Add multi-threading support for DS2 data generator.

data_utils/data.py

@@ -44,6 +44,8 @@ class DataGenerator(object):
     :types max_freq: None|float
     :param specgram_type: Specgram feature type. Options: 'linear'.
     :type specgram_type: str
+    :param num_threads: Number of CPU threads for processing data.
+    :type num_threads: int
     :param random_seed: Random seed.
     :type random_seed: int
     """
@@ -58,6 +60,7 @@ class DataGenerator(object):
                  window_ms=20.0,
                  max_freq=None,
                  specgram_type='linear',
+                 num_threads=12,
                  random_seed=0):
         self._max_duration = max_duration
         self._min_duration = min_duration
@@ -70,6 +73,7 @@ class DataGenerator(object):
             stride_ms=stride_ms,
             window_ms=window_ms,
             max_freq=max_freq)
+        self._num_threads = num_threads
         self._rng = random.Random(random_seed)
         self._epoch = 0
 
@@ -207,10 +211,14 @@ class DataGenerator(object):
 
         def reader():
             for instance in manifest:
-                yield self._process_utterance(instance["audio_filepath"],
+                yield instance
+
+        def mapper(instance):
+            return self._process_utterance(instance["audio_filepath"],
                                            instance["text"])
 
-        return reader
+        return paddle.reader.xmap_readers(
+            mapper, reader, self._num_threads, 1024, order=True)
 
     def _padding_batch(self, batch, padding_to=-1, flatten=False):
         """

data_utils/speech.py

@@ -94,7 +94,7 @@ class SpeechSegment(AudioSegment):
         return cls(samples, sample_rate, transcripts)
 
     @classmethod
-    def slice_from_file(cls, filepath, start=None, end=None, transcript):
+    def slice_from_file(cls, filepath, transcript, start=None, end=None):
         """Loads a small section of an speech without having to load
         the entire file into the memory which can be incredibly wasteful.
 

infer.py

@@ -38,6 +38,11 @@ parser.add_argument(
     default=True,
     type=distutils.util.strtobool,
     help="Use gpu or not. (default: %(default)s)")
+parser.add_argument(
+    "--num_threads_data",
+    default=12,
+    type=int,
+    help="Number of cpu threads for preprocessing data. (default: %(default)s)")
 parser.add_argument(
     "--mean_std_filepath",
     default='mean_std.npz',
@@ -67,7 +72,8 @@ def infer():
     data_generator = DataGenerator(
         vocab_filepath=args.vocab_filepath,
         mean_std_filepath=args.mean_std_filepath,
-        augmentation_config='{}')
+        augmentation_config='{}',
+        num_threads=args.num_threads_data)
 
     # create network config
     # paddle.data_type.dense_array is used for variable batch input.

train.py

@@ -52,6 +52,18 @@ parser.add_argument(
     default=True,
     type=distutils.util.strtobool,
     help="Use sortagrad or not. (default: %(default)s)")
+parser.add_argument(
+    "--max_duration",
+    default=100.0,
+    type=float,
+    help="Audios with duration larger than this will be discarded. "
+    "(default: %(default)s)")
+parser.add_argument(
+    "--min_duration",
+    default=0.0,
+    type=float,
+    help="Audios with duration smaller than this will be discarded. "
+    "(default: %(default)s)")
 parser.add_argument(
     "--shuffle_method",
     default='instance_shuffle',
@@ -63,6 +75,11 @@ parser.add_argument(
     default=4,
     type=int,
     help="Trainer number. (default: %(default)s)")
+parser.add_argument(
+    "--num_threads_data",
+    default=12,
+    type=int,
+    help="Number of cpu threads for preprocessing data. (default: %(default)s)")
 parser.add_argument(
     "--mean_std_filepath",
     default='mean_std.npz',
@@ -107,7 +124,10 @@ def train():
         return DataGenerator(
             vocab_filepath=args.vocab_filepath,
             mean_std_filepath=args.mean_std_filepath,
-            augmentation_config=args.augmentation_config)
+            augmentation_config=args.augmentation_config,
+            max_duration=args.max_duration,
+            min_duration=args.min_duration,
+            num_threads=args.num_threads_data)
 
     train_generator = data_generator()
     test_generator = data_generator()