import asyncio


class TranscriptionEngine:
    _model = None

    def _get_model(self, model_name: str = "large-v3", device: str = "auto"):
        if self._model is None:
            from faster_whisper import WhisperModel
            if device == "auto":
                try:
                    self._model = WhisperModel(model_name, device="cuda", compute_type="float16")
                except Exception:
                    self._model = WhisperModel(model_name, device="cpu", compute_type="int8")
            else:
                compute = "float16" if device in ("cuda", "rocm") else "int8"
                self._model = WhisperModel(model_name, device=device, compute_type=compute)
        return self._model

    async def transcribe_file(
        self,
        audio_path: str,
        language: str = "de",
        model_name: str = "large-v3",
        device: str = "auto",
    ) -> str:
        loop = asyncio.get_event_loop()
        model = self._get_model(model_name, device)
        segments, _ = await loop.run_in_executor(
            None,
            lambda: model.transcribe(audio_path, language=language),
        )
        return "".join(seg.text for seg in segments).strip()


engine = TranscriptionEngine()