Small fix

egs/librispeech/ASR/zipformer/scaling.py

@@ -636,8 +636,9 @@ def __init__(
                 )
 
     def forward(self, x: Tensor, chunk_size: int = -1) -> Tensor:
-        """
-             Forward function.  Args:
+        """Forward function.
+
+        Args:
                x: a Tensor of shape (batch_size, channels, seq_len)
         chunk_size: the chunk size, in frames; does not have to divide seq_len exactly.
         """

egs/librispeech/ASR/zipformer/train.py

@@ -406,7 +406,7 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; " "2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
     )
 
     parser.add_argument(
@@ -429,7 +429,7 @@ def get_parser():
         "--am-scale",
         type=float,
         default=0.0,
-        help="The scale to smooth the loss with am (output of encoder network)" "part.",
+        help="The scale to smooth the loss with am (output of encoder network) part.",
     )
 
     parser.add_argument(
@@ -848,7 +848,7 @@ def compute_loss(
         True for training. False for validation. When it is True, this
         function enables autograd during computation; when it is False, it
         disables autograd.
-     warmup: a floating point value which increases throughout training;
+      warmup: a floating point value which increases throughout training;
         values >= 1.0 are fully warmed up and have all modules present.
     """
     device = model.device if isinstance(model, DDP) else next(model.parameters()).device