BirdSet.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BirdSet: The General Avian Monitoring Evaluation Benchmark"""

import os
import datasets
import pandas as pd
from tqdm.auto import tqdm
import tarfile

from . import classes

from .classes import BIRD_NAMES_NIPS4BPLUS, BIRD_NAMES_AMAZON_BASIN, BIRD_NAMES_HAWAII, \
    BIRD_NAMES_HIGH_SIERRAS, BIRD_NAMES_SIERRA_NEVADA, BIRD_NAMES_POWDERMILL_NATURE, BIRD_NAMES_SAPSUCKER, \
    BIRD_NAMES_COLUMBIA_COSTA_RICA, BIRD_NAMES_XENOCANTO, BIRD_NAMES_XENOCANTO_M

from .descriptions import _NIPS4BPLUS_CITATION, _NIPS4BPLUS_DESCRIPTION, \
    _HIGH_SIERRAS_DESCRIPTION, _HIGH_SIERRAS_CITATION, _SIERRA_NEVADA_DESCRIPTION, _SIERRA_NEVADA_CITATION, \
    _POWDERMILL_NATURE_DESCRIPTION, _POWDERMILL_NATURE_CITATION, _AMAZON_BASIN_DESCRIPTION, _AMAZON_BASIN_CITATION, \
    _SAPSUCKER_WOODS_DESCRIPTION, _SAPSUCKER_WOODS_CITATION, _COLUMBIA_COSTA_RICA_CITATION, \
    _COLUMBIA_COSTA_RICA_DESCRIPTION, _HAWAIIAN_ISLANDS_CITATION, _HAWAIIAN_ISLANDS_DESCRIPTION


#############################################
_BIRDSET_CITATION = """\
    @article{birdset,
        title = {BirdSet: A Multi-Task Benchmark For Avian Diversity Monitoring},
        author={anonymous},
        year={2024}
    }
"""
_BIRDSET_DESCRIPTION = """\
    This dataset offers a unified, well-structured platform for avian bioacoustics and consists of various tasks. \
    By creating a set of tasks, BirdSet enables an overall performance score for models and uncovers their limitations \
    in certain areas.
    Note that each BirdSet dataset has its own citation. Please see the source to get the correct citation for each 
    contained dataset. 
"""

base_url = "https://huggingface.co/datasets/DBD-research-group/BirdSet/resolve/data"


def _extract_all_to_same_folder(tar_path, output_dir):
    """custom extraction for tar.gz files, that extracts all files to output_dir without subfolders"""
    # check if data already exists
    if not os.path.isfile(output_dir) and os.path.isdir(output_dir) and os.listdir(output_dir):
        return output_dir
    os.makedirs(output_dir, exist_ok=True)

    with tarfile.open(tar_path, "r:gz") as tar:
        for member in tar.getmembers():
            if member.isfile():
                member.name = os.path.basename(member.name)
                tar.extract(member, path=output_dir)

    return output_dir


def _extract_and_delete(dl_dir: dict) -> dict:
    """extracts downloaded files and deletes the archive file immediately, with progress bar.
    only the processed archive and its content are saved at the same time."""
    audio_paths = {name: [] for name, data in dl_dir.items() if isinstance(data, list)}
    for name, data in dl_dir.items():
        if not isinstance(data, list):
            continue

        # extract and immediately delete archives
        for path in tqdm(data, f"Extracting {name} split"):
            head, tail = os.path.split(path)
            output_dir = os.path.join(head, "extracted", tail)
            #audio_path = dl_manager.extract(path) # if all archive files are without subfolders this works just fine
            audio_path = _extract_all_to_same_folder(path, output_dir)
            os.remove(path)
            os.remove(f"{path}.lock")
            os.remove(f"{path}.json")
            audio_paths[name].append(audio_path)

    return audio_paths


class BirdSetConfig(datasets.BuilderConfig):
    def __init__(
            self,
            name,
            citation,
            class_list,
            genus_list,
            species_group_list,
            order_list,
            **kwargs):
        super().__init__(version=datasets.Version("0.0.4"), name=name, **kwargs)

