diff --git a/whisperx/alignment.py b/whisperx/alignment.py
index b77a7f1bf..50cb3d9dc 100644
--- a/whisperx/alignment.py
+++ b/whisperx/alignment.py
@@ -415,8 +415,6 @@ def get_wildcard_emission(frame_emission, tokens, blank_id):
     Returns:
         tensor: Maximum probability score for each token position
     """
-    assert 0 <= blank_id < len(frame_emission)
-
     # Convert tokens to a tensor if they are not already
     tokens = torch.tensor(tokens) if not isinstance(tokens, torch.Tensor) else tokens
 
@@ -424,17 +422,20 @@ def get_wildcard_emission(frame_emission, tokens, blank_id):
     wildcard_mask = (tokens == -1)
 
     # Get scores for non-wildcard positions
-    regular_scores = frame_emission[tokens.clamp(min=0)]  # clamp to avoid -1 index
+    # regular_scores = frame_emission[tokens.clamp(min=0).long()]  # clamp to avoid -1 index
+    V = frame_emission.size(0)
+    assert V > 0 and 0 <= blank_id < V, "empty emissions or invalid blank_id"
 
-    # Create a mask and compute the maximum value without modifying frame_emission
-    max_valid_score = frame_emission.clone()   # Create a copy
-    max_valid_score[blank_id] = float('-inf')  # Modify the copy to exclude the blank token
-    max_valid_score = max_valid_score.max()
+    t = torch.as_tensor(tokens)
+    wildcard_mask = t.eq(-1)                  # capture wildcards BEFORE clamping
+    idx = t.clamp(0, V - 1).long()            # clamp both ends [0..V-1]
+    regular_scores = frame_emission[idx]
 
-    # Use where operation to combine results
-    result = torch.where(wildcard_mask, max_valid_score, regular_scores)
+    max_valid = frame_emission.clone()
+    max_valid[blank_id] = float("-inf")
+    max_valid = max_valid.max()
 
-    return result
+    return torch.where(wildcard_mask, max_valid, regular_scores)
 
 
 @dataclass