binary)
void Register(OptionsItf *opts)
Definition: fmllr-raw.h:73
A templated class for reading objects sequentially from an archive or script file; see The Table conc...
Definition: kaldi-table.h:287
#define KALDI_WARN
Definition: kaldi-error.h:130
MatrixIndexT NumRows() const
Returns number of rows (or zero for emtpy matrix).
Definition: kaldi-matrix.h:58
void AccStatsForUtterance(const TransitionModel &trans_model, const AmDiagGmm &am_gmm, const GaussPost &gpost, const Matrix< BaseFloat > &feats, FmllrRawAccs *accs)
std::vector< std::vector< std::pair< int32, Vector< BaseFloat > > > > GaussPost
GaussPost is a typedef for storing Gaussian-level posteriors for an utterance.
Definition: posterior.h:52
#define KALDI_LOG
Definition: kaldi-error.h:133
void Read(std::istream &in_stream, bool binary)
Definition: am-diag-gmm.cc:147