feat(asr): diarize uploaded audio
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41baca051c
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0a369bd72c
103
asr/main.py
103
asr/main.py
@ -103,6 +103,9 @@ VOICEPRINT_STORE = os.environ.get("VOICEPRINT_STORE", "/tmp/asr_voiceprints.json
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VOICEPRINT_MATCH_THRESHOLD = float(os.environ.get("VOICEPRINT_MATCH_THRESHOLD", "0.45"))
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REALTIME_PARTIAL_INTERVAL_SECONDS = float(os.environ.get("REALTIME_PARTIAL_INTERVAL_SECONDS", "2.0"))
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REALTIME_PARTIAL_MIN_SECONDS = float(os.environ.get("REALTIME_PARTIAL_MIN_SECONDS", "1.5"))
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ASR_SPK_MODEL = os.environ.get(
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"ASR_SPK_MODEL", "iic/speech_campplus_sv_zh-cn_16k-common"
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)
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def _voiceprint_key(database_name: str, collection_name: str) -> str:
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@ -224,6 +227,8 @@ def load_model():
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model="/app/models/seacomodel",
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vad_model="/app/models/vadmodel",
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punc_model="/app/models/ctpuncmodel",
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# Native diarization puts a `spk` cluster id on each VAD sentence.
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spk_model=ASR_SPK_MODEL,
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)
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logger.info("模型加载完成")
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@ -370,20 +375,58 @@ async def websocket_asr_root(websocket: WebSocket):
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await run_asr_websocket(websocket)
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def make_speakr_response(text: str, language: str = "zh", speaker: str = "SPEAKER_00"):
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text = (text or "").strip()
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return {
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"text": text,
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"language": language,
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"segments": [
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{
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"start": None,
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"end": None,
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"text": text,
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"speaker": speaker or "SPEAKER_00",
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}
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] if text else [],
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}
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def _speaker_label(value):
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"""Convert FunASR speaker ids to the labels expected by Speakr."""
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if value is None or value == "":
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return "UNKNOWN_SPEAKER"
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if isinstance(value, int) or (isinstance(value, str) and value.isdigit()):
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return f"SPEAKER_{int(value):02d}"
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return str(value)
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def _speaker_segments(result):
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"""Read native FunASR diarization output across supported versions."""
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if not isinstance(result, dict):
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return []
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sentence_info = result.get("sentence_info") or result.get("segments") or []
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if not isinstance(sentence_info, list):
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return []
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segments = []
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for sentence in sentence_info:
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if not isinstance(sentence, dict):
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continue
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text = str(sentence.get("text") or "").strip()
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if not text:
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continue
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segments.append({
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"start": sentence.get("start"),
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"end": sentence.get("end"),
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"text": text,
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"speaker": _speaker_label(sentence.get("speaker", sentence.get("spk"))),
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})
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return segments
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def make_speakr_response(
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result,
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language: str = "zh",
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diarize: bool = True,
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fallback_speaker: str = "SPEAKER_00",
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):
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"""Return diarized sentences in Speakr's upload-transcription contract."""
