import os
from typing import Optional, Tuple, List

from faster_whisper import WhisperModel

_model_singleton: Optional[WhisperModel] = None


def get_model() -> WhisperModel:
	global _model_singleton
	if _model_singleton is None:
		model_size = os.getenv("WHISPER_MODEL_SIZE", "small")
		# CPU-friendly defaults; set compute_type="int8" for lower RAM
		_model_singleton = WhisperModel(model_size, device="cpu", compute_type="int8")
	return _model_singleton


def transcribe_file(audio_path: str) -> Tuple[str, float]:
	"""
	Transcribe an audio file to text locally using faster-whisper.
	Returns (text, avg_logprob).
	"""
	model = get_model()
	segments, info = model.transcribe(audio_path, beam_size=1, vad_filter=True)

	text_parts: List[str] = []
	for seg in segments:
		text_parts.append(seg.text.strip())

	text = " ".join(part for part in text_parts if part)
	avg_logprob = getattr(info, "avg_logprob", 0.0) if info is not None else 0.0
	return text.strip(), float(avg_logprob)