statserver

features_server.py

@@ -215,7 +215,7 @@ class FeaturesServer(object):
         # Perform RASTA filtering if required
         if self.rasta:
             feat, label = self._rasta(feat, label)
-
+        
         # Add temporal context
         if self.delta or self.double_delta:
             feat = self._delta_and_2delta(feat)
@@ -223,7 +223,7 @@ class FeaturesServer(object):
             feat = pca_dct(feat, self.dct_pca_config[0], self.dct_pca_config[1], self.dct_pca_config[2])
         elif self.sdc:
             feat = shifted_delta_cepstral(feat, d=self.sdc_config[0], p=self.sdc_config[1], k=self.sdc_config[2])
-
+        
         # Smooth the labels and fuse the channels if more than one.
         logging.debug('Smooth the labels and fuse the channels if more than one')
         label = label_fusion(label)
@@ -239,7 +239,7 @@ class FeaturesServer(object):
             logging.debug('no keep all')
             feat = feat[label]
             label = label[label]
-
+         
         return feat, label
 
     def _mask(self, cep):
@@ -487,8 +487,6 @@ class FeaturesServer(object):
             feat, label = self.post_processing(feat, label, global_mean, global_std)
         else:
             feat, label = self.post_processing(feat, label)
-        if self.mask is not None:
-            feat = feat[:, self.mask]
 
         return feat, label
 

statserver.py

@@ -659,8 +659,7 @@ class StatServer:
                 # Compute 0th-order statistics
                 self.stat0[idx, :] = pp.sum(0)
                 # Compute 1st-order statistics
-                self.stat1[idx, :] = numpy.reshape(numpy.transpose(
-                        numpy.dot(data.transpose(), pp)), ubm.sv_size()).astype(STAT_TYPE)
+                self.stat1[idx, :] = numpy.reshape(numpy.transpose(numpy.dot(data.transpose(), pp)), ubm.sv_size()).astype(STAT_TYPE)
 
     def get_mean_stat1(self):
         """Return the mean of first order statistics