Add GRU support.

demo_server.py

@@ -66,6 +66,11 @@ parser.add_argument(
     default=512,
     type=int,
     help="RNN layer cell number. (default: %(default)s)")
+parser.add_argument(
+    "--use_gru",
+    default=True,
+    type=bool,
+    help="Use GRU or simple RNN. (default: %(default)s)")
 parser.add_argument(
     "--use_gpu",
     default=True,
@@ -199,6 +204,7 @@ def start_server():
         num_conv_layers=args.num_conv_layers,
         num_rnn_layers=args.num_rnn_layers,
         rnn_layer_size=args.rnn_layer_size,
+        use_gru=args.use_gru,
         pretrained_model_path=args.model_filepath)
 
     # prepare ASR inference handler

evaluate.py

@@ -38,6 +38,11 @@ parser.add_argument(
     default=512,
     type=int,
     help="RNN layer cell number. (default: %(default)s)")
+parser.add_argument(
+    "--use_gru",
+    default=True,
+    type=bool,
+    help="Use GRU or simple RNN. (default: %(default)s)")
 parser.add_argument(
     "--use_gpu",
     default=True,
@@ -142,6 +147,7 @@ def evaluate():
         num_conv_layers=args.num_conv_layers,
         num_rnn_layers=args.num_rnn_layers,
         rnn_layer_size=args.rnn_layer_size,
+        use_gru=args.use_gru,
         pretrained_model_path=args.model_filepath)
 
     error_rate_func = cer if args.error_rate_type == 'cer' else wer

infer.py

@@ -33,6 +33,11 @@ parser.add_argument(
     default=512,
     type=int,
     help="RNN layer cell number. (default: %(default)s)")
+parser.add_argument(
+    "--use_gru",
+    default=True,
+    type=bool,
+    help="Use GRU or simple RNN. (default: %(default)s)")
 parser.add_argument(
     "--use_gpu",
     default=True,
@@ -143,6 +148,7 @@ def infer():
         num_conv_layers=args.num_conv_layers,
         num_rnn_layers=args.num_rnn_layers,
         rnn_layer_size=args.rnn_layer_size,
+        use_gru=args.use_gru,
         pretrained_model_path=args.model_filepath)
     result_transcripts = ds2_model.infer_batch(
         infer_data=infer_data,

layer.py

@@ -57,7 +57,7 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, act):
     # input-hidden weights shared across bi-direcitonal rnn.
     input_proj = paddle.layer.fc(
         input=input, size=size, act=paddle.activation.Linear(), bias_attr=False)
-    # batch norm is only performed on input-state projection 
+    # batch norm is only performed on input-state projection
     input_proj_bn = paddle.layer.batch_norm(
         input=input_proj, act=paddle.activation.Linear())
     # forward and backward in time
@@ -68,6 +68,38 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, act):
     return paddle.layer.concat(input=[forward_simple_rnn, backward_simple_rnn])
 
 
+def bidirectional_gru_bn_layer(name, input, size, act):
+    """Bidirectonal gru layer with sequence-wise batch normalization.
+    The batch normalization is only performed on input-state weights.
+
+    :param name: Name of the layer.
+    :type name: string
+    :param input: Input layer.
+    :type input: LayerOutput
+    :param size: Number of RNN cells.
+    :type size: int
+    :param act: Activation type.
+    :type act: BaseActivation
+    :return: Bidirectional simple rnn layer.
+    :rtype: LayerOutput
+    """
+    # input-hidden weights shared across bi-direcitonal rnn.
+    input_proj = paddle.layer.fc(
+        input=input,
+        size=size * 3,
+        act=paddle.activation.Linear(),
+        bias_attr=False)
+    # batch norm is only performed on input-state projection
+    input_proj_bn = paddle.layer.batch_norm(
+        input=input_proj, act=paddle.activation.Linear())
+    # forward and backward in time
+    forward_gru = paddle.layer.grumemory(
+        input=input_proj_bn, act=act, reverse=False)
+    backward_gru = paddle.layer.grumemory(
+        input=input_proj_bn, act=act, reverse=True)
+    return paddle.layer.concat(input=[forward_gru, backward_gru])
+
+
 def conv_group(input, num_stacks):
     """Convolution group with stacked convolution layers.
 
@@ -83,7 +115,7 @@ def conv_group(input, num_stacks):
         filter_size=(11, 41),
         num_channels_in=1,
         num_channels_out=32,
-        stride=(3, 2),
+        stride=(2, 2),
         padding=(5, 20),
         act=paddle.activation.BRelu())
     for i in xrange(num_stacks - 1):
@@ -100,7 +132,7 @@ def conv_group(input, num_stacks):
     return conv, output_num_channels, output_height
 
 
-def rnn_group(input, size, num_stacks):
+def rnn_group(input, size, num_stacks, use_gru):
     """RNN group with stacked bidirectional simple RNN layers.
 
