[feat] 说话人分离:可选返回每个说话人的声纹质心向量 (spk_embedding_center)

funasr/auto/auto_model.py

@@ -818,7 +818,18 @@ 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())
+                # postprocess expects np.ndarray embeddings (per its type hint).
+                spk_embedding_np = spk_embedding.detach().cpu().numpy()
+                if kwargs.get("return_spk_center", False):
+                    sv_output, spk_center = postprocess(
+                        all_segments, None, labels, spk_embedding_np, 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_np)
                 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"

funasr/models/campplus/utils.py

@@ -138,8 +138,12 @@ def extract_feature(audio):
 
 
 def postprocess(
-    segments: list, vad_segments: list, labels: np.ndarray, embeddings: np.ndarray
-) -> list:
+    segments: list,
+    vad_segments: list,
+    labels: np.ndarray,
+    embeddings: np.ndarray,
+    return_spk_center: bool = False,
+) -> Union[list, tuple]:
     """Postprocess.
 
         Args:
@@ -156,13 +160,6 @@ def postprocess(
     # merge the same speakers chronologically
     distribute_res = merge_seque(distribute_res)
 
-    # accquire speaker center
-    spk_embs = []
-    for i in range(labels.max() + 1):
-        spk_emb = embeddings[labels == i].mean(0)
-        spk_embs.append(spk_emb)
-    spk_embs = np.stack(spk_embs)
-
     def is_overlapped(t1, t2):
         """Is overlapped.
 
@@ -184,6 +181,14 @@ 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.
+        # Computed lazily: only when the caller requests speaker centers.
+        spk_embs = np.stack(
+            [embeddings[labels == i].mean(0) for i in range(labels.max() + 1)]
+        )
+        return distribute_res, spk_embs
     return distribute_res