def extract_acoustic_features(dataset, output_path, cfg, n_workers=1):
    """Extract acoustic features of utterances in the dataset

    Args:
        dataset (str): name of dataset, e.g. opencpop
        output_path (str): directory that stores train, test and feature files of datasets
        cfg (dict): dictionary that stores configurations
        n_workers (int, optional): num of processes to extract features in parallel. Defaults to 1.
    """
    types = ["train", "test"] if "eval" not in dataset else ["test"]
    metadata = []
    dataset_output = os.path.join(output_path, dataset)

    for dataset_type in types:
        dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type))
        with open(dataset_file, "r") as f:
            metadata.extend(json.load(f))

        # acoustic_extractor.extract_utt_acoustic_features_parallel(
        #     metadata, dataset_output, cfg, n_workers=n_workers
        # )
    acoustic_extractor.extract_utt_acoustic_features_serial(
        metadata, dataset_output, cfg
    )
Extract acoustic features of utterances in the dataset

Args:
dataset (str): name of dataset, e.g. opencpop
output_path (str): directory that stores train, test and feature files of datasets
cfg (dict): dictionary that stores configurations
n_workers (int, optional): num of processes to extract features in parallel. Defaults to 1.
bins/svc/preprocess.py