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"""yuanqi 本地 ASR 适配器 — ``POST /v1/audio/transcriptions`` (multipart)。
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与 MiMo 走 ``/chat/completions`` + ``input_audio`` 不同，元炁本地推理引擎
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（yuanqi）对外暴露的是 OpenAI Whisper 兼容的 multipart 端点::
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POST {base_url}/v1/audio/transcriptions
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Content-Type: multipart/form-data
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Authorization: Bearer {api_key}
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file: <audio bytes>
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language: zh (可选)
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响应::
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{"text": "转写后的文本"}
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模型实际为 FunASR SenseVoiceSmall，转写文本已在 yuanqi 侧剥离了
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``<|zh|><|NEUTRAL|><|Speech|><|withitn|>`` 等内部标签，此处无需二次清洗。
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"""
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from __future__ import annotations
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import hashlib
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import time
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import httpx
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from app.ai_provider.audio.base import (
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ASRProtocol,
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ASRRequest,
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ASRResponse,
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AudioAdapterBase,
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AudioServiceError,
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)
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from app.core.logging import logger
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# multipart 使用的 MIME 映射（与 yuanqi _SUPPORTED_FORMATS 白名单对齐）
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_MIME_MAP = {
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"wav": "audio/wav",
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"mp3": "audio/mpeg",
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"m4a": "audio/mp4",
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"flac": "audio/flac",
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"ogg": "audio/ogg",
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"opus": "audio/opus",
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"aac": "audio/aac",
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"webm": "audio/webm",
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}
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# yuanqi ASR 路径前缀（与 src/endpoints/asr.py 的 router 前缀一致）
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_ASR_PATH = "/v1/audio/transcriptions"
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# ASR 缓存：默认最多 128 条，TTL 1 小时（与 MiMo ASR 适配器一致）
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_ASR_CACHE_MAX_SIZE = 128
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_ASR_CACHE_TTL_SECONDS = 3600
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class _ASRCacheEntry:
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__slots__ = ("text", "stored_at")
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def __init__(self, text: str) -> None:
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self.text = text
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self.stored_at = time.monotonic()
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_asr_cache: dict[str, _ASRCacheEntry] = {}
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def _asr_cache_key(model: str, audio_data: bytes, language: str, fmt: str) -> str:
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"""生成 ASR 缓存键：SHA256(model + audio_bytes + language + format)。"""
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raw = f"{model}\0{language}\0{fmt}\0"
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raw_bytes = raw.encode() + audio_data
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return hashlib.sha256(raw_bytes).hexdigest()
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def _asr_cache_get(key: str) -> str | None:
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entry = _asr_cache.get(key)
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if entry is None:
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return None
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if time.monotonic() - entry.stored_at > _ASR_CACHE_TTL_SECONDS:
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del _asr_cache[key]
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return None
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return entry.text
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def _asr_cache_set(key: str, text: str) -> None:
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if len(_asr_cache) >= _ASR_CACHE_MAX_SIZE:
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sorted_keys = sorted(_asr_cache.keys(), key=lambda k: _asr_cache[k].stored_at)
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for old_key in sorted_keys[: _ASR_CACHE_MAX_SIZE // 2]:
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del _asr_cache[old_key]
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_asr_cache[key] = _ASRCacheEntry(text)
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class YuanqiASRWhisper(AudioAdapterBase, ASRProtocol):
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"""yuanqi 本地 ASR 适配器，走 OpenAI Whisper 兼容的 multipart 端点。
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将音频字节以 ``multipart/form-data`` 的 ``file`` 字段上传到 yuanqi 的
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``/v1/audio/transcriptions``，从 JSON 响应中提取转写文本。
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"""
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_adapter_type = "asr"
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_default_timeout = 300
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def __init__(
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self,
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*,
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base_url: str,
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api_key: str,
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model: str = "SenseVoiceSmall",
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request_timeout_seconds: int = 300,
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) -> None:
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self._base_url = base_url.rstrip("/")
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self._api_key = api_key
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self._model = model
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self._timeout = request_timeout_seconds
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self._client: httpx.AsyncClient | None = None
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# ── 客户端 ───────────────────────────────────────────────────────────
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@property
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def _http(self) -> httpx.AsyncClient:
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if self._client is None or self._client.is_closed:
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self._client = httpx.AsyncClient(timeout=httpx.Timeout(self._timeout))
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return self._client
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async def close(self) -> None:
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if self._client is not None and not self._client.is_closed:
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await self._client.aclose()
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# ── ASRProtocol ──────────────────────────────────────────────────────
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async def transcribe(self, request: ASRRequest) -> ASRResponse:
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"""通过 ``POST /v1/audio/transcriptions`` 转写音频。"""
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# ── 缓存查找 ──────────────────────────────────────────────────
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cache_key = _asr_cache_key(
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self._model, request.audio_data, request.language or "auto", request.format
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)
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cached = _asr_cache_get(cache_key)
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if cached is not None:
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logger.info("audio_asr.cache_hit", model=self._model, text_length=len(cached))
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return ASRResponse(text=cached)
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# ── 构造 multipart 请求 ───────────────────────────────────────
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mime = _MIME_MAP.get(request.format, "audio/wav")
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filename = f"audio.{request.format}"
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files = {"file": (filename, request.audio_data, mime)}
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data: dict[str, str] = {}
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if request.language:
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data["language"] = request.language
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# 本地 yuanqi 引擎通常无需鉴权；仅当配置了 api_key 才带 Authorization 头
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headers: dict[str, str] = {}
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if self._api_key:
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headers["Authorization"] = f"Bearer {self._api_key}"
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url = f"{self._base_url}{_ASR_PATH}"
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logger.info(
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"audio_asr.request",
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model=self._model,
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format=request.format,
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language=request.language,
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url=url,
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size_bytes=len(request.audio_data),
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)
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# ── API 调用 ────────────────────────────────────────────────
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try:
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response = await self._http.post(url, files=files, data=data, headers=headers)
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except httpx.TimeoutException as exc:
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raise AudioServiceError(
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status_code=504,
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code="audio_asr_timeout",
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message="ASR request timed out calling yuanqi transcriptions.",
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) from exc
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except httpx.RequestError as exc:
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raise AudioServiceError(
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status_code=503,
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code="audio_asr_connection_error",
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message=f"ASR connection error: {exc}",
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) from exc
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if response.status_code != 200:
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body = response.text[:500]
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raise AudioServiceError(
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status_code=response.status_code,
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code="audio_asr_api_error",
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message=f"ASR API returned {response.status_code}: {body}",
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)
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# 解析响应，提取转写文本
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text = self._extract_text(response.json())
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if not text:
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raise AudioServiceError(
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status_code=500,
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code="audio_asr_empty_response",
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message="ASR API returned empty transcription.",
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)
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# ── 写入缓存 ────────────────────────────────────────────────
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_asr_cache_set(cache_key, text)
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logger.info(
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"audio_asr.success",
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model=self._model,
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text_length=len(text),
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cache_size=len(_asr_cache),
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)
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return ASRResponse(text=text)
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# ── 辅助方法 ──────────────────────────────────────────────────────────
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@staticmethod
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def _extract_text(body: dict) -> str:
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"""从 Whisper 兼容响应中提取转写文本。"""
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try:
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text = body.get("text")
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if text is None:
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raise AudioServiceError(
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status_code=500,
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code="audio_asr_no_text",
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message="ASR response has no 'text' field.",
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)
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return text or ""
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except AudioServiceError:
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raise
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except Exception as exc:
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raise AudioServiceError(
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status_code=500,
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code="audio_asr_response_parse_error",
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message=f"Failed to parse ASR response: {exc}",
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) from exc
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