From d65187ec5245457a43e352f4c0c9930ab2d98225 Mon Sep 17 00:00:00 2001
From: Yifan Yang <64255737+yfyeung@users.noreply.github.com>
Date: Thu, 11 Jul 2024 14:45:35 +0800
Subject: [PATCH] Small fix (#1686)

---
 egs/librispeech/ASR/zipformer/scaling.py | 5 +++--
 egs/librispeech/ASR/zipformer/train.py   | 6 +++---
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/egs/librispeech/ASR/zipformer/scaling.py b/egs/librispeech/ASR/zipformer/scaling.py
index e7c3f4ab1..3c7e0fa4e 100644
--- a/egs/librispeech/ASR/zipformer/scaling.py
+++ b/egs/librispeech/ASR/zipformer/scaling.py
@@ -636,8 +636,9 @@ class ChunkCausalDepthwiseConv1d(torch.nn.Module):
                 )
 
     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.
         """
diff --git a/egs/librispeech/ASR/zipformer/train.py b/egs/librispeech/ASR/zipformer/train.py
index 3797de484..9b6f4a93a 100755
--- a/egs/librispeech/ASR/zipformer/train.py
+++ b/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