class NemoTranscriptionUDF:
        def __init__(
            self,
            model_name: str = model_name,
            batch_size: int = batch_size,
            device: str = "cuda",
        ):
            from nemo.collections.asr.models import ASRModel

            self.asr_ast_model = ASRModel.from_pretrained(
                model_name=model_name
            )

            self.device = device
            self.batch_size = batch_size

        def __call__(self, audios):
            with torch.inference_mode():
                outputs = self.asr_ast_model.transcribe(audios.to_pylist(), batch_size=self.batch_size)
            return [output[0].text for output in outputs]