from pywhispercpp.model import Model
import soundfile
import config
import numpy as np
from logging import getLogger

logger = getLogger(__name__)


class WhisperCPP:

    def __init__(self, warmup=True) -> None:
        models_dir = config.MODEL_DIR.as_posix()
        self.model = Model(
            model=config.WHISPER_MODEL, 
            models_dir=models_dir,
            print_realtime=False,
            print_progress=False,
            print_timestamps=False,
            translate=False
        )
        if warmup:
            self.warmup()

    
    def warmup(cls, warmup_steps=1):
        mel, _, = soundfile.read(f"{config.ASSERT_DIR}/jfk.flac")
        for _ in range(warmup_steps):
            cls.model.transcribe(mel, print_progress=False)

    @staticmethod
    def config_language(language):
        if language == "zh":
            return config.MAX_LENTH_ZH, config.WHISPER_PROMPT_ZH
        elif language == "en":
            return config.MAX_LENGTH_EN, config.WHISPER_PROMPT_EN
        raise ValueError(f"Unsupported language : {language}")

    def transcribe(self, audio_buffer:bytes, language):
        max_len, prompt = self.config_language(language)
        audio_buffer = np.frombuffer(audio_buffer, dtype=np.float32)
        try:
            output = self.model.transcribe(
                audio_buffer,
                initial_prompt=prompt,
                language=language,  
                token_timestamps=True, 
                max_len=max_len
            )
            return output
        except Exception as e:
            logger.error(e)
            return []