diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index 7f3d5867..68465f93 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -194,8 +194,8 @@ def align(
             aligned_segments.append(aligned_seg)
             continue
 
-        if t1 >= MAX_DURATION or t2 - t1 < 0.02:
-            print("Failed to align segment: original start time longer than audio duration, skipping...")
+        if t1 >= MAX_DURATION:
+            print(f'Failed to align segment ("{segment["text"]}"): original start time longer than audio duration, skipping...')
             aligned_segments.append(aligned_seg)
             continue
 
@@ -207,17 +207,17 @@ def align(
 
         # TODO: Probably can get some speedup gain with batched inference here
         waveform_segment = audio[:, f1:f2]
-
+        # Handle the minimum input length for wav2vec2 models
+        if waveform_segment.shape[-1] < 400:
+            lengths = torch.as_tensor([waveform_segment.shape[-1]]).to(device)
+            waveform_segment = torch.nn.functional.pad(
+                waveform_segment, (0, 400 - waveform_segment.shape[-1])
+            )
+        else:
+            lengths = None
+            
         with torch.inference_mode():
             if model_type == "torchaudio":
-                # Handle the minimum input length for torchaudio wav2vec2 models
-                if waveform_segment.shape[-1] < 400:
-                    lengths = torch.as_tensor([waveform_segment.shape[-1]]).to(device)
-                    waveform_segment = torch.nn.functional.pad(
-                        waveform_segment, (0, 400 - waveform_segment.shape[-1])
-                    )
-                else:
-                    lengths = None
                 emissions, _ = model(waveform_segment.to(device), lengths=lengths)
             elif model_type == "huggingface":
                 emissions = model(waveform_segment.to(device)).logits