diff --git a/codes/models/gpt_voice/unified_voice2.py b/codes/models/gpt_voice/unified_voice2.py
index 3b8d57b2..d3ee7648 100644
--- a/codes/models/gpt_voice/unified_voice2.py
+++ b/codes/models/gpt_voice/unified_voice2.py
@@ -489,10 +489,38 @@ class UnifiedVoice(nn.Module):
         else:
             return gen.sequences[:, fake_inputs.shape[1]:]
 
+
+    # Turns the (utterly insane) output of HF.generate() into a far more sane output:
+    # [tensors(B,H,S,S)]. Outer=layers, B=batch,H=head,S=sequence
+    def make_hf_generate_attentions_sane(self, attentions):
+        layers = [[] for _ in range(len(attentions[0]))]
+        full_attention_size = attentions[-1][0].shape[-1]
+        for i, gen in enumerate(attentions):
+            for j, lyr in enumerate(gen):
+                layers[j].append(F.pad(lyr, (0, full_attention_size - lyr.shape[-1])))
+        catted = []
+        for lyr in layers:
+            catted.append(torch.cat(lyr, dim=2))
+        return catted
+
     def convert_attentions_to_aligned_codes(self, text, attentions, codes, num_conds):
-        text_padding = num_conds+1
+        """
+        This was an attempt to make some sense out of the attention matrix retrieved from the unified_voice model. Unfortunately, I can't use it for aligning text & voice.
+        """
+        text_padding = num_conds+2
         num_text = text.shape[-1]
-        results = torch.empty_like(codes)
+        num_context = num_text + text_padding
+        assert num_context + 1 == attentions[0][0].shape[-1]
+        attentions = self.make_hf_generate_attentions_sane(attentions)
+        results = [torch.empty_like(codes) for _ in range(len(attentions))]
+        for l, layer in enumerate(attentions):
+            dec_context = layer[:, :, num_context:, :]
+            # Mask out everything that isn't text (including the start token, which gets a LOT of attention)
+            dec_context[:,:,:,:text_padding+1] = 0
+            dec_context[:,:,:,num_context:] = 0
+            for h in range(dec_context.shape[1]):
+                dec_context_indices = torch.argmax(dec_context[0,h], dim=-1)
+                print(f'layer_{l};head_{h}: ' + str(dec_context_indices))
         for t, att_tok in enumerate(attentions):
             combined_attention_weights = torch.zeros((codes.shape[0], num_text), device=codes.device)
             for lyr in att_tok: