Adding gmm-init-model-flat

git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@871 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8

src/gmmbin/gmm-init-model-flat.cc

+// gmmbin/gmm-init-model-flat.cc
+
+// Copyright 2012  Daniel Povey
+
+// 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"
+#include "tree/build-tree-utils.h"
+#include "tree/context-dep.h"
+#include "tree/clusterable-classes.h"
+#include "util/text-utils.h"
+
+namespace kaldi {
+
+void GetFeatureMeanAndVariance(const std::string &feat_rspecifier,
+                               Vector<BaseFloat> *inv_var_out,                               
+                               Vector<BaseFloat> *mean_out) {
+  double count = 0.0;
+  Vector<double> x_stats, x2_stats;
+
+  SequentialDoubleMatrixReader feat_reader(feat_rspecifier);
+  for (; !feat_reader.Done(); feat_reader.Next()) {
+    const Matrix<double> &mat = feat_reader.Value();
+    if (x_stats.Dim() == 0) {
+      int32 dim = mat.NumCols();
+      x_stats.Resize(dim);
+      x2_stats.Resize(dim);
+    }
+    for (int32 i = 0; i < mat.NumRows(); i++) {
+      count += 1.0;
+      x_stats.AddVec(1.0, mat.Row(i));
+      x2_stats.AddVec2(1.0, mat.Row(i));
+    }
+  }
+  if (count == 0) { KALDI_ERR << "No features were read!"; }
+  x_stats.Scale(1.0/count);
+  x2_stats.Scale(1.0/count);
+  x2_stats.AddVec2(-1.0, x_stats);
+  if (x2_stats.Min() <= 0.0)
+    KALDI_ERR << "Variance is zero or negative!";
+  x2_stats.InvertElements();
+  int32 dim = x_stats.Dim();
+  inv_var_out->Resize(dim);
+  mean_out->Resize(dim);
+  inv_var_out->CopyFromVec(x2_stats);
+  mean_out->CopyFromVec(x_stats);
+}
+
+
+}
+
+int main(int argc, char *argv[]) {
+  using namespace kaldi;
+  try {
+    using namespace kaldi;
+    typedef kaldi::int32 int32;
+
+    const char *usage =
+        "Initialize GMM, with Gaussians initialized to mean and variance\n"
+        " of some provided example data\n"
+        "Usage:  gmm-init-model-flat [options] <tree-in> <topo-file> <model-out> <features-rspecifier>\n"        
+        "e.g.: \n"
+        "  gmm-init-model-flat tree topo 1.mdl ark:feats.scp\n";
+    
+    bool binary = true;
+
+
+    ParseOptions po(usage);
+    po.Register("binary", &binary, "Write output in binary mode");
+
+    po.Read(argc, argv);
+
+    if (po.NumArgs() != 4) {
+      po.PrintUsage();
+      exit(1);
+    }
+
+    std::string
+        tree_filename = po.GetArg(1),
+        topo_filename = po.GetArg(2),
+        model_out_filename = po.GetArg(3),
+        feats_rspecifier = po.GetArg(4);
+
+    ContextDependency ctx_dep;
+    ReadKaldiObject(tree_filename, &ctx_dep);
+
+    HmmTopology topo; 
+    ReadKaldiObject(topo_filename, &topo);
+
+    Vector<BaseFloat> global_inverse_var, global_mean;
+    GetFeatureMeanAndVariance(feats_rspecifier,
+                              &global_inverse_var,
+                              &global_mean);
+
+    int32 num_pdfs = ctx_dep.NumPdfs();
+
+    AmDiagGmm am_gmm;
+    DiagGmm gmm;
+    int32 dim = global_mean.Dim();
+    gmm.Resize(1, dim);
+    {  // Initialize the gmm.
+      Matrix<BaseFloat> inv_var(1, dim);
+      inv_var.Row(0).CopyFromVec(global_inverse_var);
+      Matrix<BaseFloat> mu(1, dim);
+      mu.Row(0).CopyFromVec(global_mean);
+      Vector<BaseFloat> weights(1);
+      weights.Set(1.0);
+      gmm.SetInvVarsAndMeans(inv_var, mu);
+      gmm.SetWeights(weights);
+      gmm.ComputeGconsts();
+    }
+    for (int i = 0; i < num_pdfs; i++)
+      am_gmm.AddPdf(gmm);
+    
+    TransitionModel trans_model(ctx_dep, topo);
+
+    {
+      Output ko(model_out_filename, binary);
+      trans_model.Write(ko.Stream(), binary);
+      am_gmm.Write(ko.Stream(), binary);
+    }
+    KALDI_LOG << "Wrote model.";
+  } catch(const std::exception& e) {
+    std::cerr << e.what();
+    return -1;
+  }
+}