        features = datasets.Features({
            "audio": datasets.Audio(sampling_rate=32_000, mono=True, decode=False),
            "filepath": datasets.Value("string"),
            "start_time": datasets.Value("float64"),  # can be changed to timestamp later
            "end_time": datasets.Value("float64"),
            "low_freq": datasets.Value("int64"),
            "high_freq": datasets.Value("int64"),
            "ebird_code": datasets.ClassLabel(names=class_list),
            "ebird_code_multilabel": datasets.Sequence(datasets.ClassLabel(names=class_list)),
            "ebird_code_secondary": datasets.Sequence(datasets.Value("string")),
            "call_type": datasets.Value("string"),
            "sex": datasets.Value("string"),
            "lat": datasets.Value("float64"),
            "long": datasets.Value("float64"),
            "length": datasets.Value("int64"),
            "microphone": datasets.Value("string"),
            "license": datasets.Value("string"),
            "source": datasets.Value("string"),
            "local_time": datasets.Value("string"),
            "detected_events": datasets.Sequence(datasets.Sequence(datasets.Value("float64"))),
            "event_cluster": datasets.Sequence(datasets.Value("int64")),
            "peaks": datasets.Sequence(datasets.Value("float64")),
            "quality": datasets.Value("string"),
            "recordist": datasets.Value("string"),
            "genus": datasets.ClassLabel(names=genus_list),
            "species_group": datasets.ClassLabel(names=species_group_list),
            "order": datasets.ClassLabel(names=order_list),
            "genus_multilabel": datasets.Sequence(datasets.ClassLabel(names=genus_list)),
            "species_group_multilabel": datasets.Sequence(datasets.ClassLabel(names=species_group_list)),
            "order_multilabel": datasets.Sequence(datasets.ClassLabel(names=order_list)),
        })

        self.features = features
        self.citation = citation


class BirdSet(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""
    # ram problems?
    DEFAULT_WRITER_BATCH_SIZE = 500

    BUILDER_CONFIGS = [
        BirdSetConfig(
            name="SSW",
            description=_SAPSUCKER_WOODS_DESCRIPTION,
            citation=_SAPSUCKER_WOODS_CITATION,
            data_dir=f"{base_url}/SSW",
            class_list=BIRD_NAMES_SAPSUCKER,
            genus_list=classes.GENUS_SSW,
            species_group_list=classes.SPECIES_GROUP_SSW,
            order_list=classes.ORDER_SSW,
        ),
        BirdSetConfig(
            name="SSW_xc",
            description=_SAPSUCKER_WOODS_DESCRIPTION,
            citation=_SAPSUCKER_WOODS_CITATION,
            data_dir=f"{base_url}/SSW",
            class_list=BIRD_NAMES_SAPSUCKER,
            genus_list=classes.GENUS_SSW,
            species_group_list=classes.SPECIES_GROUP_SSW,
            order_list=classes.ORDER_SSW,
        ),
        BirdSetConfig(
            name="SSW_scape",
            description=_SAPSUCKER_WOODS_DESCRIPTION,
            citation=_SAPSUCKER_WOODS_CITATION,
            data_dir=f"{base_url}/SSW",
            class_list=BIRD_NAMES_SAPSUCKER,
            genus_list=classes.GENUS_SSW,
            species_group_list=classes.SPECIES_GROUP_SSW,
            order_list=classes.ORDER_SSW,
        ),
        BirdSetConfig(
            name="PER",
            description=_AMAZON_BASIN_DESCRIPTION,
            citation=_AMAZON_BASIN_CITATION,
            data_dir=f"{base_url}/PER",
            class_list=BIRD_NAMES_AMAZON_BASIN,
            genus_list=classes.GENUS_PER,
            species_group_list=classes.SPECIES_GROUP_PER,
            order_list=classes.ORDER_PER,
        ),
        BirdSetConfig(
            name="PER_xc",
            description=_AMAZON_BASIN_DESCRIPTION,
            citation=_AMAZON_BASIN_CITATION,
            data_dir=f"{base_url}/PER",
            class_list=BIRD_NAMES_AMAZON_BASIN,
            genus_list=classes.GENUS_PER,
            species_group_list=classes.SPECIES_GROUP_PER,
            order_list=classes.ORDER_PER,
        ),
        BirdSetConfig(
            name="PER_scape",
            description=_AMAZON_BASIN_DESCRIPTION,
            citation=_AMAZON_BASIN_CITATION,
            data_dir=f"{base_url}/PER",
            class_list=BIRD_NAMES_AMAZON_BASIN,
            genus_list=classes.GENUS_PER,
            species_group_list=classes.SPECIES_GROUP_PER,
            order_list=classes.ORDER_PER,
        ),
        BirdSetConfig(
            name="UHH",
            description=_HAWAIIAN_ISLANDS_DESCRIPTION,
            citation=_HAWAIIAN_ISLANDS_CITATION,
            data_dir=f"{base_url}/UHH",
            class_list=BIRD_NAMES_HAWAII,
            genus_list=classes.GENUS_UHH,
            species_group_list=classes.SPECIES_GROUP_UHH,
            order_list=classes.ORDER_UHH,
        ),
        BirdSetConfig(
            name="UHH_xc",
            description=_HAWAIIAN_ISLANDS_DESCRIPTION,
            citation=_HAWAIIAN_ISLANDS_CITATION,
            data_dir=f"{base_url}/UHH",
            class_list=BIRD_NAMES_HAWAII,
            genus_list=classes.GENUS_UHH,
            species_group_list=classes.SPECIES_GROUP_UHH,
            order_list=classes.ORDER_UHH,
        ),
        BirdSetConfig(
            name="UHH_scape",
            description=_HAWAIIAN_ISLANDS_DESCRIPTION,
            citation=_HAWAIIAN_ISLANDS_CITATION,
            data_dir=f"{base_url}/UHH",
            class_list=BIRD_NAMES_HAWAII,
            genus_list=classes.GENUS_UHH,
            species_group_list=classes.SPECIES_GROUP_UHH,
            order_list=classes.ORDER_UHH,
        ),
        BirdSetConfig(
            name="SNE",
            description=_SIERRA_NEVADA_DESCRIPTION,
            citation=_SIERRA_NEVADA_CITATION,
            data_dir=f"{base_url}/SNE",
            class_list=BIRD_NAMES_SIERRA_NEVADA,
            genus_list=classes.GENUS_SNE,
            species_group_list=classes.SPECIES_GROUP_SNE,
            order_list=classes.ORDER_SNE,
        ),
        BirdSetConfig(
            name="SNE_xc",
            description=_SIERRA_NEVADA_DESCRIPTION,
            citation=_SIERRA_NEVADA_CITATION,
            data_dir=f"{base_url}/SNE",
            class_list=BIRD_NAMES_SIERRA_NEVADA,
            genus_list=classes.GENUS_SNE,
            species_group_list=classes.SPECIES_GROUP_SNE,
            order_list=classes.ORDER_SNE,
        ),
        BirdSetConfig(
            name="SNE_scape",
            description=_SIERRA_NEVADA_DESCRIPTION,
            citation=_SIERRA_NEVADA_CITATION,
            data_dir=f"{base_url}/SNE",
            class_list=BIRD_NAMES_SIERRA_NEVADA,
            genus_list=classes.GENUS_SNE,
            species_group_list=classes.SPECIES_GROUP_SNE,
            order_list=classes.ORDER_SNE,
        ),
        BirdSetConfig(
            name="POW",
            description=_POWDERMILL_NATURE_DESCRIPTION,
            citation=_POWDERMILL_NATURE_CITATION,
            data_dir=f"{base_url}/POW",
            class_list=BIRD_NAMES_POWDERMILL_NATURE,
            genus_list=classes.GENUS_POW,
            species_group_list=classes.SPECIES_GROUP_POW,
            order_list=classes.ORDER_POW,
        ),
        BirdSetConfig(
            name="POW_xc",
