Add function docs for layer.py and model.py and update other details.

decoder.py

@@ -205,9 +205,9 @@ def ctc_beam_search_decoder_batch(probs_split,
     :type num_processes: int
     :param cutoff_prob: Cutoff probability in pruning,
                         default 1.0, no pruning.
+    :type cutoff_prob: float
     :param num_processes: Number of parallel processes.
     :type num_processes: int
-    :type cutoff_prob: float
     :param ext_scoring_func: External scoring function for
                             partially decoded sentence, e.g. word count
                             or language model.

infer.py

@@ -40,7 +40,7 @@ parser.add_argument(
     help="Use gpu or not. (default: %(default)s)")
 parser.add_argument(
     "--num_threads_data",
-    default=multiprocessing.cpu_count(),
+    default=1,
     type=int,
     help="Number of cpu threads for preprocessing data. (default: %(default)s)")
 parser.add_argument(

layer.py

@@ -5,13 +5,27 @@ from __future__ import print_function
 
 import paddle.v2 as paddle
 
-DISABLE_CUDNN_BATCH_NORM = True
-
 
 def conv_bn_layer(input, filter_size, num_channels_in, num_channels_out, stride,
                   padding, act):
-    """
-    Convolution layer with batch normalization.
+    """Convolution layer with batch normalization.
+
+    :param input: Input layer.
+    :type input: LayerOutput
+    :param filter_size: The x dimension of a filter kernel. Or input a tuple for
+                        two image dimension.
+    :type filter_size: int|tuple|list
+    :param num_channels_in: Number of input channels.
+    :type num_channels_in: int
+    :type num_channels_out: Number of output channels.
+    :type num_channels_in: out
+    :param padding: The x dimension of the padding. Or input a tuple for two
+                    image dimension.
+    :type padding: int|tuple|list
+    :param act: Activation type.
+    :type act: BaseActivation
+    :return: Batch norm layer after convolution layer.
+    :rtype: LayerOutput
     """
     conv_layer = paddle.layer.img_conv(
         input=input,
@@ -22,32 +36,30 @@ def conv_bn_layer(input, filter_size, num_channels_in, num_channels_out, stride,
         padding=padding,
         act=paddle.activation.Linear(),
         bias_attr=False)
-    if DISABLE_CUDNN_BATCH_NORM:
-        # temopary patch, need to be removed.
-        return paddle.layer.batch_norm(
-            input=conv_layer, act=act, batch_norm_type="batch_norm")
-    else:
-        return paddle.layer.batch_norm(input=conv_layer, act=act)
+    return paddle.layer.batch_norm(input=conv_layer, act=act)
 
 
 def bidirectional_simple_rnn_bn_layer(name, input, size, act):
-    """
-    Bidirectonal simple rnn layer with sequence-wise batch normalization.
+    """Bidirectonal simple rnn 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, act=paddle.activation.Linear(), bias_attr=False)
     # batch norm is only performed on input-state projection 
-    if DISABLE_CUDNN_BATCH_NORM:
-        # temopary patch, need to be removed.
-        input_proj_bn = paddle.layer.batch_norm(
-            input=input_proj,
-            act=paddle.activation.Linear(),
-            batch_norm_type="batch_norm")
-    else:
-        input_proj_bn = paddle.layer.batch_norm(
-            input=input_proj, act=paddle.activation.Linear())
+    input_proj_bn = paddle.layer.batch_norm(
+        input=input_proj, act=paddle.activation.Linear())
     # forward and backward in time
     forward_simple_rnn = paddle.layer.recurrent(
         input=input_proj_bn, act=act, reverse=False)
@@ -57,8 +69,14 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, act):
 
 
 def conv_group(input, num_stacks):
-    """
-    Convolution group with several stacking convolution layers.
+    """Convolution group with stacked convolution layers.
+
+    :param input: Input layer.
+    :type input: LayerOutput
+    :param num_stacks: Number of stacked convolution layers.
+    :type num_stacks: int
+    :return: Output layer of the convolution group.
+    :rtype: LayerOutput
     """
     conv = conv_bn_layer(
         input=input,
@@ -83,8 +101,16 @@ def conv_group(input, num_stacks):
 
 
 def rnn_group(input, size, num_stacks):
-    """
-    RNN group with several stacking RNN layers.
+    """RNN group with stacked bidirectional simple RNN layers.
+
+    :param input: Input layer.
+    :type input: LayerOutput
+    :param size: Number of RNN cells in each layer.
+    :type size: int
+    :param num_stacks: Number of stacked rnn layers.
+    :type num_stacks: int
+    :return: Output layer of the RNN group.
+    :rtype: LayerOutput
     """
     output = input
     for i in xrange(num_stacks):
@@ -114,12 +140,8 @@ 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 is_inference: False in the training mode, and True in the
-                         inferene mode.
-    :type is_inference: bool
-    :return: If is_inference set False, return a ctc cost layer;
-             if is_inference set True, return a sequence layer of output
-             probability distribution.
+    :return: A tuple of an output unnormalized log probability layer (
+             before softmax) and a ctc cost layer.
     :rtype: tuple of LayerOutput
     """
     # convolution group

