feat(asr): diarize uploaded audio

asr/main.py

@@ -103,6 +103,9 @@ VOICEPRINT_STORE = os.environ.get("VOICEPRINT_STORE", "/tmp/asr_voiceprints.json
 VOICEPRINT_MATCH_THRESHOLD = float(os.environ.get("VOICEPRINT_MATCH_THRESHOLD", "0.45"))
 REALTIME_PARTIAL_INTERVAL_SECONDS = float(os.environ.get("REALTIME_PARTIAL_INTERVAL_SECONDS", "2.0"))
 REALTIME_PARTIAL_MIN_SECONDS = float(os.environ.get("REALTIME_PARTIAL_MIN_SECONDS", "1.5"))
+ASR_SPK_MODEL = os.environ.get(
+    "ASR_SPK_MODEL", "iic/speech_campplus_sv_zh-cn_16k-common"
+)
 
 
 def _voiceprint_key(database_name: str, collection_name: str) -> str:
@@ -224,6 +227,8 @@ def load_model():
             model="/app/models/seacomodel",
             vad_model="/app/models/vadmodel",
             punc_model="/app/models/ctpuncmodel",
+            # Native diarization puts a `spk` cluster id on each VAD sentence.
+            spk_model=ASR_SPK_MODEL,
         )
         logger.info("模型加载完成")
 
@@ -370,20 +375,58 @@ async def websocket_asr_root(websocket: WebSocket):
     await run_asr_websocket(websocket)
 
 
-def make_speakr_response(text: str, language: str = "zh", speaker: str = "SPEAKER_00"):
+def _speaker_label(value):
-    text = (text or "").strip()
+    """Convert FunASR speaker ids to the labels expected by Speakr."""
-    return {
+    if value is None or value == "":
-        "text": text,
+        return "UNKNOWN_SPEAKER"
-        "language": language,
+    if isinstance(value, int) or (isinstance(value, str) and value.isdigit()):
-        "segments": [
+        return f"SPEAKER_{int(value):02d}"
-            {
+    return str(value)
-                "start": None,
+
-                "end": None,
+
-                "text": text,
+def _speaker_segments(result):
-                "speaker": speaker or "SPEAKER_00",
+    """Read native FunASR diarization output across supported versions."""
-            }
+    if not isinstance(result, dict):
-        ] if text else [],
+        return []
-    }
+
+    sentence_info = result.get("sentence_info") or result.get("segments") or []
+    if not isinstance(sentence_info, list):
+        return []
+
+    segments = []
+    for sentence in sentence_info:
+        if not isinstance(sentence, dict):
+            continue
+        text = str(sentence.get("text") or "").strip()
+        if not text:
+            continue
+        segments.append({
+            "start": sentence.get("start"),
+            "end": sentence.get("end"),
+            "text": text,
+            "speaker": _speaker_label(sentence.get("speaker", sentence.get("spk"))),
+        })
+    return segments
+
+
+def make_speakr_response(
+    result,
+    language: str = "zh",
+    diarize: bool = True,
+    fallback_speaker: str = "SPEAKER_00",
+):
+    """Return diarized sentences in Speakr's upload-transcription contract."""
+    result = result if isinstance(result, dict) else {}
+    text = str(result.get("text") or "").strip()
+    segments = _speaker_segments(result) if diarize else []
+    if not segments and text:
+        segments = [{
+            "start": None,
+            "end": None,
+            "text": text,
+            "speaker": fallback_speaker or "SPEAKER_00",
+        }]
+    return {"text": text, "language": language, "segments": segments}
 
 
 async def run_asr(
@@ -396,20 +439,22 @@ async def run_asr(
         result = model.generate(input=audio_path, hotword=hotword)
 
         if result and len(result) > 0:
-            text = result[0].get("text", "")
+            result_data = result[0]
+            text = result_data.get("text", "")
         else:
+            result_data = {}
             text = ""
 
         text = cn_to_arabic(text)
+        if isinstance(result_data, dict):
+            result_data = dict(result_data)
+            result_data["text"] = text
         logger.info(f"识别结果: {text}")
 
         if response_format == "text":
             return PlainTextResponse(content=text)
 
