add dataset building script

NBMSet24.py

@@ -0,0 +1,263 @@
+"""NBMSet24: Nocturnal Bird Migration Dataset"""
+
+import os
+import datasets
+import pandas as pd
+from tqdm.auto import tqdm
+import tarfile
+
+from classes import BIRD_NAMES_NBM
+
+from descriptions import _NBM_CITATION, _NBM_DESCRIPTION
+
+
+#############################################
+_NBMSET24_CITATION = """\
+    @article{birdset,
+        title = {NBMSet24},
+        author={anonymous},
+        year={2025}
+    }
+"""
+_NBMSET24_DESCRIPTION = """\
+    Dataset from https://arxiv.org/abs/2412.03633
+"""
+
+base_url = "https://huggingface.co/datasets/DBD-research-group/NBMSet24"
+
+
+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)
+            # datasets >3.0.0 hadels cach differently
+            os.remove(f"{path}.lock") if os.path.exists(f"{path}.lock") else None
+            os.remove(f"{path}.json") if os.path.exists(f"{path}.json") else None
+            audio_paths[name].append(audio_path)
+
+    return audio_paths
+
+
+class NBMSetConfig(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"),
+            "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")),
+            "label":datasets.Value("string"),
+            "original_label": datasets.Value("string"),
+            # "french_label":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 = [
+        NBMSetConfig(
+            name="NBM",
+            description=_NBMSET24_DESCRIPTION,
+            citation=_NBMSET24_CITATION,
+            data_dir=f"{base_url}",
+            class_list=BIRD_NAMES_NBM,
+            # genus_list=classes.GENUS_NBM,
+            # species_group_list=classes.SPECIES_GROUP_NBM,
+            # order_list=classes.ORDER_NBM,
+        ),
+        # NBMSetConfig(
+        #     name="NBM_xc",
+        #     description=_NBMSET24_DESCRIPTION,
+        #     citation=_NBMSET24_CITATION,
+        #     data_dir=f"{base_url}",
+        #     class_list=BIRD_NAMES_NBM,
+        #     # genus_list=classes.GENUS_NBM,
+        #     # species_group_list=classes.SPECIES_GROUP_NBM,
+        #     # order_list=classes.ORDER_NBM,
+        # ),
+        # NBMSetConfig(
+        #     name="NBM_scape",
+        #     description=_NBMSET24_DESCRIPTION,
+        #     citation=_NBMSET24_CITATION,
+        #     data_dir=f"{base_url}",
+        #     class_list=BIRD_NAMES_NBM,
+        #     # genus_list=classes.GENUS_NBM,
+        #     # species_group_list=classes.SPECIES_GROUP_NBM,
+        #     # order_list=classes.ORDER_NBM,
+        # ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_NBMSET24_DESCRIPTION + self.config.description,
+            features=self.config.features,
+            citation=self.config.citation + "\n" + _NBMSET24_DESCRIPTION,
+        )
+
+    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 = {"NBM": 12
+                       }
+
+        test_files = {"NBM": 1,
+                      }
+
+        # test_5s_files = {"NBM",}
+
+        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":
+            metadata.index = metadata["filepath"].str.split("/").apply(lambda x: x[-1])
+
+        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.get("ebird_code_multilabel", None),
+                "ebird_code_multilabel": row.get("ebird_code_multilabel", None),
+                "ebird_code_secondary": row.get("ebird_code_secondary", None),
+                "original_label":row["original_label"],
+                "label":row["label"],
+                # "french_label":row["french_label"],
+                # "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")]),
+            }

classes.py

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+BIRD_NAMES_NBM=['comsan', 'eurcoo', 'skylar', 'comcha', '', 'eursis', 'carcro1',
+       'comgre', 'whiwag', 'gretit1', 'norlap', 'gnwtea', 'meapip1',
+       'trepip', 'dunlin', 'sonthr1', 'graher1', 'eurcur', 'litowl1',
+       'comnig1', 'eursco1', 'eutkne1', 'houspa', 'lirplo', 'corplo',
+       'grscuc1', 'comqua1', 'eurgre1', 'blutit', 'eurrob1', 'eurbla',
+       'comchi1', 'shttre1', 'grcgre1', 'mallar3', 'yellow2', 'ortbun1',
+       'eupfly1', 'comsni', 'dunnoc1', 'winwre4', 'redwin', 'commoo3',
+       'comcra', 'euroys1', 'litbit1', 'blksco1', 'bkhgul', 'eaywag1',
+       'eurlin1', 'reebun', 'combuz1', 'spofly1', 'whimbr', 'bcnher',
+       'tawowl1', 'blackc1', 'fieldf', 'misthr1', 'eurmag1', 'eugplo',
+       'bkbplo', 'watrai1', 'litgre1', 'grnsan', 'comred1', 'brambl',
+       'rinouz1', 'brnowl', 'brant', 'gragoo', 'woosan', 'hawfin',
+       'firecr1', 'grebit1', 'goldcr1', 'bird1', 'blared1', 'purher1',
+       'cangoo', 'eucdov', 'rinphe1', 'eurjac', 'comswi', 'redcro',
+       'loeowl', 'norpin', 'eursta', 'pieavo1', 'tawpip1', 'woolar1',
+       'eurnig1', 'spocra1', 'spored', 'eargre', 'comcuc', 'cetwar1',
+       'litbus1', 'rook1', 'cowpig1', 'bkwsti', 'eurgol', 'medgul1',
+       'gadwal', 'eurwig', 'eueowl1', 'eurwoo', 'eurdot', 'lbbgul',
+       'yelgul1', 'larus1', 'olbpip', 'watpip1', 'eurjay1', 'eurser1',
+       'eurbul', 'eurnut2', 'litgul', 'hergul', 'grewhi1', 'whwsco3',
+       'eugori2', 'cretit2', 'wlwwar', 'comrav', 'grswoo', 'grywag']

descriptions.py

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+_NBM_DESCRIPTION="This is NBM DATASET"
+_NBM_CITATION="NBM Citation"