Add shuffle type of instance_shuffle and batch_shuffle_clipped.

data_utils/data.py

@@ -80,7 +80,7 @@ class DataGenerator(object):
                              padding_to=-1,
                              flatten=False,
                              sortagrad=False,
-                             batch_shuffle=False):
+                             shuffle_method="batch_shuffle"):
         """
         Batch data reader creator for audio data. Return a callable generator
         function to produce batches of data.
@@ -104,12 +104,22 @@ class DataGenerator(object):
         :param sortagrad: If set True, sort the instances by audio duration
                           in the first epoch for speed up training.
         :type sortagrad: bool
-        :param batch_shuffle: If set True, instances are batch-wise shuffled.
+        :param shuffle_method: Shuffle method. Options:
+                                '' or None: no shuffle.
+                                'instance_shuffle': instance-wise shuffle.
+                                'batch_shuffle': similarly-sized instances are
+                                                 put into batches, and then
+                                                 batch-wise shuffle the batches.
                                                  For more details, please see
                                                  ``_batch_shuffle.__doc__``.
-                              If sortagrad is True, batch_shuffle is disabled
+                                'batch_shuffle_clipped': 'batch_shuffle' with
+                                                         head shift and tail
+                                                         clipping. For more
+                                                         details, please see
+                                                         ``_batch_shuffle``.
+                              If sortagrad is True, shuffle is disabled
                               for the first epoch.
-        :type batch_shuffle: bool
+        :type shuffle_method: None|str
         :return: Batch reader function, producing batches of data when called.
         :rtype: callable
         """
@@ -123,8 +133,20 @@ class DataGenerator(object):
             # sort (by duration) or batch-wise shuffle the manifest
             if self._epoch == 0 and sortagrad:
                 manifest.sort(key=lambda x: x["duration"])
-            elif batch_shuffle:
-                manifest = self._batch_shuffle(manifest, batch_size)
+            else:
+                if shuffle_method == "batch_shuffle":
+                    manifest = self._batch_shuffle(
+                        manifest, batch_size, clipped=False)
+                elif shuffle_method == "batch_shuffle_clipped":
+                    manifest = self._batch_shuffle(
+                        manifest, batch_size, clipped=True)
+                elif shuffle_method == "instance_shuffle":
+                    self._rng.shuffle(manifest)
+                elif not shuffle_method:
+                    pass
+                else:
+                    raise ValueError("Unknown shuffle method %s." %
+                                     shuffle_method)
             # prepare batches
             instance_reader = self._instance_reader_creator(manifest)
             batch = []
@@ -218,7 +240,7 @@ class DataGenerator(object):
             new_batch.append((padded_audio, text))
         return new_batch
 
-    def _batch_shuffle(self, manifest, batch_size):
+    def _batch_shuffle(self, manifest, batch_size, clipped=False):
         """Put similarly-sized instances into minibatches for better efficiency
         and make a batch-wise shuffle.
 
@@ -233,6 +255,9 @@ class DataGenerator(object):
         :param batch_size: Batch size. This size is also used for generate
                            a random number for batch shuffle.
         :type batch_size: int
+        :param clipped: Whether to clip the heading (small shift) and trailing
+                        (incomplete batch) instances.
+        :type clipped: bool
         :return: Batch shuffled mainifest.
         :rtype: list
         """
@@ -241,6 +266,7 @@ class DataGenerator(object):
         batch_manifest = zip(*[iter(manifest[shift_len:])] * batch_size)
         self._rng.shuffle(batch_manifest)
         batch_manifest = list(sum(batch_manifest, ()))
+        if not clipped:
             res_len = len(manifest) - shift_len - len(batch_manifest)
             batch_manifest.extend(manifest[-res_len:])
             batch_manifest.extend(manifest[0:shift_len])

datasets/librispeech/librispeech.py

@@ -37,8 +37,7 @@ MD5_TRAIN_CLEAN_100 = "2a93770f6d5c6c964bc36631d331a522"
 MD5_TRAIN_CLEAN_360 = "c0e676e450a7ff2f54aeade5171606fa"
 MD5_TRAIN_OTHER_500 = "d1a0fd59409feb2c614ce4d30c387708"
 
-parser = argparse.ArgumentParser(
-    description='Downloads and prepare LibriSpeech dataset.')
+parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
     "--target_dir",
     default=DATA_HOME + "/Libri",

decoder.py

@@ -8,8 +8,7 @@ from itertools import groupby
 
 
 def ctc_best_path_decode(probs_seq, vocabulary):
-    """
-    Best path decoding, also called argmax decoding or greedy decoding.
+    """Best path decoding, also called argmax decoding or greedy decoding.
     Path consisting of the most probable tokens are further post-processed to
     remove consecutive repetitions and all blanks.
 
