diff --git a/src/stylish_tts/train/losses.py b/src/stylish_tts/train/losses.py
index a9ce029..b3397d9 100644
--- a/src/stylish_tts/train/losses.py
+++ b/src/stylish_tts/train/losses.py
@@ -583,6 +583,8 @@ def forced_align(
         input_lengths: Tensor,
         target_lengths: Tensor,
     ):
+        import k2
+
         supervision_segments, token_ids, indices = self.encode_supervisions(
             targets, target_lengths, input_lengths
         )
@@ -602,7 +604,11 @@ def forced_align(
 
         best_paths = k2.shortest_path(lattices, use_double_scores=True)
         frame_scores = best_paths.scores[(best_paths.labels != -1)]
-        frame_scores = frame_scores.split(input_lengths.tolist())
+        # encode_supervisions sorted the batch by length, so best_paths come back
+        # in that sorted order. Split the scores by the sorted per-segment lengths
+        # rather than the original input lengths.
+        sorted_lengths = supervision_segments[:, 2].tolist()
+        frame_scores = frame_scores.split(sorted_lengths)
         scores = torch.stack([p.mean() for p in frame_scores])
 
         batch_arc_shape = best_paths.arcs.shape().remove_axis(1)
@@ -612,6 +618,13 @@ def forced_align(
         # k2 makes an extra frame for some reasons
         for i in range(len(batch_frame_labels)):
             batch_frame_labels[i][-1] -= 1
+        # Restore the original batch order so each segment's labels and score line
+        # up with the caller's segment. Without this every segment receives another
+        # segment's alignment.
+        inverse = torch.empty_like(indices)
+        inverse[indices] = torch.arange(indices.numel(), device=indices.device)
+        batch_frame_labels = [batch_frame_labels[j] for j in inverse.tolist()]
+        scores = scores[inverse]
         return batch_frame_labels, scores
 
     def on_train_epoch_end(self, train):