diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/frame_reducer.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/frame_reducer.py
index 0841f7cf1..1517a494f 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/frame_reducer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/frame_reducer.py
@@ -74,27 +74,31 @@ class FrameReducer(nn.Module):
         padding_mask = make_pad_mask(x_lens)
         non_blank_mask = (ctc_output[:, :, blank_id] < math.log(0.9)) * (~padding_mask)
 
-        if y_lens is not None:
+        if y_lens is not None or self.training is False:
             # Limit the maximum number of reduced frames
-            limit_lens = T - y_lens
+            if y_lens is not None:
+                limit_lens = T - y_lens
+            else:
+                # In eval mode, ensure audio that is completely silent does not make any errors
+                limit_lens = T - torch.ones_like(x_lens)
             max_limit_len = limit_lens.max().int()
             fake_limit_indexes = torch.topk(
                 ctc_output[:, :, blank_id], max_limit_len
             ).indices
-            T = (
+            _T = (
                 torch.arange(max_limit_len)
                 .expand_as(
                     fake_limit_indexes,
                 )
                 .to(device=x.device)
             )
-            T = torch.remainder(T, limit_lens.unsqueeze(1))
-            limit_indexes = torch.gather(fake_limit_indexes, 1, T)
-            limit_mask = torch.full_like(
+            _T = torch.remainder(_T, limit_lens.unsqueeze(1))
+            limit_indexes = torch.gather(fake_limit_indexes, 1, _T)
+            limit_mask = (torch.full_like(
                 non_blank_mask,
-                False,
+                0,
                 device=x.device,
-            ).scatter_(1, limit_indexes, True)
+            ).scatter_(1, limit_indexes, 1) == 1)
 
             non_blank_mask = non_blank_mask | ~limit_mask
 
@@ -108,7 +112,7 @@ class FrameReducer(nn.Module):
             )
             - out_lens
         )
-        max_pad_len = pad_lens_list.max()
+        max_pad_len = int(pad_lens_list.max().item())
 
         out = F.pad(x, (0, 0, 0, max_pad_len))