diff --git a/indextts/infer_v2.py b/indextts/infer_v2.py
index 880fe33..71c36e1 100644
--- a/indextts/infer_v2.py
+++ b/indextts/infer_v2.py
@@ -301,34 +301,42 @@ class IndexTTS2:
         print(">> starting inference...")
         self._set_gr_progress(0, "starting inference...")
         if verbose:
-            print(f"origin text:{text}, spk_audio_prompt:{spk_audio_prompt},"
-                  f" emo_audio_prompt:{emo_audio_prompt}, emo_alpha:{emo_alpha}, "
+            print(f"origin text:{text}, spk_audio_prompt:{spk_audio_prompt}, "
+                  f"emo_audio_prompt:{emo_audio_prompt}, emo_alpha:{emo_alpha}, "
                   f"emo_vector:{emo_vector}, use_emo_text:{use_emo_text}, "
                   f"emo_text:{emo_text}")
         start_time = time.perf_counter()
 
-        if use_emo_text:
+        if use_emo_text or emo_vector is not None:
+            # we're using a text or emotion vector guidance; so we must remove
+            # "emotion reference voice", to ensure we use correct emotion mixing!
             emo_audio_prompt = None
-            emo_alpha = 1.0
-            # assert emo_audio_prompt is None
-            # assert emo_alpha == 1.0
+
+        if use_emo_text:
+            # automatically generate emotion vectors from text prompt
             if emo_text is None:
-                emo_text = text
+                emo_text = text  # use main text prompt
             emo_dict = self.qwen_emo.inference(emo_text)
-            print(emo_dict)
+            print(f"detected emotion vectors from text: {emo_dict}")
             # convert ordered dict to list of vectors; the order is VERY important!
             emo_vector = list(emo_dict.values())
 
         if emo_vector is not None:
-            emo_audio_prompt = None
-            emo_alpha = 1.0
-            # assert emo_audio_prompt is None
-            # assert emo_alpha == 1.0
+            # we have emotion vectors; they can't be blended via alpha mixing
+            # in the main inference process later, so we must pre-calculate
+            # their new strengths here based on the alpha instead!
+            emo_vector_scale = max(0.0, min(1.0, emo_alpha))
+            if emo_vector_scale != 1.0:
+                # scale each vector and truncate to 4 decimals (for nicer printing)
+                emo_vector = [int(x * emo_vector_scale * 10000) / 10000 for x in emo_vector]
+                print(f"scaled emotion vectors to {emo_vector_scale}x: {emo_vector}")
 
         if emo_audio_prompt is None:
+            # we are not using any external "emotion reference voice"; use
+            # speaker's voice as the main emotion reference audio.
             emo_audio_prompt = spk_audio_prompt
+            # must always use alpha=1.0 when we don't have an external reference voice
             emo_alpha = 1.0
-            # assert emo_alpha == 1.0
 
         # 如果参考音频改变了，才需要重新生成, 提升速度
         if self.cache_spk_cond is None or self.cache_spk_audio_prompt != spk_audio_prompt: