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.
# TODO: Address all TODOs and remove all explanatory comments
"""BirdSet: The General Avian Monitoring Evaluation Benchmark"""

import os
import datasets
import pandas as pd

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 _BIRD_DB_CITATION, _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{rauch2024,
        title = {BirdSet: A Multi-Task Benchmark For Avian Diversity Monitoring},
        author={Rauch, Lukas and Schwinger, Raphael and Wirth, Moritz and Lange, Jonas and Heinrich, René},
        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/gadme/resolve/data"


class BirdSetConfig(datasets.BuilderConfig):
    def __init__(
            self,
            name,
            citation,
            class_list,
            **kwargs):
        super().__init__(version=datasets.Version("0.0.2"), 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"),
        })

        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,
        ),
        BirdSetConfig(
            name="SSW_xc",
            description=_SAPSUCKER_WOODS_DESCRIPTION,
            citation=_SAPSUCKER_WOODS_CITATION,
            data_dir=f"{base_url}/SSW",
            class_list=BIRD_NAMES_SAPSUCKER,
        ),
        BirdSetConfig(
            name="SSW_scape",
            description=_SAPSUCKER_WOODS_DESCRIPTION,
            citation=_SAPSUCKER_WOODS_CITATION,
            data_dir=f"{base_url}/SSW",
            class_list=BIRD_NAMES_SAPSUCKER,
        ),
        BirdSetConfig(
            name="PER",
            description=_AMAZON_BASIN_DESCRIPTION,
            citation=_AMAZON_BASIN_CITATION,
            data_dir=f"{base_url}/PER",
            class_list=BIRD_NAMES_AMAZON_BASIN,
        ),
        BirdSetConfig(
            name="PER_xc",
            description=_AMAZON_BASIN_DESCRIPTION,
            citation=_AMAZON_BASIN_CITATION,
            data_dir=f"{base_url}/PER",
            class_list=BIRD_NAMES_AMAZON_BASIN,
        ),
        BirdSetConfig(
            name="PER_scape",
            description=_AMAZON_BASIN_DESCRIPTION,
            citation=_AMAZON_BASIN_CITATION,
            data_dir=f"{base_url}/PER",
            class_list=BIRD_NAMES_AMAZON_BASIN,
        ),
        BirdSetConfig(
            name="UHH",
            description=_HAWAIIAN_ISLANDS_DESCRIPTION,
            citation=_HAWAIIAN_ISLANDS_CITATION,
            data_dir=f"{base_url}/UHH",
            class_list=BIRD_NAMES_HAWAII,
        ),
        BirdSetConfig(
            name="UHH_xc",
            description=_HAWAIIAN_ISLANDS_DESCRIPTION,
            citation=_HAWAIIAN_ISLANDS_CITATION,
            data_dir=f"{base_url}/UHH",
            class_list=BIRD_NAMES_HAWAII,
        ),
        BirdSetConfig(
            name="UHH_scape",
            description=_HAWAIIAN_ISLANDS_DESCRIPTION,
            citation=_HAWAIIAN_ISLANDS_CITATION,
            data_dir=f"{base_url}/UHH",
            class_list=BIRD_NAMES_HAWAII,
        ),
        BirdSetConfig(
            name="SNE",
            description=_SIERRA_NEVADA_DESCRIPTION,
            citation=_SIERRA_NEVADA_CITATION,
            data_dir=f"{base_url}/SNE",
            class_list=BIRD_NAMES_SIERRA_NEVADA,
        ),
        BirdSetConfig(
            name="SNE_xc",