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result = result if isinstance(result, dict) else {}
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text = str(result.get("text") or "").strip()
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segments = _speaker_segments(result) if diarize else []
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if not segments and text:
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segments = [{
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"start": None,
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"end": None,
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"text": text,
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"speaker": fallback_speaker or "SPEAKER_00",
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}]
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return {"text": text, "language": language, "segments": segments}
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async def run_asr(
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@ -396,20 +439,22 @@ async def run_asr(
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result = model.generate(input=audio_path, hotword=hotword)
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if result and len(result) > 0:
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text = result[0].get("text", "")
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result_data = result[0]
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text = result_data.get("text", "")
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else:
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result_data = {}
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text = ""
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text = cn_to_arabic(text)
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if isinstance(result_data, dict):
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result_data = dict(result_data)
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result_data["text"] = text
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logger.info(f"识别结果: {text}")
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if response_format == "text":
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return PlainTextResponse(content=text)
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return {
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"text": text,
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"language": language,
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}
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return {**result_data, "text": text, "language": language}
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except Exception as e:
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logger.error(f"识别出错: {e}")
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@ -517,6 +562,10 @@ async def transcribe_speakr_compatible(
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language: Optional[str] = Form("zh"),
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database_name: Optional[str] = Form("NB"),
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collection_name: Optional[str] = Form("voice"),
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spk_diarization: bool = True,
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spk_num: int = 0,
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spk_min_speakers: Optional[int] = None,
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spk_max_speakers: Optional[int] = None,
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):
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"""
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Speakr 兼容接口：
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@ -560,11 +609,21 @@ async def transcribe_speakr_compatible(
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except Exception as e:
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logger.warning(f"声纹匹配失败，使用默认说话人: {e}")
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return make_speakr_response(
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text=result.get("text", "") if isinstance(result, dict) else "",
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response = make_speakr_response(
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result=result,
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language=result.get("language", language or "zh") if isinstance(result, dict) else language or "zh",
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speaker=speaker_name,
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diarize=spk_diarization,
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fallback_speaker=speaker_name,
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)
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logger.info(
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"Speakr diarization: enabled=%s requested_speakers=%s range=%s-%s detected=%s",
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spk_diarization,
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spk_num,
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spk_min_speakers,
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spk_max_speakers,
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len({segment["speaker"] for segment in response["segments"]}),
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)
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return response
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finally:
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if tmp_path and os.path.exists(tmp_path):
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@ -3,7 +3,8 @@
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# Speakr / ASR
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USE_ASR_ENDPOINT=true
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ASR_BASE_URL=http://asr-test:59805
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ASR_DIARIZE=true
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ASR_DIARIZE=true
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ASR_SPK_MODEL=iic/speech_campplus_sv_zh-cn_16k-common
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ASR_MIN_SPEAKERS=1
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ASR_MAX_SPEAKERS=10
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@ -221,10 +221,15 @@ def transcribe_audio_asr(app_context, recording_id, filepath, original_filename,
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with open(current_filepath, 'rb') as audio_file:
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url = f"{ASR_BASE_URL}/asr"
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params = {
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'batch_size_s':300,
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'spk_diarization':True,
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'spk_num':0,
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requested_speaker_count = 0
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if min_speakers and max_speakers and min_speakers == max_speakers:
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requested_speaker_count = min_speakers
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params = {
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'batch_size_s':300,
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'spk_diarization':bool(diarize),
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'spk_num':requested_speaker_count,
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'spk_min_speakers':min_speakers,
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'spk_max_speakers':max_speakers,
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'spk_threshold':0.6,
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'spk_cluster_method':'ahc',
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'spk_smooth_window':3,
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@ -334,8 +339,9 @@ def transcribe_audio_asr(app_context, recording_id, filepath, original_filename,
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segments_without_speakers = 0
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for segment in asr_response_data['segments']:
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if 'speaker' in segment and segment['speaker'] is not None:
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all_speakers.add(segment['speaker'])
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speaker = segment.get('speaker', segment.get('spk', segment.get('spk_name')))
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if speaker is not None:
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all_speakers.add(speaker)
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segments_with_speakers += 1
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else:
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segments_without_speakers += 1
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@ -355,7 +361,9 @@ def transcribe_audio_asr(app_context, recording_id, filepath, original_filename,
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last_known_speaker = None
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for i, segment in enumerate(asr_response_data['segments']):
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speaker = segment.get('speaker')
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# Native FunASR uses `spk`; the realtime bridge exposes
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# `spk_name`. Normalize both to Speakr's `speaker` field.
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speaker = segment.get('speaker', segment.get('spk', segment.get('spk_name')))
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text = segment.get('text', '').strip()
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# 如果片段没有说话人，使用前一个片段的说话人
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