            description=_POWDERMILL_NATURE_DESCRIPTION,
            citation=_POWDERMILL_NATURE_CITATION,
            data_dir=f"{base_url}/POW",
            class_list=BIRD_NAMES_POWDERMILL_NATURE,
            genus_list=classes.GENUS_POW,
            species_group_list=classes.SPECIES_GROUP_POW,
            order_list=classes.ORDER_POW,
        ),
        BirdSetConfig(
            name="POW_scape",
            description=_POWDERMILL_NATURE_DESCRIPTION,
            citation=_POWDERMILL_NATURE_CITATION,
            data_dir=f"{base_url}/POW",
            class_list=BIRD_NAMES_POWDERMILL_NATURE,
            genus_list=classes.GENUS_POW,
            species_group_list=classes.SPECIES_GROUP_POW,
            order_list=classes.ORDER_POW,
        ),
        BirdSetConfig(
            name="HSN",
            description=_HIGH_SIERRAS_DESCRIPTION,
            citation=_HIGH_SIERRAS_CITATION,
            data_dir=f"{base_url}/HSN",
            class_list=BIRD_NAMES_HIGH_SIERRAS,
            genus_list=classes.GENUS_HSN,
            species_group_list=classes.SPECIES_GROUP_HSN,
            order_list=classes.ORDER_HSN,
        ),
        BirdSetConfig(
            name="HSN_xc",
            description=_HIGH_SIERRAS_DESCRIPTION,
            citation=_HIGH_SIERRAS_CITATION,
            data_dir=f"{base_url}/HSN",
            class_list=BIRD_NAMES_HIGH_SIERRAS,
            genus_list=classes.GENUS_HSN,
            species_group_list=classes.SPECIES_GROUP_HSN,
            order_list=classes.ORDER_HSN,
        ),
        BirdSetConfig(
            name="HSN_scape",
            description=_HIGH_SIERRAS_DESCRIPTION,
            citation=_HIGH_SIERRAS_CITATION,
            data_dir=f"{base_url}/HSN",
            class_list=BIRD_NAMES_HIGH_SIERRAS,
            genus_list=classes.GENUS_HSN,
            species_group_list=classes.SPECIES_GROUP_HSN,
            order_list=classes.ORDER_HSN,
        ),
        BirdSetConfig(
            name="NES",
            description=_COLUMBIA_COSTA_RICA_DESCRIPTION,
            citation=_COLUMBIA_COSTA_RICA_CITATION,
            data_dir=f"{base_url}/NES",
            class_list=BIRD_NAMES_COLUMBIA_COSTA_RICA,
            genus_list=classes.GENUS_NES,
            species_group_list=classes.SPECIES_GROUP_NES,
            order_list=classes.ORDER_NES,
        ),
        BirdSetConfig(
            name="NES_xc",
            description=_COLUMBIA_COSTA_RICA_DESCRIPTION,
            citation=_COLUMBIA_COSTA_RICA_CITATION,
            data_dir=f"{base_url}/NES",
            class_list=BIRD_NAMES_COLUMBIA_COSTA_RICA,
            genus_list=classes.GENUS_NES,
            species_group_list=classes.SPECIES_GROUP_NES,
            order_list=classes.ORDER_NES,
        ),
        BirdSetConfig(
            name="NES_scape",
            description=_COLUMBIA_COSTA_RICA_DESCRIPTION,
            citation=_COLUMBIA_COSTA_RICA_CITATION,
            data_dir=f"{base_url}/NES",
            class_list=BIRD_NAMES_COLUMBIA_COSTA_RICA,
            genus_list=classes.GENUS_NES,
            species_group_list=classes.SPECIES_GROUP_NES,
            order_list=classes.ORDER_NES,
        ),
        BirdSetConfig(
            name="NBP",
            description=_NIPS4BPLUS_DESCRIPTION,
            citation=_NIPS4BPLUS_CITATION,
            data_dir=f"{base_url}/NBP",
            class_list=BIRD_NAMES_NIPS4BPLUS,
            genus_list=classes.GENUS_NBP,
            species_group_list=classes.SPECIES_GROUP_NBP,
            order_list=classes.ORDER_NBP,
        ),
        BirdSetConfig(
            name="NBP_xc",
            description=_NIPS4BPLUS_DESCRIPTION,
            citation=_NIPS4BPLUS_CITATION,
            data_dir=f"{base_url}/NBP",
            class_list=BIRD_NAMES_NIPS4BPLUS,
            genus_list=classes.GENUS_NBP,
            species_group_list=classes.SPECIES_GROUP_NBP,
            order_list=classes.ORDER_NBP,
        ),
        BirdSetConfig(
            name="NBP_scape",
            description=_NIPS4BPLUS_DESCRIPTION,
            citation=_NIPS4BPLUS_CITATION,
            data_dir=f"{base_url}/NBP",
            class_list=BIRD_NAMES_NIPS4BPLUS,
            genus_list=classes.GENUS_NBP,
            species_group_list=classes.SPECIES_GROUP_NBP,
            order_list=classes.ORDER_NBP,
        ),
        BirdSetConfig(
            name="XCM",
            description="TODO",
            citation="TODO",
            data_dir=f"{base_url}/XCM",
            class_list=BIRD_NAMES_XENOCANTO_M,
            genus_list=classes.GENUS_XCM,
            species_group_list=classes.SPECIES_GROUP_XCM,
            order_list=classes.ORDER_XCM,
        ),
        BirdSetConfig(
            name="XCL",
            description="TODO",
            citation="TODO",
            data_dir=f"{base_url}/XCL",
            class_list=BIRD_NAMES_XENOCANTO,
            genus_list=classes.GENUS_XCL,
            species_group_list=classes.SPECIES_GROUP_XCL,
            order_list=classes.ORDER_XCL,
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_BIRDSET_DESCRIPTION + self.config.description,
            features=self.config.features,
            citation=self.config.citation + "\n" + _BIRDSET_CITATION,
        )

