NBMSet24.py

"""NBMSet24: Nocturnal Bird Migration Dataset"""

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

from . import classes

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/ppeyret/NBMSet24/resolve/main/"


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("0.0.1"), name=name, **kwargs)
        
        print(f"name: \n{name}")
        print(f"class list : \n{class_list}")
        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"),
            "xenocanto_id": datasets.Value("int64"),
            # "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="NBMSet24",
            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 = {"NBMSet24": 12
                       }

        test_files = {"NBMSet24": 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", 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),
                "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"],
                "xenocanto_id": row["xenocanto_id"],
                # "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")]),
            }