def compose_eval_data(
    audio_dirpath: str,
    audio_format: str,
    references_filepath: str,
    reference_format: str,
    reference_tsv_column: str = None,
    save_manifest_filepath=None,
):
    """
    Speech matrix decoding pipeline produces audio with the following mask "N_pred.wav" where N is the order of the corresponding input sample
    """

    reference_sentences = extract_text_for_eval(
        references_filepath, reference_format, reference_tsv_column
    )
    predicted_audio_fp_list = extract_audio_for_eval(audio_dirpath, audio_format)
    assert len(predicted_audio_fp_list) == len(reference_sentences)

    audio_text_pairs = [
        (audio, reference)
        for audio, reference in zip(predicted_audio_fp_list, reference_sentences)
    ]

    tsv_manifest = pd.DataFrame(audio_text_pairs, columns=["prediction", "reference"])

    if save_manifest_filepath is not None:
        tsv_manifest.to_csv(save_manifest_filepath, sep="\t", quoting=3)

    return tsv_manifest
Speech matrix decoding pipeline produces audio with the following mask "N_pred.wav" where N is the order of the corresponding input sample
examples/speech_to_speech/asr_bleu/compute_asr_bleu.py