diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 3820782db..9789a04f0 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -818,7 +818,16 @@ def inference_with_vad(self, input, input_len=None, **cfg):
                     spk_embedding.cpu(), oracle_num=kwargs.get("preset_spk_num", None)
                 )
                 # del result['spk_embedding']
-                sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
+                if kwargs.get("return_spk_center", False):
+                    sv_output, spk_center = postprocess(
+                        all_segments, None, labels, spk_embedding.cpu(), return_spk_center=True
+                    )
+                    # Per-speaker ERes2NetV2 centroids, indexed by the `spk` id in
+                    # sentence_info. Kept on the result for downstream voiceprint use
+                    # (the per-chunk spk_embedding below is still deleted to keep output small).
+                    result["spk_embedding_center"] = spk_center
+                else:
+                    sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
                 if self.spk_mode == "punc_segment" and "timestamp" not in result and "timestamps" not in result:
                     logging.warning("No timestamps in ASR result (e.g. SenseVoice), falling back to vad_segment mode for speaker diarization.")
                     self.spk_mode = "vad_segment"
diff --git a/funasr/models/campplus/utils.py b/funasr/models/campplus/utils.py
index e9f5eb4a1..a3314f745 100644
--- a/funasr/models/campplus/utils.py
+++ b/funasr/models/campplus/utils.py
@@ -138,7 +138,11 @@ def extract_feature(audio):
 
 
 def postprocess(
-    segments: list, vad_segments: list, labels: np.ndarray, embeddings: np.ndarray
+    segments: list,
+    vad_segments: list,
+    labels: np.ndarray,
+    embeddings: np.ndarray,
+    return_spk_center: bool = False,
 ) -> list:
     """Postprocess.
     
@@ -184,6 +188,10 @@ def is_overlapped(t1, t2):
     # smooth the result
     distribute_res = smooth(distribute_res)
 
+    if return_spk_center:
+        # spk_embs[i] is the centroid (mean of clustered chunk embeddings) for
+        # corrected speaker label i, aligned with the `spk` ids in sentence_info.
+        return distribute_res, spk_embs
     return distribute_res