def METHOD_NAME(
    self, model_name, target_level, audio_and_texts, conformer_ctc_bpe_model, conformer_rnnt_bpe_model
):
    model = conformer_ctc_bpe_model if model_name == "ctc" else conformer_rnnt_bpe_model
    assert isinstance(model, ASRModel)
    filepaths, reference_texts = audio_and_texts
    confidence_cfg = (
        ConfidenceConfig(preserve_token_confidence=True)
        if target_level == "token"
        else ConfidenceConfig(preserve_word_confidence=True)
    )
    model.change_decoding_strategy(
        RNNTDecodingConfig(fused_batch_size=-1, strategy="greedy_batch", confidence_cfg=confidence_cfg)
        if model_name == "rnnt"
        else CTCDecodingConfig(confidence_cfg=confidence_cfg)
    )
    with tempfile.TemporaryDirectory() as tmpdir:
        assert np.allclose(
            np.array(
                run_confidence_benchmark(model, target_level, filepaths, reference_texts, plot_dir=tmpdir)[
                    target_level
                ]
            ),
            np.array([0.5, 1.0, 0.0, -math.inf, ECE_VALUES[(target_level, model_name)], 0.0, 0.0, 0.0]),
            atol=TOL,
        )