V2

local_stt_bridge/server.py

@@ -0,0 +1,92 @@
+import base64
+import os
+import tempfile
+from typing import Optional
+
+from fastapi import FastAPI, Header, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+try:
+    from faster_whisper import WhisperModel
+except ImportError as exc:  # pragma: no cover
+    raise RuntimeError("faster-whisper is required. Install dependencies from requirements.txt") from exc
+
+
+STT_MODEL = os.getenv("STT_MODEL", "small")
+STT_DEVICE = os.getenv("STT_DEVICE", "auto")
+STT_COMPUTE_TYPE = os.getenv("STT_COMPUTE_TYPE", "int8")
+STT_API_KEY = os.getenv("STT_API_KEY", "").strip()
+
+app = FastAPI(title="Local STT Bridge", version="1.0.0")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=False,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model = WhisperModel(STT_MODEL, device=STT_DEVICE, compute_type=STT_COMPUTE_TYPE)
+
+
+class TranscribeRequest(BaseModel):
+    audioBase64: str
+    mimeType: Optional[str] = "audio/webm"
+    captureMode: Optional[str] = "tab"
+    model: Optional[str] = None
+
+
+@app.get("/health")
+def health():
+    return {
+        "ok": True,
+        "engine": "faster-whisper",
+        "model": STT_MODEL,
+        "device": STT_DEVICE,
+        "computeType": STT_COMPUTE_TYPE,
+    }
+
+
+@app.post("/transcribe")
+def transcribe(payload: TranscribeRequest, x_stt_api_key: Optional[str] = Header(default=None)):
+    if STT_API_KEY and x_stt_api_key != STT_API_KEY:
+        raise HTTPException(status_code=401, detail="Invalid STT API key")
+
+    try:
+        audio_bytes = base64.b64decode(payload.audioBase64)
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=f"Invalid base64 audio payload: {exc}") from exc
+
+    suffix = ".webm"
+    if payload.mimeType and "mp4" in payload.mimeType:
+        suffix = ".mp4"
+    elif payload.mimeType and "wav" in payload.mimeType:
+        suffix = ".wav"
+
+    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
+        tmp.write(audio_bytes)
+        tmp_path = tmp.name
+
+    try:
+        segments, info = model.transcribe(
+            tmp_path,
+            vad_filter=True,
+            beam_size=1,
+            language=None,
+        )
+        text = " ".join(segment.text.strip() for segment in segments).strip()
+        return {
+            "success": True,
+            "text": text,
+            "language": info.language,
+            "duration": info.duration,
+        }
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=f"Transcription failed: {exc}") from exc
+    finally:
+        try:
+            os.remove(tmp_path)
+        except OSError:
+            pass
+