    def _split_generators(self, dl_manager):
        ds_name = self.config.name
        # settings for how much archives (tar.gz) files are uploaded for a specific dataset
        train_files = {"PER": 11,
                       "NES": 13,
                       "UHH": 5,
                       "HSN": 7,
                       "NBP": 32,
                       "POW": 9,
                       "SSW": 29,
                       "SNE": 21,
                       "XCM": 182,
                       "XCL": 98}

        test_files = {"PER": 3,
                      "NES": 8,
                      "UHH": 7,
                      "HSN": 3,
                      "NBP": 1,
                      "POW": 3,
                      "SSW": 36,
                      "SNE": 5}

        test_5s_files = {"PER": 1,
                      "NES": 1,
                      "UHH": 1,
                      "HSN": 1,
                      "NBP": 1,
                      "POW": 1,
                      "SSW": 4,
                      "SNE": 1}

        # different configs, determine what needs to be downloaded
        if self.config.name.endswith("_xc"):
            ds_name = ds_name[:-3]
            dl_dir = dl_manager.download({
                "train": [os.path.join(self.config.data_dir, f"{ds_name}_train_shard_{n:04d}.tar.gz") for n in range(1, train_files[ds_name] + 1)],
                "meta_train": os.path.join(self.config.data_dir, f"{ds_name}_metadata_train.parquet"),
            })

        elif self.config.name.endswith("_scape"):
            ds_name = ds_name[:-6]
            dl_dir = dl_manager.download({
                "test": [os.path.join(self.config.data_dir, f"{ds_name}_test_shard_{n:04d}.tar.gz") for n in range(1, test_files[ds_name] + 1)],
                "test_5s": [os.path.join(self.config.data_dir, f"{ds_name}_test5s_shard_{n:04d}.tar.gz") for n in range(1, test_5s_files[ds_name] + 1)],
                "meta_test": os.path.join(self.config.data_dir, f"{ds_name}_metadata_test.parquet"),
                "meta_test_5s": os.path.join(self.config.data_dir, f"{ds_name}_metadata_test_5s.parquet"),
            })

        # use POW for XCM/XCL validation
        elif self.config.name.startswith("XC"):
            dl_dir = dl_manager.download({
                "train": [os.path.join(self.config.data_dir, f"{ds_name}_shard_{n:04d}.tar.gz") for n in range(1, train_files[ds_name] + 1)],
                "valid": [os.path.join(self.config.data_dir[:-3] + "POW", f"POW_test5s_shard_{n:04d}.tar.gz") for n in range(1, test_5s_files["POW"] + 1)],
                "meta_train": os.path.join(self.config.data_dir, f"{ds_name}_metadata.parquet"),
                "meta_valid": os.path.join(self.config.data_dir[:-3] + "POW", f"POW_metadata_test_5s.parquet"),
            })