     :param input: Input layer.
@@ -109,13 +141,25 @@ def rnn_group(input, size, num_stacks):
     :type size: int
     :param num_stacks: Number of stacked rnn layers.
     :type num_stacks: int
+    :param use_gru: Use gru if set True. Use simple rnn if set False.
+    :type use_gru: bool
     :return: Output layer of the RNN group.
     :rtype: LayerOutput
     """
     output = input
     for i in xrange(num_stacks):
-        output = bidirectional_simple_rnn_bn_layer(
-            name=str(i), input=output, size=size, act=paddle.activation.BRelu())
+        if use_gru:
+            output = bidirectional_gru_bn_layer(
+                name=str(i),
+                input=output,
+                size=size,
+                act=paddle.activation.BRelu())
+        else:
+            output = bidirectional_simple_rnn_bn_layer(
+                name=str(i),
+                input=output,
+                size=size,
+                act=paddle.activation.BRelu())
     return output
 
 
@@ -124,7 +168,8 @@ def deep_speech2(audio_data,
                  dict_size,
                  num_conv_layers=2,
                  num_rnn_layers=3,
-                 rnn_size=256):
+                 rnn_size=256,
+                 use_gru=True):
     """
     The whole DeepSpeech2 model structure (a simplified version).
 
@@ -140,6 +185,8 @@ def deep_speech2(audio_data,
     :type num_rnn_layers: int
     :param rnn_size: RNN layer size (number of RNN cells).
     :type rnn_size: int
+    :param use_gru: Use gru if set True. Use simple rnn if set False.
+    :type use_gru: bool
     :return: A tuple of an output unnormalized log probability layer (
              before softmax) and a ctc cost layer.
     :rtype: tuple of LayerOutput
@@ -157,7 +204,10 @@ def deep_speech2(audio_data,
         block_y=conv_group_height)
     # rnn group
     rnn_group_output = rnn_group(
-        input=conv2seq, size=rnn_size, num_stacks=num_rnn_layers)
+        input=conv2seq,
+        size=rnn_size,
+        num_stacks=num_rnn_layers,
+        use_gru=use_gru)
     fc = paddle.layer.fc(
         input=rnn_group_output,
         size=dict_size + 1,

model.py

@@ -30,9 +30,9 @@ class DeepSpeech2Model(object):
     """
 
     def __init__(self, vocab_size, num_conv_layers, num_rnn_layers,
-                 rnn_layer_size, pretrained_model_path):
+                 rnn_layer_size, use_gru, pretrained_model_path):
         self._create_network(vocab_size, num_conv_layers, num_rnn_layers,
-                             rnn_layer_size)
+                             rnn_layer_size, use_gru)
         self._create_parameters(pretrained_model_path)
         self._inferer = None
         self._loss_inferer = None
@@ -226,7 +226,7 @@ class DeepSpeech2Model(object):
                 gzip.open(model_path))
 
     def _create_network(self, vocab_size, num_conv_layers, num_rnn_layers,
-                        rnn_layer_size):
+                        rnn_layer_size, use_gru):
         """Create data layers and model network."""
         # paddle.data_type.dense_array is used for variable batch input.
         # The size 161 * 161 is only an placeholder value and the real shape
@@ -243,4 +243,5 @@ class DeepSpeech2Model(object):
             dict_size=vocab_size,
             num_conv_layers=num_conv_layers,
             num_rnn_layers=num_rnn_layers,
-            rnn_size=rnn_layer_size)
+            rnn_size=rnn_layer_size,
+            use_gru=use_gru)

train.py

@@ -37,9 +37,14 @@ parser.add_argument(
     help="RNN layer number. (default: %(default)s)")
 parser.add_argument(
     "--rnn_layer_size",
-    default=512,
+    default=1280,
     type=int,
     help="RNN layer cell number. (default: %(default)s)")
+parser.add_argument(
+    "--use_gru",
+    default=True,
+    type=bool,
+    help="Use GRU or simple RNN. (default: %(default)s)")
 parser.add_argument(
     "--adam_learning_rate",
     default=5e-4,
@@ -170,6 +175,7 @@ def train():
         num_conv_layers=args.num_conv_layers,
         num_rnn_layers=args.num_rnn_layers,
         rnn_layer_size=args.rnn_layer_size,
+        use_gru=args.use_gru,
         pretrained_model_path=args.init_model_path)
     ds2_model.train(
         train_batch_reader=train_batch_reader,

tune.py

@@ -34,6 +34,11 @@ parser.add_argument(
     default=512,
     type=int,
     help="RNN layer cell number. (default: %(default)s)")
+parser.add_argument(
+    "--use_gru",
+    default=True,
+    type=bool,
+    help="Use GRU or simple RNN. (default: %(default)s)")
 parser.add_argument(
     "--use_gpu",
     default=True,
@@ -158,6 +163,7 @@ def tune():
         num_conv_layers=args.num_conv_layers,
         num_rnn_layers=args.num_rnn_layers,
         rnn_layer_size=args.rnn_layer_size,
+        use_gru=args.use_gru,
         pretrained_model_path=args.model_filepath)
 
     # create grid for search