diff --git a/docs/vllm_guide.md b/docs/vllm_guide.md
index 334870d83..ff850a088 100644
--- a/docs/vllm_guide.md
+++ b/docs/vllm_guide.md
@@ -226,8 +226,6 @@ const result = await resp.json();
 
 兼容 OpenAI Whisper API 标准，可直接用 OpenAI SDK 接入。
 
-> 注意：OpenAI 标准不支持 SPK 参数。如需说话人分离，请用 HTTP REST 接口。
-
 **请求**：`multipart/form-data`
 
 | 参数 | 类型 | 默认值 | 说明 |
@@ -237,6 +235,7 @@ const result = await resp.json();
 | `language` | string | None | 语种 |
 | `response_format` | string | "json" | "json" / "text" / "verbose_json" |
 | `timestamp_granularities` | string | "word" | "word" / "segment" |
+| `spk` | bool | false | 说话人分离（FunASR 扩展字段） |
 
 **响应**（`verbose_json`）：
 
@@ -278,9 +277,8 @@ curl -X POST http://localhost:8899/v1/audio/transcriptions \
 
 ### 3.5 协议三：WebSocket — `ws://host:port/ws`
 
-> 注意：WebSocket 接口当前不支持 SPK。如需说话人分离，请用 HTTP REST 接口。
 
-离线服务的 WebSocket 接口，发送完整音频后获取结果（非流式）。
+离线服务的 WebSocket 接口，发送完整音频后获取结果。STOP 时自动进行说话人聚类，结果中包含 `spk` 字段。
 
 **客户端 → 服务端**：
 
@@ -451,28 +449,18 @@ asyncio.run(stream("audio.wav"))
 
 fsmn-vad 默认启用动态静音阈值。离线和流式使用不同配置。
 
-### 离线配置（保留长段 ≤60s）
+| 累积时长 | 离线（保留长段 ≤60s） | 流式（平衡延迟） |
+|---------|-------------------|----------------|
+| ≤ 5s | 2000ms | 2000ms |
+| 5-10s | 2000ms | 1500ms |
+| 10-15s | 1000ms | 1000ms |
+| 15-20s | 1000ms | 800ms |
+| 20-30s | 800ms | 800ms |
+| 30-45s | 600ms | 400ms |
+| 45-60s | 200-400ms | 100ms |
+| > 60s | 100ms | 100ms |
 
-| 累积时长 | 静音阈值 |
-|---------|---------|
-| ≤ 10s | 2000ms |
-| 10-20s | 1000ms |
-| 20-30s | 800ms |
-| 30-40s | 600ms |
-| 40-50s | 400ms |
-| 50-60s | 200ms |
-| > 60s | 100ms |
-
-### 流式配置（平衡延迟）
-
-| 累积时长 | 静音阈值 |
-|---------|---------|
-| ≤ 5s | 2000ms |
-| 5-10s | 1500ms |
-| 10-15s | 1000ms |
-| 15-30s | 800ms |
-| 30-45s | 400ms |
-| > 45s | 100ms |
+离线倾向保留长段减少边界损失；流式更快收紧以降低延迟。
 
 ### 自定义
 
diff --git a/examples/industrial_data_pretraining/fun_asr_nano/serve_vllm.py b/examples/industrial_data_pretraining/fun_asr_nano/serve_vllm.py
index ef54c69eb..0d9e9a033 100644
--- a/examples/industrial_data_pretraining/fun_asr_nano/serve_vllm.py
+++ b/examples/industrial_data_pretraining/fun_asr_nano/serve_vllm.py
@@ -210,13 +210,14 @@ async def openai_transcriptions(
     language: str = Form(default=None),
     response_format: str = Form(default="json"),
     timestamp_granularities: str = Form(default="word"),
+    spk: bool = Form(default=False),
 ):
-    """OpenAI Whisper-compatible transcription API."""
+    """OpenAI Whisper-compatible transcription API (extended with spk support)."""
     content = await file.read()
     audio_data, sr = sf.read(io.BytesIO(content))
 
     use_ts = "word" in timestamp_granularities or "segment" in timestamp_granularities
-    result = process_audio(audio_data, sr=sr, language=language, use_timestamp=use_ts)
+    result = process_audio(audio_data, sr=sr, language=language, use_spk=spk, use_timestamp=use_ts)
 
     if response_format == "text":
         return JSONResponse(content=result["text"])
@@ -304,6 +305,34 @@ async def websocket_endpoint(websocket: WebSocket):
                                         "text": res[0]["text"], "start": start_ms, "end": end_ms
                                     })
 
+                        # SPK: run full clustering on all sentences
+                        if locked_sentences and _spk_model is not None:
+                            try:
+                                from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
+                                from funasr.models.campplus.cluster_backend import ClusterBackend
+                                vad_segs = [[s["start"]/1000, s["end"]/1000, 
+                                            audio_buffer[int(s["start"]*16):int(s["end"]*16)]]
+                                           for s in locked_sentences]
+                                chunks = sv_chunk(vad_segs)
+                                if chunks:
+                                    speech_list = [ch[2] for ch in chunks]
+                                    spk_res = _spk_model.generate(input=speech_list, cache={}, is_final=True)
+                                    import torch as _torch
+                                    embs = _torch.cat([r["spk_embedding"] for r in spk_res], dim=0)
+                                    cluster = ClusterBackend(merge_thr=0.78).to(_args.device)
+                                    labels = cluster(embs.cpu(), oracle_num=None)
+                                    if not isinstance(labels, np.ndarray):
+                                        labels = np.array(labels)
+                                    all_sorted = sorted(chunks, key=lambda x: x[0])
+                                    sv_output = postprocess(all_sorted, None, labels, embs.cpu())
+                                    spk_sents = [{"text": s["text"], "start": int(s["start"]), "end": int(s["end"])}
+                                                for s in locked_sentences]
+                                    distribute_spk(spk_sents, sv_output)
+                                    for i, ss in enumerate(spk_sents):
+                                        locked_sentences[i]["spk"] = ss.get("spk", 0)
+                            except Exception as e:
+                                logger.warning(f"SPK failed: {e}")
+
                         await websocket.send_json({
                             "sentences": locked_sentences,
                             "is_final": True,