add more comments to explain the new classifier designed for speech command classification task

examples/speechcommand_v1/s0/run.sh

@@ -6,7 +6,7 @@
 
 export CUDA_VISIBLE_DEVICES="0"
 
-stage=2
+stage=-1
 stop_stage=2
 num_keywords=11
 

kws/model/kws_model.py

@@ -123,6 +123,8 @@ def init_model(configs):
         print('Unknown body type {}'.format(backbone_type))
         sys.exit(1)
     if 'classifier' in configs:
+        # For speech command dataset, we use 2 FC layer as classifier,
+        # we add dropout after first FC layer to prevent overfitting
         classifier_type = configs['classifier']['type']
         dropout = configs['classifier']['dropout']
 
@@ -131,8 +133,11 @@ def init_model(configs):
                                         nn.Dropout(dropout),
                                         nn.Linear(64, output_dim))
         if classifier_type == 'global':
+            # global means we add a global average pooling before classifier
             classifier = GlobalClassifier(classifier_base)
         elif classifier_type == 'last':
+            # last means we use last frame to do backpropagation, so the model
+            # can be infered streamingly
             classifier = LastClassifier(classifier_base)
         else:
             print('Unknown classifier type {}'.format(classifier_type))