add more comments and update train.py

deep_speech_2/audio_data_utils.py

@@ -247,25 +247,34 @@ class DataGenerator(object):
             new_batch.append((padded_audio, text))
         return new_batch
 
-    def __batch_shuffle__(self, manifest, batch_shuffle_size):
+    def __batch_shuffle__(self, manifest, batch_size):
         """
+        The instances have different lengths and they cannot be
+        combined into a single matrix multiplication. It usually
+        sorts the training examples by length and combines only
+        similarly-sized instances into minibatches, pads with
+        silence when necessary so that all instances in a batch
+        have the same length. This batch shuffle fuction is used
+        to make similarly-sized instances into minibatches and
+        make a batch-wise shuffle.
+
         1. Sort the audio clips by duration.
-        2. Generate a random number `k`, k in [0, batch_shuffle_size).
+        2. Generate a random number `k`, k in [0, batch_size).
         3. Randomly remove `k` instances in order to make different mini-batches,
-           then make minibatches and each minibatch size is batch_shuffle_size.
+           then make minibatches and each minibatch size is batch_size.
         4. Shuffle the minibatches.
 
         :param manifest: manifest file.
         :type manifest: list
-        :param batch_shuffle_size: This size is uesed to generate a random number,
-                                   it usually equals to batch size.
-        :type batch_shuffle_size: int
+        :param batch_size: Batch size. This size is also used for generate
+                           a random number for batch shuffle.
+        :type batch_size: int
         :return: batch shuffled mainifest.
         :rtype: list
         """
         manifest.sort(key=lambda x: x["duration"])
-        shift_len = self.__random__.randint(0, batch_shuffle_size - 1)
-        batch_manifest = zip(*[iter(manifest[shift_len:])] * batch_shuffle_size)
+        shift_len = self.__random__.randint(0, batch_size - 1)
+        batch_manifest = zip(*[iter(manifest[shift_len:])] * batch_size)
         self.__random__.shuffle(batch_manifest)
         batch_manifest = list(sum(batch_manifest, ()))
         res_len = len(manifest) - shift_len - len(batch_manifest)
@@ -327,8 +336,9 @@ class DataGenerator(object):
                           if set True.
         :type sortagrad: bool
         :param batch_shuffle: Shuffle the audio clips if set True. It is
-                              not a thorough instance-wise shuffle,
-                              but a specific batch-wise shuffle.
+                              not a thorough instance-wise shuffle, but a
+                              specific batch-wise shuffle. For more details,
+                              please see `__batch_shuffle__` function.
         :type batch_shuffle: bool
         :return: Batch reader function, producing batches of data when called.
         :rtype: callable

deep_speech_2/train.py

@@ -143,12 +143,12 @@ def train():
     train_batch_reader = train_generator.batch_reader_creator(
         manifest_path=args.train_manifest_path,
         batch_size=args.batch_size,
-        sortagrad=True,
-        shuffle=True)
+        sortagrad=True if args.init_model_path is None else False,
+        batch_shuffle=True)
     test_batch_reader = test_generator.batch_reader_creator(
         manifest_path=args.dev_manifest_path,
         batch_size=args.batch_size,
-        shuffle=False)
+        batch_shuffle=False)
     feeding = train_generator.data_name_feeding()
 
     # create event handler