def __call__(
            self,
            audio_path: str = "test.mp3",
            chunk_length_s: int = 30,
            stride_length_s: int = 5,
            max_new_tokens: int = 128,
            batch_size: int = 100,
            language: str = "turkish",
            return_timestamps: bool = False):
        """
        Converts audio to text using the pre-trained speech recognition model.

        Args:
            audio_path (str): Path to the audio file to be transcribed.

        Returns:
            str: Transcribed text from the audio.
        """

        pipe = pipeline(
            "automatic-speech-recognition",
            model=self.model,
            chunk_length_s=chunk_length_s,
            stride_length_s=stride_length_s,
            max_new_tokens=max_new_tokens,
            batch_size=batch_size,
            return_timestamps=return_timestamps,
            tokenizer=self.processor.tokenizer,
            feature_extractor=self.processor.feature_extractor,
            model_kwargs={"use_flash_attention_2": self.flash_attention_2},
            generate_kwargs={"language": language},
            device_map=self.device)
        logging.info("Transcribing audio...")
        result = pipe(audio_path)
        return result

        Converts audio to text using the pre-trained speech recognition model.

        Args:
            audio_path (str): Path to the audio file to be transcribed.

        Returns:
            str: Transcribed text from the audio.
        
whisperplus/pipelines/whisper.py