model.py

@@ -14,6 +14,21 @@ from layer import *
 
 
 class DeepSpeech2Model(object):
+    """DeepSpeech2Model class.
+
+    :param vocab_size: Decoding vocabulary size.
+    :type vocab_size: int
+    :param num_conv_layers: Number of stacking convolution layers.
+    :type num_conv_layers: int
+    :param num_rnn_layers: Number of stacking RNN layers.
+    :type num_rnn_layers: int
+    :param rnn_layer_size: RNN layer size (number of RNN cells).
+    :type rnn_layer_size: int
+    :param pretrained_model_path: Pretrained model path. If None, will train
+                                  from stratch.
+    :type pretrained_model_path: basestring|None
+    """
+
     def __init__(self, vocab_size, num_conv_layers, num_rnn_layers,
                  rnn_layer_size, pretrained_model_path):
         self._create_network(vocab_size, num_conv_layers, num_rnn_layers,
@@ -29,8 +44,33 @@ class DeepSpeech2Model(object):
               learning_rate,
               gradient_clipping,
               num_passes,
-              num_iterations_print=100,
-              output_model_dir='checkpoints'):
+              output_model_dir,
+              num_iterations_print=100):
+        """Train the model.
+
+        :param train_batch_reader: Train data reader.
+        :type train_batch_reader: callable
+        :param dev_batch_reader: Validation data reader.
+        :type dev_batch_reader: callable
+        :param feeding_dict: Feeding is a map of field name and tuple index
+                             of the data that reader returns.
+        :type feeding_dict: dict|list
+        :param learning_rate: Learning rate for ADAM optimizer.
+        :type learning_rate: float
+        :param gradient_clipping: Gradient clipping threshold.
+        :type gradient_clipping: float
+        :param num_passes: Number of training epochs.
+        :type num_passes: int
+        :param num_iterations_print: Number of training iterations for printing
+                                     a training loss.
+        :type rnn_iteratons_print: int
+        :param output_model_dir: Directory for saving the model (every pass).
+        :type output_model_dir: basestring
+        """
+        # prepare model output directory
+        if not os.path.exists(output_model_dir):
+            os.mkdir(output_model_dir)
+
         # prepare optimizer and trainer
         optimizer = paddle.optimizer.Adam(
             learning_rate=learning_rate,
@@ -81,6 +121,34 @@ class DeepSpeech2Model(object):
     def infer_batch(self, infer_data, decode_method, beam_alpha, beam_beta,
                     beam_size, cutoff_prob, vocab_list, language_model_path,
                     num_processes):
+        """Model inference. Infer the transcription for a batch of speech
+        utterances.
+
+        :param infer_data: List of utterances to infer, with each utterance a
+                           tuple of audio features and transcription text (empty
+                           string).
+        :type infer_data: list
+        :param decode_method: Decoding method name, 'best_path' or
+                              'beam search'.
+        :param decode_method: string
+        :param beam_alpha: Parameter associated with language model.
+        :type beam_alpha: float
+        :param beam_beta: Parameter associated with word count.
+        :type beam_beta: float
+        :param beam_size: Width for Beam search.
+        :type beam_size: int
+        :param cutoff_prob: Cutoff probability in pruning,
+                            default 1.0, no pruning.
+        :type cutoff_prob: float
+        :param vocab_list: List of tokens in the vocabulary, for decoding.
+        :type vocab_list: list
+        :param language_model_path: Filepath for language model.
+        :type language_model_path: basestring|None
+        :param num_processes: Number of processes (CPU) for decoder.
+        :type num_processes: int
+        :return: List of transcription texts.
+        :rtype: List of basestring
+        """
         # define inferer
         if self._inferer == None:
             self._inferer = paddle.inference.Inference(
@@ -126,6 +194,7 @@ class DeepSpeech2Model(object):
         return results
 
     def _create_parameters(self, model_path=None):
+        """Load or create model parameters."""
         if model_path is None:
             self._parameters = paddle.parameters.create(self._loss)
         else:
@@ -134,6 +203,7 @@ class DeepSpeech2Model(object):
 
     def _create_network(self, vocab_size, num_conv_layers, num_rnn_layers,
                         rnn_layer_size):
+        """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
         # of input batch data will be induced during training.

setup.sh

@@ -26,7 +26,4 @@ if [ $? != 0 ]; then
     rm libsndfile-1.0.28.tar.gz
 fi
 
-# prepare ./checkpoints
-mkdir checkpoints
-
 echo "Install all dependencies successfully."

train.py

@@ -116,6 +116,11 @@ parser.add_argument(
     help="If set None, the training will start from scratch. "
     "Otherwise, the training will resume from "
     "the existing model of this path. (default: %(default)s)")
+parser.add_argument(
+    "--output_model_dir",
+    default="./checkpoints",
+    type=str,
+    help="Directory for saving models. (default: %(default)s)")
 parser.add_argument(
     "--augmentation_config",
     default='[{"type": "shift", '
@@ -169,7 +174,8 @@ def train():
         learning_rate=args.adam_learning_rate,
         gradient_clipping=400,
         num_passes=args.num_passes,
-        num_iterations_print=args.num_iterations_print)
+        num_iterations_print=args.num_iterations_print,
+        output_model_dir=args.output_model_dir)
 
 
 def main():

tune.py

@@ -46,7 +46,7 @@ parser.add_argument(
     help="Trainer number. (default: %(default)s)")
 parser.add_argument(
     "--num_threads_data",
-    default=multiprocessing.cpu_count(),
+    default=1,
     type=int,
     help="Number of cpu threads for preprocessing data. (default: %(default)s)")
 parser.add_argument(
@@ -67,7 +67,7 @@ parser.add_argument(
     help="Manifest path for normalizer. (default: %(default)s)")
 parser.add_argument(
     "--tune_manifest_path",
-    default='datasets/manifest.test',
+    default='datasets/manifest.dev',
     type=str,
     help="Manifest path for tuning. (default: %(default)s)")
 parser.add_argument(