-        return {
+        return {**result_data, "text": text, "language": language}
-            "text": text,
-            "language": language,
-        }
 
     except Exception as e:
         logger.error(f"识别出错: {e}")
@@ -517,6 +562,10 @@ async def transcribe_speakr_compatible(
     language: Optional[str] = Form("zh"),
     database_name: Optional[str] = Form("NB"),
     collection_name: Optional[str] = Form("voice"),
+    spk_diarization: bool = True,
+    spk_num: int = 0,
+    spk_min_speakers: Optional[int] = None,
+    spk_max_speakers: Optional[int] = None,
 ):
     """
     Speakr 兼容接口：
@@ -560,11 +609,21 @@ async def transcribe_speakr_compatible(
         except Exception as e:
             logger.warning(f"声纹匹配失败，使用默认说话人: {e}")
 
-        return make_speakr_response(
+        response = make_speakr_response(
-            text=result.get("text", "") if isinstance(result, dict) else "",
+            result=result,
             language=result.get("language", language or "zh") if isinstance(result, dict) else language or "zh",
-            speaker=speaker_name,
+            diarize=spk_diarization,
+            fallback_speaker=speaker_name,
         )
+        logger.info(
+            "Speakr diarization: enabled=%s requested_speakers=%s range=%s-%s detected=%s",
+            spk_diarization,
+            spk_num,
+            spk_min_speakers,
+            spk_max_speakers,
+            len({segment["speaker"] for segment in response["segments"]}),
+        )
+        return response
 
     finally:
         if tmp_path and os.path.exists(tmp_path):

config.env

@@ -3,7 +3,8 @@
 # Speakr / ASR
 USE_ASR_ENDPOINT=true
 ASR_BASE_URL=http://asr-test:59805
-ASR_DIARIZE=true
+ASR_DIARIZE=true
+ASR_SPK_MODEL=iic/speech_campplus_sv_zh-cn_16k-common
 ASR_MIN_SPEAKERS=1
 ASR_MAX_SPEAKERS=10
 

speakr/src/services/transcription_service.py

@@ -221,10 +221,15 @@ def transcribe_audio_asr(app_context, recording_id, filepath, original_filename,
                     
                     with open(current_filepath, 'rb') as audio_file:
                         url = f"{ASR_BASE_URL}/asr"
-                        params = {
+                        requested_speaker_count = 0
-                            'batch_size_s':300,
+                        if min_speakers and max_speakers and min_speakers == max_speakers:
-                            'spk_diarization':True,
+                            requested_speaker_count = min_speakers
-                            'spk_num':0,
+                        params = {
+                            'batch_size_s':300,
+                            'spk_diarization':bool(diarize),
+                            'spk_num':requested_speaker_count,
+                            'spk_min_speakers':min_speakers,
+                            'spk_max_speakers':max_speakers,
                             'spk_threshold':0.6,
                             'spk_cluster_method':'ahc',
                             'spk_smooth_window':3,
@@ -334,8 +339,9 @@ def transcribe_audio_asr(app_context, recording_id, filepath, original_filename,
                 segments_without_speakers = 0
                 
                 for segment in asr_response_data['segments']:
-                    if 'speaker' in segment and segment['speaker'] is not None:
+                    speaker = segment.get('speaker', segment.get('spk', segment.get('spk_name')))
-                        all_speakers.add(segment['speaker'])
+                    if speaker is not None:
+                        all_speakers.add(speaker)
                         segments_with_speakers += 1
                     else:
                         segments_without_speakers += 1
@@ -355,7 +361,9 @@ def transcribe_audio_asr(app_context, recording_id, filepath, original_filename,
                 last_known_speaker = None
                 
                 for i, segment in enumerate(asr_response_data['segments']):
-                    speaker = segment.get('speaker')
+                    # Native FunASR uses `spk`; the realtime bridge exposes
+                    # `spk_name`. Normalize both to Speakr's `speaker` field.
+                    speaker = segment.get('speaker', segment.get('spk', segment.get('spk_name')))
                     text = segment.get('text', '').strip()
                     
                     # 如果片段没有说话人，使用前一个片段的说话人