import numpy as np
import librosa
import io

TASK = "transcribe"
BATCH_SIZE = 8

class A2T:
    def __init__(self, mic):
        self.mic = mic

    def __transcribe(self, inputs, task: str = None):
        if inputs is  None:
            print("Inputs None")

        transcribed_text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
        return transcribed_text

    def __preprocces(self, raw):
        print(f"Raw type : {type(raw)}")
        chunk = io.BytesIO(raw)
        audio, sample_rate = librosa.load(chunk, sr=16000)
        print(f"Sample rate : {sample_rate}")
        return audio
 
    def predict(self):
        try:
            if self.mic is not None:
                raw = self.mic
                audio = self.__preprocces(raw=raw)
                print(f"audio type : {type(audio)} \n shape : {audio.shape}")
            else:
                raise Exception("please provide audio")

            if isinstance(audio , np.ndarray):
                return self.__transcribe(inputs=inputs, task=TASK)
            else:
                raise Exception("Audio is not np array")
                
        except Exception as e:
            return f"Oops some kinda error : {e}"