            description=_SIERRA_NEVADA_DESCRIPTION,
            citation=_SIERRA_NEVADA_CITATION,
            data_dir=f"{base_url}/SNE",
            class_list=BIRD_NAMES_SIERRA_NEVADA,
        ),
        BirdSetConfig(
            name="SNE_scape",
            description=_SIERRA_NEVADA_DESCRIPTION,
            citation=_SIERRA_NEVADA_CITATION,
            data_dir=f"{base_url}/SNE",
            class_list=BIRD_NAMES_SIERRA_NEVADA,
        ),
        BirdSetConfig(
            name="POW",
            description=_POWDERMILL_NATURE_DESCRIPTION,
            citation=_POWDERMILL_NATURE_CITATION,
            data_dir=f"{base_url}/POW",
            class_list=BIRD_NAMES_POWDERMILL_NATURE,
        ),
        BirdSetConfig(
            name="POW_xc",
            description=_POWDERMILL_NATURE_DESCRIPTION,
            citation=_POWDERMILL_NATURE_CITATION,
            data_dir=f"{base_url}/POW",
            class_list=BIRD_NAMES_POWDERMILL_NATURE,
        ),
        BirdSetConfig(
            name="POW_scape",
            description=_POWDERMILL_NATURE_DESCRIPTION,
            citation=_POWDERMILL_NATURE_CITATION,
            data_dir=f"{base_url}/POW",
            class_list=BIRD_NAMES_POWDERMILL_NATURE,
        ),
        BirdSetConfig(
            name="HSN",
            description=_HIGH_SIERRAS_DESCRIPTION,
            citation=_HIGH_SIERRAS_CITATION,
            data_dir=f"{base_url}/HSN",
            class_list=BIRD_NAMES_HIGH_SIERRAS,
        ),
        BirdSetConfig(
            name="HSN_xc",
            description=_HIGH_SIERRAS_DESCRIPTION,
            citation=_HIGH_SIERRAS_CITATION,
            data_dir=f"{base_url}/HSN",
            class_list=BIRD_NAMES_HIGH_SIERRAS,
        ),
        BirdSetConfig(
            name="HSN_scape",
            description=_HIGH_SIERRAS_DESCRIPTION,
            citation=_HIGH_SIERRAS_CITATION,
            data_dir=f"{base_url}/HSN",
            class_list=BIRD_NAMES_HIGH_SIERRAS,
        ),
        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,
        ),
        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,
        ),
        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,
        ),
        BirdSetConfig(
            name="NBP",
            description=_NIPS4BPLUS_DESCRIPTION,
            citation=_NIPS4BPLUS_CITATION,
            data_dir=f"{base_url}/NBP",
            class_list=BIRD_NAMES_NIPS4BPLUS,
        ),
        BirdSetConfig(
            name="NBP_xc",
            description=_NIPS4BPLUS_DESCRIPTION,
            citation=_NIPS4BPLUS_CITATION,
            data_dir=f"{base_url}/NBP",
            class_list=BIRD_NAMES_NIPS4BPLUS,
        ),
        BirdSetConfig(
            name="NBP_scape",
            description=_NIPS4BPLUS_DESCRIPTION,
            citation=_NIPS4BPLUS_CITATION,
            data_dir=f"{base_url}/NBP",
            class_list=BIRD_NAMES_NIPS4BPLUS,
        ),
        BirdSetConfig(
            name="XCM",
            description="TODO",
            citation="TODO",
            data_dir=f"{base_url}/XCM",
            class_list=BIRD_NAMES_XENOCANTO_M,
        ),
        BirdSetConfig(
            name="XCL",
            description="TODO",
            citation="TODO",
            data_dir=f"{base_url}/XCL",
            class_list=BIRD_NAMES_XENOCANTO,
        ),
    ]

