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BirdSet

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Audio Classification
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bird classification
passive acoustic monitoring
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# 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)
}