def split_command()

in datasets.py [0:0]
65 lines of code
7 McCabe index (conditional complexity)

def split_command(dataset: str, path: str) -> None:
    """
    Split into train / validation / test.
    """
    if dataset == "ljspeech":
        dset = torchaudio.datasets.LJSPEECH(root=path, download=False)
        num_samples = len(dset)
        split = {
            "dataset": "ljspeech",
            "test": list(range(0, NUM_LJSPEECH_TEST_SAMPLES)),
            "validation": list(
                range(
                    NUM_LJSPEECH_TEST_SAMPLES,
                    NUM_LJSPEECH_TEST_SAMPLES + NUM_LJSPEECH_VALIDATION_SAMPLES,
                )
            ),
            "train": list(
                range(
                    NUM_LJSPEECH_TEST_SAMPLES + NUM_LJSPEECH_VALIDATION_SAMPLES,
                    num_samples,
                )
            ),
        }
    elif dataset == "libritts":
        dset_train = torch.utils.data.ConcatDataset(
            [
                torchaudio.datasets.LIBRITTS(root=path, url=split, download=False)
                for split in LIBRITTS_TRAIN_SPLITS
            ]
        )
        dset_validation = torch.utils.data.ConcatDataset(
            [
                torchaudio.datasets.LIBRITTS(root=path, url=split, download=False)
                for split in LIBRITTS_VALIDATION_SPLITS
            ]
        )
        dset_test = torch.utils.data.ConcatDataset(
            [
                torchaudio.datasets.LIBRITTS(root=path, url=split, download=False)
                for split in LIBRITTS_TEST_SPLITS
            ]
        )
        num_train_samples = len(dset_train)
        num_validation_samples = len(dset_validation)
        num_test_samples = len(dset_test)
        split = {
            "dataset": "libritts",
            "train": list(range(num_train_samples)),
            "validation": list(range(num_validation_samples)),
            "test": list(range(num_test_samples)),
        }
    elif dataset == "vctk":
        dset = torchaudio.datasets.VCTK_092(root=path, download=False)
        num_samples = len(dset)
        num_train_samples = int(num_samples * VCTK_TRAIN_SPLIT_PRC)
        num_validation_samples = int(num_samples * VCTK_VALIDATION_SPLIT_PRC)

        indices = list(range(num_samples))
        random.Random(42).shuffle(indices)
        split = {
            "dataset": "vctk",
            "train": indices[0:num_train_samples],
            "validation": indices[
                num_train_samples : num_train_samples + num_validation_samples
            ],
            "test": indices[num_train_samples + num_validation_samples :],
        }

    with open(os.path.join(path, SPLIT_JSON), "w") as handle:
        json.dump(split, handle)  # pyre-ignore