some updates..

Author: Suwon Shon

scripts/ivector_tools.py

@@ -60,14 +60,13 @@ def read_ivector_key(file):
     return spkid, ivec_pointer,total_num
 
 def length_norm(mat):
-    mat = mat.transpose()
     norm_mat = []
     for line in mat:
         temp = line/np.math.sqrt(sum(np.power(line,2)))
         norm_mat.append(temp)
     norm_mat = np.array(norm_mat)
 
-    return norm_mat.transpose()
+    return norm_mat
 
 def lda(mat,label):
     # mat = observation x dim ( for example, 8x600 for 8 obs and 600dim ivector)

scripts/siamese_model.py

@@ -26,13 +26,13 @@ def network(self, x):
         n_prev_weight = int(x.get_shape()[1])
         conv1_d = tf.reshape(conv1r,[-1, n_prev_weight/stride*depth])
 
-        kernel_size =10
-        stride = 2
-        depth=10
-        conv2 = self.conv_layer(conv1_d, kernel_size,stride,depth,'conv2')
-        conv2r = tf.nn.relu(conv2)
-        n_prev_weight = int(conv1_d.get_shape()[1])
-        conv2_d = tf.reshape(conv2r,[-1, n_prev_weight/stride*depth])
+#        kernel_size =10
+#        stride = 2
+#        depth=10
+#        conv2 = self.conv_layer(conv1_d, kernel_size,stride,depth,'conv2')
+#        conv2r = tf.nn.relu(conv2)
+#        n_prev_weight = int(conv1_d.get_shape()[1])
+#        conv2_d = tf.reshape(conv2r,[-1, n_prev_weight/stride*depth])
 
         fc1 = self.fc_layer(conv1_d, 1500, "fc1")
         ac1 = tf.nn.relu(fc1)

train_ivector.ipynb

@@ -108,10 +108,10 @@
     "dev_ivectors = np.dot( np.subtract( dev_ivectors, m), W)\n",
     "tst_ivectors = np.dot( np.subtract( tst_ivectors, m), W)\n",
     "\n",
-    "trn_ivectors = it.length_norm(trn_ivectors.transpose()).transpose()\n",
-    "trndev_ivectors = it.length_norm(trndev_ivectors.transpose()).transpose()\n",
-    "dev_ivectors = it.length_norm(dev_ivectors.transpose()).transpose()\n",
-    "tst_ivectors = it.length_norm(tst_ivectors.transpose()).transpose()\n",
+    "trn_ivectors = it.length_norm(trn_ivectors)\n",
+    "trndev_ivectors = it.length_norm(trndev_ivectors)\n",
+    "dev_ivectors = it.length_norm(dev_ivectors)\n",
+    "tst_ivectors = it.length_norm(tst_ivectors)\n",
     "\n",
     "\n",
     "#language modeling\n",
@@ -120,7 +120,7 @@
     "    lang_mean.append(np.mean(np.append(trndev_ivectors[np.nonzero(trndev_labels == i+1)] ,dev_ivectors[np.nonzero(dev_labels == i+1)],axis=0),axis=0))\n",
     "\n",
     "lang_mean = np.array(lang_mean)\n",
-    "lang_mean = it.length_norm(lang_mean.transpose()).transpose()\n",
+    "lang_mean = it.length_norm(lang_mean.transpose)\n",
     "\n",
     "print( np.shape(trn_ivectors), np.shape(dev_ivectors), np.shape(lang_mean),np.shape(tst_ivectors) )\n"
    ]