        else:
            dl_dir = dl_manager.download({
                "train": [os.path.join(self.config.data_dir, f"{ds_name}_train_shard_{n:04d}.tar.gz") for n in range(1, train_files[ds_name] + 1)],
                "test": [os.path.join(self.config.data_dir, f"{ds_name}_test_shard_{n:04d}.tar.gz") for n in range(1, test_files[ds_name] + 1)],
                "test_5s": [os.path.join(self.config.data_dir, f"{ds_name}_test5s_shard_{n:04d}.tar.gz") for n in range(1, test_5s_files[ds_name] + 1)],
                "meta_train": os.path.join(self.config.data_dir, f"{ds_name}_metadata_train.parquet"),
                "meta_test": os.path.join(self.config.data_dir, f"{ds_name}_metadata_test.parquet"),
                "meta_test_5s": os.path.join(self.config.data_dir, f"{ds_name}_metadata_test_5s.parquet"),
            })

        # custom extraction that deletes archives right after extraction
        audio_paths = _extract_and_delete(dl_dir) if not dl_manager.is_streaming else None

        # construct split generators
        # assumes every key in dl_dir of NAME also has meta_NAME
        names = [name for name in dl_dir.keys() if not name.startswith("meta_")]
        is_streaming = dl_manager.is_streaming

        return [datasets.SplitGenerator(
            name=name,
            gen_kwargs={
                "audio_archive_iterators": (dl_manager.iter_archive(archive_path) for archive_path in dl_dir[name]) if is_streaming else () ,
                "audio_extracted_paths": audio_paths[name] if not is_streaming else (),
                "meta_path": dl_dir[f"meta_{name}"],
                "split": name
            }
        ) for name in names]


    def _generate_examples(self, audio_archive_iterators, audio_extracted_paths, meta_path, split):
        metadata = pd.read_parquet(meta_path)
        if metadata.index.name != "filepath":  # TODO could be removed by changing train metadata files to have filepath as index
            metadata.index = metadata["filepath"].str.split("/").apply(lambda x: x[-1]) # TODO work around for update_1 inconsistent filepath

        idx = 0
        # in case of streaming
        for audio_archive_iterator in audio_archive_iterators:
            for audio_path_in_archive, audio_file in audio_archive_iterator:
                file_name = os.path.split(audio_path_in_archive)[-1]
                rows = metadata.loc[[file_name]]
                audio = audio_file.read()
                for _, row in rows.iterrows():
                    yield idx, self._metadata_from_row(row, split, audio_path=file_name, audio=audio)
                idx += 1

        # in case of not streaming
        for audio_extracted_path in audio_extracted_paths:
            audio_files = os.listdir(audio_extracted_path)
            current_metadata = metadata.loc[audio_files]
            for audio_file, row in current_metadata.iterrows():
                audio_path = os.path.join(audio_extracted_path, audio_file)
                yield idx, self._metadata_from_row(row, split, audio_path=audio_path)
                idx += 1


    @staticmethod
    def _metadata_from_row(row, split: str, audio_path=None, audio=None) -> dict:
        return {"audio": audio_path if not audio else {"path": None, "bytes": audio},
                "filepath": audio_path,
                "start_time": row["start_time"],
                "end_time": row["end_time"],
                "low_freq": row["low_freq"],
                "high_freq": row["high_freq"],
                "ebird_code": row["ebird_code"] if split != "test_5s" else None,
                "ebird_code_multilabel": row.get("ebird_code_multilabel", None),
                "ebird_code_secondary": row.get("ebird_code_secondary", None),
                "call_type": row["call_type"],
                "sex": row["sex"],
                "lat": row["lat"],
                "long": row["long"],
                "length": row.get("length", None),
                "microphone": row["microphone"],
                "license": row.get("license", None),
                "source": row["source"],
                "local_time": row["local_time"],
                "detected_events": row.get("detected_events", None),
                "event_cluster": row.get("event_cluster", None),
                "peaks": row.get("peaks", None),
                "quality": row.get("quality", None),
                "recordist": row.get("recordist", None),
                "genus": row.get("genus", None) if split != "test_5s" else None,
                "species_group": row.get("species_group", None) if split != "test_5s" else None,
                "order": row.get("order", None) if split != "test_5s" else None,
                "genus_multilabel": row.get("genus_multilabel", [row.get("genus")]),
                "species_group_multilabel": row.get("species_group_multilabel", [row.get("species_group")]),
                "order_multilabel": row.get("order_multilabel", [row.get("order")]),
            }