fix: apply temperature scaling to logits before softmax in sampler

kimia_infer/utils/sampler.py

@@ -62,13 +62,14 @@ def sample_audio_logits(
             logits.scatter_(dim=0, index=recent_window, src=scores)
             logits = logits.unsqueeze(0)  # Add batch dimension back
 
+        # Apply temperature scaling if not greedy
+        if self.audio_temperature > 1e-6:
+            logits = logits / self.audio_temperature
+
         # Convert to probabilities with softmax
         logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)
 
-        # Apply temperature scaling if not greedy
         if self.audio_temperature > 1e-6:
-            logprobs = logprobs / self.audio_temperature
-
             # Apply top-k sampling
             if self.audio_top_k > 0:
                 # Get probabilities from logprobs
@@ -134,13 +135,14 @@ def sample_text_logits(
             logits.scatter_(dim=0, index=recent_window, src=scores)
             logits = logits.unsqueeze(0)  # Add batch dimension back
 
+        # Apply temperature scaling if not greedy
+        if self.text_temperature > 1e-6:
+            logits = logits / self.text_temperature
+
         # Convert to probabilities with softmax
         logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)
 
-        # Apply temperature scaling if not greedy
         if self.text_temperature > 1e-6:
-            logprobs = logprobs / self.text_temperature
-
             # Apply top-k sampling
             if self.text_top_k > 0:
                 # Get probabilities from logprobs