linter changes

swr2_asr/inference.py

@@ -109,7 +109,7 @@ def main(config_path: str, file_path: str, target_path: Union[str, None] = None)
             target = target.replace("!", "")
 
         print("---------")
-        print(f"Prediction:\n\{preds}")
+        print(f"Prediction:\n{preds}")
         print("---------")
         print(f"Target:\n{target}")
         print("---------")

swr2_asr/model_deep_speech.py

@@ -11,7 +11,7 @@ class CNNLayerNorm(nn.Module):
     """Layer normalization built for cnns input"""
 
     def __init__(self, n_feats):
-        super(CNNLayerNorm, self).__init__()
+        super().__init__()
         self.layer_norm = nn.LayerNorm(n_feats)
 
     def forward(self, data):
@@ -27,7 +27,7 @@ class ResidualCNN(nn.Module):
     """
 
     def __init__(self, in_channels, out_channels, kernel, stride, dropout, n_feats):
-        super(ResidualCNN, self).__init__()
+        super().__init__()
 
         self.cnn1 = nn.Conv2d(in_channels, out_channels, kernel, stride, padding=kernel // 2)
         self.cnn2 = nn.Conv2d(out_channels, out_channels, kernel, stride, padding=kernel // 2)
@@ -55,7 +55,7 @@ class BidirectionalGRU(nn.Module):
     """Bidirectional GRU layer"""
 
     def __init__(self, rnn_dim, hidden_size, dropout, batch_first):
-        super(BidirectionalGRU, self).__init__()
+        super().__init__()
 
         self.BiGRU = nn.GRU(  # pylint: disable=invalid-name
             input_size=rnn_dim,
@@ -82,7 +82,7 @@ class SpeechRecognitionModel(nn.Module):
     def __init__(
         self, n_cnn_layers, n_rnn_layers, rnn_dim, n_class, n_feats, stride=2, dropout=0.1
     ):
-        super(SpeechRecognitionModel, self).__init__()
+        super().__init__()
         n_feats = n_feats // 2
         self.cnn = nn.Conv2d(
             1, 32, 3, stride=stride, padding=3 // 2

swr2_asr/utils/visualization.py

@@ -14,7 +14,9 @@ def plot(path):
     epoch = 5
     while True:
         try:
-            current_state = torch.load(path + str(epoch), map_location=torch.device("cpu"))
+            current_state = torch.load(
+                path + str(epoch), map_location=torch.device("cpu")
+            )  # pylint: disable=no-member
         except FileNotFoundError:
             break
         train_losses.append((epoch, current_state["train_loss"].item()))