feat(provider): 完成阶段E协议适配、国内预设与模型路由

backend/app/services/transcription_service.py

@@ -1,37 +1,59 @@
-"""转写适配层；第一阶段消费文本附件或桌面 Host 预生成的旁路文本。"""
+"""转写作业：API 优先，本地模型回退；保留已有 Host 文本入口。"""
 
 from __future__ import annotations
 
 from collections import OrderedDict
 from datetime import datetime, timezone
-from pathlib import Path
 from uuid import uuid4
 
 from app.contracts import TranscriptionJob
+from app.errors import ApiError
 from app.services.attachment_service import attachment_path
 
 _jobs: OrderedDict[str, TranscriptionJob] = OrderedDict()
 MAX_JOBS = 100
 
 
-def create_transcription(attachment_id: str, language: str | None = None) -> TranscriptionJob:
-    # TODO(ai-core): 第二阶段接入本地 ASR 队列后，保留相同 Job 契约替换此同步降级实现。
-    del language  # 预生成 transcript 暂不需要语言识别。
+async def create_transcription(attachment_id: str, language: str | None = None, *, diarization: bool = False) -> TranscriptionJob:
+    from app.container import container
+
     source = attachment_path(attachment_id)
-    transcript = source if source.suffix.lower() in {".txt", ".md"} else Path(f"{source}.txt")
     job = TranscriptionJob(
         job_id=f"transcription_{uuid4().hex}",
         attachment_id=attachment_id,
-        status="completed" if transcript.is_file() else "failed",
-        text=transcript.read_text(encoding="utf-8") if transcript.is_file() else None,
-        error_code=None if transcript.is_file() else "TRANSCRIPTION_BACKEND_UNAVAILABLE",
-        error_message=(
-            None
-            if transcript.is_file()
-            else "No host-generated transcript is available; local speech models are phase two."
-        ),
+        status="processing",
         created_at=datetime.now(timezone.utc),
     )
+    try:
+        if diarization:
+            # Speaker verification and diarization are different capabilities.
+            raise ApiError(501, "DIARIZATION_NOT_IMPLEMENTED", "说话人分离将在阶段 F 接入，当前不能忽略 diarization 请求。")
+        transcript = source if source.suffix.lower() in {".txt", ".md"} else attachment_path(f"{attachment_id}.txt")
+        # A saved transcript remains an explicit import path, never faked ASR.
+        if transcript.is_file() and (source == transcript or container.model_routing.configuration().transcription is None):
+            with transcript.open("rb") as handle:
+                content = handle.read(1024 * 1024 + 1)
+            if len(content) > 1024 * 1024:
+                raise ApiError(413, "TRANSCRIPT_TOO_LARGE", "Transcript exceeds 1 MiB.")
+            job.text = content.decode("utf-8")
+            if not job.text.strip():
+                raise ApiError(422, "TRANSCRIPT_EMPTY", "Transcript is empty.")
+            job.source = "sidecar"
+        else:
+            result = await container.model_routing.transcribe(source, language)
+            job.text = result.text
+            job.source = result.source
+            job.fallback_reason = result.fallback_reason
+        job.status = "completed"
+    except ApiError as exc:
+        job.status = "failed"
+        job.error_code = exc.code
+        job.error_message = exc.message
+        job.fallback_reason = exc.details.get("fallback_reason")
+    except (OSError, UnicodeError):
+        job.status = "failed"
+        job.error_code = "TRANSCRIPT_UNREADABLE"
+        job.error_message = "Transcript could not be read."
     _jobs[job.job_id] = job
     while len(_jobs) > MAX_JOBS:
         _jobs.popitem(last=False)