@@ -38,8 +37,7 @@ def ctc_best_path_decode(probs_seq, vocabulary):
 
 
 def ctc_decode(probs_seq, vocabulary, method):
-    """
-    CTC-like sequence decoding from a sequence of likelihood probablilites. 
+    """CTC-like sequence decoding from a sequence of likelihood probablilites.
 
     :param probs_seq: 2-D list of probabilities over the vocabulary for each
                       character. Each element is a list of float probabilities

infer.py

@@ -10,9 +10,9 @@ import paddle.v2 as paddle
 from data_utils.data import DataGenerator
 from model import deep_speech2
 from decoder import ctc_decode
+import utils
 
-parser = argparse.ArgumentParser(
-    description='Simplified version of DeepSpeech2 inference.')
+parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
     "--num_samples",
     default=10,
@@ -62,9 +62,7 @@ args = parser.parse_args()
 
 
 def infer():
-    """
-    Max-ctc-decoding for DeepSpeech2.
-    """
+    """Max-ctc-decoding for DeepSpeech2."""
     # initialize data generator
     data_generator = DataGenerator(
         vocab_filepath=args.vocab_filepath,
@@ -98,7 +96,7 @@ def infer():
         manifest_path=args.decode_manifest_path,
         batch_size=args.num_samples,
         sortagrad=False,
-        batch_shuffle=False)
+        shuffle_method=None)
     infer_data = batch_reader().next()
 
     # run inference
@@ -123,6 +121,7 @@ def infer():
 
 
 def main():
+    utils.print_arguments(args)
     paddle.init(use_gpu=args.use_gpu, trainer_count=1)
     infer()
 

train.py

@@ -12,6 +12,7 @@ import distutils.util
 import paddle.v2 as paddle
 from model import deep_speech2
 from data_utils.data import DataGenerator
+import utils
 
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
@@ -51,6 +52,12 @@ parser.add_argument(
     default=True,
     type=distutils.util.strtobool,
     help="Use sortagrad or not. (default: %(default)s)")
+parser.add_argument(
+    "--shuffle_method",
+    default='instance_shuffle',
+    type=str,
+    help="Shuffle method: 'instance_shuffle', 'batch_shuffle', "
+    "'batch_shuffle_batch'. (default: %(default)s)")
 parser.add_argument(
     "--trainer_count",
     default=4,
@@ -93,9 +100,7 @@ args = parser.parse_args()
 
 
 def train():
-    """
-    DeepSpeech2 training.
-    """
+    """DeepSpeech2 training."""
 
     # initialize data generator
     def data_generator():
@@ -145,13 +150,13 @@ def train():
         batch_size=args.batch_size,
         min_batch_size=args.trainer_count,
         sortagrad=args.use_sortagrad if args.init_model_path is None else False,
-        batch_shuffle=True)
+        shuffle_method=args.shuffle_method)
     test_batch_reader = test_generator.batch_reader_creator(
         manifest_path=args.dev_manifest_path,
         batch_size=args.batch_size,
         min_batch_size=1,  # must be 1, but will have errors.
         sortagrad=False,
-        batch_shuffle=False)
+        shuffle_method=None)
 
     # create event handler
     def event_handler(event):
@@ -186,6 +191,7 @@ def train():
 
 
 def main():
+    utils.print_arguments(args)
     paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
     train()
 

utils.py

+"""Contains common utility functions."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+def print_arguments(args):
+    """Print argparse's arguments.
+
+    Usage:
+
+    .. code-block:: python
+        
+        parser = argparse.ArgumentParser()
+        parser.add_argument("name", default="Jonh", type=str, help="User name.")
+        args = parser.parse_args() 
+        print_arguments(args)
+    
+    :param args: Input argparse.Namespace for printing.
+    :type args: argparse.Namespace
+    """
+    print("-----  Configuration Arguments -----")
+    for arg, value in vars(args).iteritems():
+        print("%s: %s" % (arg, value))
+    print("------------------------------------")