# 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) }