    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
        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}

        test5s_files = {"PER": 1,
                      "NES": 1,
                      "UHH": 1,
                      "HSN": 1,
                      "NBP": 1,
                      "POW": 1,
                      "SSW": 4,
                      "SNE": 1}

        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)],
                "metadata": 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, test5s_files[ds_name] + 1)],
                "metadata": os.path.join(self.config.data_dir, f"{ds_name}_metadata_test.parquet"),
                "metadata_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, test5s_files["POW"] + 1)],
                "metadata": os.path.join(self.config.data_dir, f"{ds_name}_metadata.parquet"),
                "meta_test_5s": os.path.join(self.config.data_dir[:-3] + "POW", f"POW_metadata_test_5s.parquet"),
            })

        elif self.config.name in train_files.keys():
            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, test5s_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"),
            })

        local_audio_archives_paths = dl_manager.extract(dl_dir) if not dl_manager.is_streaming else None

        if self.config.name.startswith("XC") or self.config.name.endswith("_xc"):
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "audio_archive_iterators": [dl_manager.iter_archive(archive_path) for archive_path in dl_dir["train"]],
                        "local_audio_archives_paths": local_audio_archives_paths["train"] if local_audio_archives_paths else None,
                        "metapath": dl_dir["metadata"],
                        "split": datasets.Split.TRAIN,
                    },
                ),
                datasets.SplitGenerator(
                    name="valid",
                    gen_kwargs={
                        "audio_archive_iterators": [dl_manager.iter_archive(archive_path) for archive_path in dl_dir["valid"]],
                        "local_audio_archives_paths": local_audio_archives_paths["valid"] if local_audio_archives_paths else None,
                        "metapath": dl_dir["meta_test_5s"],
                        "split": "valid",
                    },
                ),
            ]

        elif self.config.name.endswith("_scape"):
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "audio_archive_iterators": [dl_manager.iter_archive(archive_path) for archive_path in dl_dir["test"]],
                        "local_audio_archives_paths": local_audio_archives_paths["test"] if local_audio_archives_paths else None,
                        "metapath": dl_dir["metadata"],
                        "split": datasets.Split.TEST,
                    },
                ),
                datasets.SplitGenerator(
                    name="test_5s",
                    gen_kwargs={
                        "audio_archive_iterators": [dl_manager.iter_archive(archive_path) for archive_path in dl_dir["test_5s"]],
                        "local_audio_archives_paths": local_audio_archives_paths["test_5s"] if local_audio_archives_paths else None,
                        "metapath": dl_dir["metadata_5s"],
                        "split": "test_multilabel"
                    },
                ),
            ]

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "audio_archive_iterators": [dl_manager.iter_archive(archive_path) for archive_path in dl_dir["train"]],
                    "local_audio_archives_paths": local_audio_archives_paths["train"] if local_audio_archives_paths else None,
                    "metapath": dl_dir["meta_train"],
                    "split": datasets.Split.TRAIN,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "audio_archive_iterators": [dl_manager.iter_archive(archive_path) for archive_path in dl_dir["test"]],
                    "local_audio_archives_paths": local_audio_archives_paths["test"] if local_audio_archives_paths else None,
                    "metapath": dl_dir["meta_test"],
                    "split": datasets.Split.TEST,
                },
            ),
            datasets.SplitGenerator(
                name="test_5s",
                gen_kwargs={
                    "audio_archive_iterators": [dl_manager.iter_archive(archive_path) for archive_path in dl_dir["test_5s"]],
                    "local_audio_archives_paths": local_audio_archives_paths["test_5s"] if local_audio_archives_paths else None,
                    "metapath": dl_dir["meta_test_5s"],
                    "split": "test_multilabel"
                },
            ),
        ]

    def _generate_examples(self, audio_archive_iterators, local_audio_archives_paths, metapath, split):
        metadata = pd.read_parquet(metapath)
        idx = 0
        for i, audio_archive_iterator in enumerate(audio_archive_iterators):
            for audio_path_in_archive, audio_file in audio_archive_iterator:
                id = os.path.split(audio_path_in_archive)[-1]
                rows = metadata[metadata.index == (int(id[2:].split(".")[0]) if split == "train" else id)]
                audio_path = os.path.join(local_audio_archives_paths[i], audio_path_in_archive) if local_audio_archives_paths else audio_path_in_archive

                audio = audio_path if local_audio_archives_paths else audio_file.read()
                for _, row in rows.iterrows():
                    idx += 1
                    yield id if split == "train" else idx, {
                        "audio": 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_multilabel" else None,
                        #"ebird_code_multilabel": row.get("ebird_code_multilabel", None),
                        "ebird_code_multilabel": row.get("ebird_code_multilabel", None) if "no_call" not in row.get("ebird_code_multilabel", []) else [],
                        "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)
                    }