ASR-микросервис: whisper-large-v3-russian + Qwen3-ASR-1.7B

app/main.py

@@ -0,0 +1,72 @@
+"""ASR-микросервис: whisper-large-v3-russian + Qwen3-ASR-1.7B для calls24."""
+import logging
+
+from fastapi import FastAPI, UploadFile
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+from starlette.middleware.cors import CORSMiddleware
+
+from .config import settings
+from .engine import load_models
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(title="ASR Microservice", version="1.0.0")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+_models = {}
+
+
+@app.on_event("startup")
+async def startup_event():
+    import torch
+
+    device = torch.device(settings.DEVICE)
+    global _models
+    _models = load_models(device)
+    logger.info(f"Модели загружены: {list(_models.keys())}")
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok", "models": list(_models.keys())}
+
+
+@app.post("/transcribe")
+async def transcribe(file: UploadFile, model: str = "qwen3"):
+    """Распознать загруженный аудиофайл (model в query-параметре)."""
+    import os
+    import tempfile
+
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
+    content = await file.read()
+    tmp.write(content)
+    tmp.close()
+
+    if model not in _models:
+        return JSONResponse(
+            status_code=400,
+            content={"error": f"Неизвестная модель: {model}. Доступны: {list(_models.keys())}"},
+        )
+
+    try:
+        result = _models[model].transcribe(tmp.name)
+        return {"model": model, "text": result}
+    except Exception as e:
+        logger.error(f"Ошибка распознавания: {e}")
+        return JSONResponse(status_code=500, content={"error": str(e)})
+    finally:
+        os.unlink(tmp.name)
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)