Fix code style

egs/aishell/ASR/conformer_ctc/test_transformer.py

@@ -17,17 +17,16 @@
 
 
 import torch
+from torch.nn.utils.rnn import pad_sequence
 from transformer import (
     Transformer,
+    add_eos,
+    add_sos,
+    decoder_padding_mask,
     encoder_padding_mask,
     generate_square_subsequent_mask,
-    decoder_padding_mask,
-    add_sos,
-    add_eos,
 )
 
-from torch.nn.utils.rnn import pad_sequence
-
 
 def test_encoder_padding_mask():
     supervisions = {
@@ -82,11 +81,7 @@ def test_decoder_padding_mask():
     y = pad_sequence(x, batch_first=True, padding_value=-1)
     mask = decoder_padding_mask(y, ignore_id=-1)
     expected_mask = torch.tensor(
-        [
-            [False, False, True],
-            [False, True, True],
-            [False, False, False],
-        ]
+        [[False, False, True], [False, True, True], [False, False, False]]
     )
     assert torch.all(torch.eq(mask, expected_mask))
 

egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -308,17 +308,13 @@ class AishellAsrDataModule(DataModule):
     @lru_cache()
     def train_cuts(self) -> CutSet:
         logging.info("About to get train cuts")
-        cuts_train = load_manifest(
-            self.args.feature_dir / "cuts_train.json.gz"
-        )
+        cuts_train = load_manifest(self.args.feature_dir / "cuts_train.json.gz")
         return cuts_train
 
     @lru_cache()
     def valid_cuts(self) -> CutSet:
         logging.info("About to get dev cuts")
-        cuts_valid = load_manifest(
-            self.args.feature_dir / "cuts_dev.json.gz"
-        )
+        cuts_valid = load_manifest(self.args.feature_dir / "cuts_dev.json.gz")
         return cuts_valid
 
     @lru_cache()

egs/aishell/ASR/tdnn_lstm_ctc/pretrained.py

@@ -29,10 +29,7 @@ import torchaudio
 from model import TdnnLstm
 from torch.nn.utils.rnn import pad_sequence
 
-from icefall.decode import (
-    get_lattice,
-    one_best_decoding,
-)
+from icefall.decode import get_lattice, one_best_decoding
 from icefall.utils import AttributeDict, get_texts
 
 
@@ -203,7 +200,7 @@ def main():
         subsampling_factor=params.subsampling_factor,
     )
 
-    assert(params.method == "1best")
+    assert params.method == "1best"
     logging.info("Use HLG decoding")
     best_path = one_best_decoding(
         lattice=lattice, use_double_scores=params.use_double_scores