format

deepspeech/exps/deepspeech2/model.py

@@ -187,7 +187,8 @@ class DeepSpeech2Trainer(Trainer):
                 sortagrad=config.data.sortagrad,
                 shuffle_method=config.data.shuffle_method)
 
-        collate_fn = SpeechCollator(keep_transcription_text=False, return_utts=False)
+        collate_fn = SpeechCollator(
+            keep_transcription_text=False, return_utts=False)
         self.train_loader = DataLoader(
             train_dataset,
             batch_sampler=batch_sampler,
@@ -220,7 +221,8 @@ class DeepSpeech2Trainer(Trainer):
             batch_size=config.decoding.batch_size,
             shuffle=False,
             drop_last=False,
-            collate_fn=SpeechCollator(keep_transcription_text=True, return_utts=True))
+            collate_fn=SpeechCollator(
+                keep_transcription_text=True, return_utts=True))
         logger.info("Setup test Dataloader!")
 
 

deepspeech/exps/u2/model.py

@@ -222,7 +222,8 @@ class U2Trainer(Trainer):
         config.data.augmentation_config = ""
         dev_dataset = ManifestDataset.from_config(config)
 
-        collate_fn = SpeechCollator(keep_transcription_text=False, return_utts=False)
+        collate_fn = SpeechCollator(
+            keep_transcription_text=False, return_utts=False)
         if self.parallel:
             batch_sampler = SortagradDistributedBatchSampler(
                 train_dataset,
@@ -272,7 +273,8 @@ class U2Trainer(Trainer):
             batch_size=config.decoding.batch_size,
             shuffle=False,
             drop_last=False,
-            collate_fn=SpeechCollator(keep_transcription_text=True, return_utts=True))
+            collate_fn=SpeechCollator(
+                keep_transcription_text=True, return_utts=True))
         logger.info("Setup train/valid/test Dataloader!")
 
     def setup_model(self):

deepspeech/frontend/augmentor/augmentation.py

@@ -13,9 +13,9 @@
 # limitations under the License.
 """Contains the data augmentation pipeline."""
 import json
-from pprint import pformat
 from collections.abc import Sequence
 from inspect import signature
+from pprint import pformat
 
 import numpy as np
 
@@ -112,7 +112,8 @@ class AugmentationPipeline():
             'audio')
         self._spec_augmentors, self._spec_rates = self._parse_pipeline_from(
             'feature')
-        logger.info(f"Augmentation: {pformat(list(zip(self._augmentors, self._rates)))}")
+        logger.info(
+            f"Augmentation: {pformat(list(zip(self._augmentors, self._rates)))}")
 
     def __call__(self, xs, uttid_list=None, **kwargs):
         if not isinstance(xs, Sequence):
@@ -203,7 +204,7 @@ class AugmentationPipeline():
             aug_confs = all_confs
         else:
             raise ValueError(f"Not support: {aug_type}")
-            
+
         augmentors = [
             self._get_augmentor(config["type"], config["params"])
             for config in aug_confs

deepspeech/frontend/featurizer/text_featurizer.py

@@ -12,17 +12,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Contains the text featurizer class."""
-import sentencepiece as spm
 from pprint import pformat
 
+import sentencepiece as spm
+
+from ..utility import BLANK
 from ..utility import EOS
+from ..utility import load_dict
+from ..utility import MASKCTC
+from ..utility import SOS
 from ..utility import SPACE
 from ..utility import UNK
-from ..utility import SOS
-from ..utility import BLANK
-from ..utility import MASKCTC
-from ..utility import load_dict
-
 from deepspeech.utils.log import Log
 
 logger = Log(__name__).getlog()

deepspeech/io/collator.py

@@ -81,4 +81,4 @@ class SpeechCollator():
         if self.return_utts:
             return padded_audios, audio_lens, padded_texts, text_lens, utts
         else:
-            return padded_audios, audio_lens, padded_texts, text_lens
\ No newline at end of file
+            return padded_audios, audio_lens, padded_texts, text_lens

deepspeech/modules/loss.py

@@ -23,7 +23,11 @@ __all__ = ['CTCLoss', "LabelSmoothingLoss"]
 
 
 class CTCLoss(nn.Layer):
-    def __init__(self, blank=0, reduction='sum', batch_average=False, grad_norm_type=None):
+    def __init__(self,
+                 blank=0,
+                 reduction='sum',
+                 batch_average=False,
+                 grad_norm_type=None):
         super().__init__()
         # last token id as blank id
         self.loss = nn.CTCLoss(blank=blank, reduction=reduction)

deepspeech/training/trainer.py

@@ -148,8 +148,8 @@ class Trainer():
             "lr": self.optimizer.get_lr()
         })
         Checkpoint().save_parameters(self.checkpoint_dir, self.iteration
-                                   if tag is None else tag, self.model,
-                                   self.optimizer, infos)
+                                     if tag is None else tag, self.model,
+                                     self.optimizer, infos)
 
     def resume_or_scratch(self):
         """Resume from latest checkpoint at checkpoints in the output