def METHOD_NAME(
    librispeech_path, global_stats_path, sp_model_path, subset=None, biasinglist=None, droprate=0.0, maxsize=1000
):
    fullbiasinglist = []
    if biasinglist:
        with open(biasinglist) as fin:
            fullbiasinglist = [line.strip() for line in fin]
    train_transform = TrainTransform(
        global_stats_path=global_stats_path,
        sp_model_path=sp_model_path,
        blist=fullbiasinglist,
        droprate=droprate,
        maxsize=maxsize,
    )
    val_transform = ValTransform(
        global_stats_path=global_stats_path,
        sp_model_path=sp_model_path,
        blist=fullbiasinglist,
        droprate=droprate,
        maxsize=maxsize,
    )
    test_transform = TestTransform(
        global_stats_path=global_stats_path,
        sp_model_path=sp_model_path,
        blist=fullbiasinglist,
        droprate=droprate,
        maxsize=maxsize,
    )
    return LibriSpeechDataModule(
        librispeech_path=librispeech_path,
        train_transform=train_transform,
        val_transform=val_transform,
        test_transform=test_transform,
        subset=subset,
        fullbiasinglist=fullbiasinglist,
    )