"""
Local STT service using faster-whisper.
Replaces Deepgram with fully self-hosted speech-to-text.
"""
import io
import logging
import wave
import numpy as np
from faster_whisper import WhisperModel

logger = logging.getLogger(__name__)


class LocalWhisperSTT:
    """Wraps faster-whisper for use in the Pipecat pipeline."""

    def __init__(self, model_size: str = "medium", device: str = "cpu", compute_type: str = "int8"):
        logger.info(f"Loading faster-whisper model: {model_size} on {device}")
        self.model = WhisperModel(model_size, device=device, compute_type=compute_type)
        logger.info("faster-whisper model loaded.")

    def transcribe(self, audio_bytes: bytes, sample_rate: int = 16000) -> str:
        """Transcribe raw PCM audio bytes to text."""
        audio_array = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32768.0

        segments, info = self.model.transcribe(
            audio_array,
            beam_size=5,
            language="en",
            vad_filter=True,
            vad_parameters=dict(min_silence_duration_ms=500),
        )

        text = " ".join(seg.text.strip() for seg in segments)
        return text