audioset.py

# coding=utf-8

"""AudioSet sound event classification dataset."""


import os
import json
import gzip
import shutil
import pathlib
import logging
import textwrap
import datasets
import itertools
import typing as tp
import pandas as pd
import urllib.request
from pathlib import Path
from copy import deepcopy
from tqdm.auto import tqdm
from rich.logging import RichHandler
from huggingface_hub import hf_hub_download

from ._audioset import ID2LABEL

logger = logging.getLogger(__name__)
logger.addHandler(RichHandler())
logger.setLevel(logging.INFO)

SAMPLE_RATE = 32_000

_HOMEPAGE = "https://huggingface.co/datasets/confit/audioset"

_BALANCED_TRAIN_FILENAME = 'balanced/balanced_train_segments.zip'
_EVAL_FILENAME = 'eval/eval_segments.zip'

# ID2LABEL = json.load(
#     open(hf_hub_download("huggingface/label-files", "audioset-id2label.json", repo_type="dataset"), "r")
# )
LABEL2ID = {v:k for k, v in ID2LABEL.items()}
CLASSES = list(ID2LABEL.values())

# Cache location
VERSION = "0.0.1"
DEFAULT_XDG_CACHE_HOME = "~/.cache"
XDG_CACHE_HOME = os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
DEFAULT_HF_CACHE_HOME = os.path.join(XDG_CACHE_HOME, "huggingface")
HF_CACHE_HOME = os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))
DEFAULT_HF_DATASETS_CACHE = os.path.join(HF_CACHE_HOME, "datasets")
HF_DATASETS_CACHE = Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))


class AudioSetConfig(datasets.BuilderConfig):
    """BuilderConfig for AudioSet."""
    
    def __init__(self, features, **kwargs):
        super(AudioSetConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
        self.features = features


class AudioSet(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        AudioSetConfig(
            features=datasets.Features(
                {
                    "file": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
                    "sound": datasets.Sequence(datasets.Value("string")), 
                    "label": datasets.Sequence(datasets.features.ClassLabel(names=CLASSES)), 
                }
            ),
            name="balanced", 
            description="",
        ), 
    ] + [
        AudioSetConfig(
            features=datasets.Features(
                {
                    "file": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
                    "sound": datasets.Sequence(datasets.Value("string")), 
                    "label": datasets.Sequence(datasets.features.ClassLabel(names=CLASSES)), 
                }
            ),
            name=f"unbalanced-part{i:02d}", 
            description="",
        ) for i in range(40+1)
    ]

    DEFAULT_CONFIG_NAME = "balanced"

    def _info(self):
        return datasets.DatasetInfo(
            description="",
            features=self.config.features,
            supervised_keys=None,
            homepage="",
            citation="",
            task_templates=None,
        )

    def _preprocess_metadata_csv(self, csv_file):
        df = pd.read_csv(csv_file, skiprows=2, sep=', ', engine='python')
        df.rename(columns={'positive_labels': 'ids'}, inplace=True)
        df['ids'] = [label.strip('\"').split(',') for label in df['ids']]
        df['filename'] = (
            'Y' + df['# YTID'] + '.wav'
        )
        return df[['filename', 'ids']]

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        if self.config.name == 'balanced':
            train_archive_path = dl_manager.download_and_extract(
                f'https://huggingface.co/datasets/confit/audioset/resolve/main/{_BALANCED_TRAIN_FILENAME}'
            )
            logger.info(f"`{_BALANCED_TRAIN_FILENAME}` is downloaded to {train_archive_path}")
            
        elif str(self.config.name).startswith('unbalanced-part'):
            partxx = str(self.config.name).split('-')[-1]
            for zip_type in ['zip', 'z01', 'z02']:
                _UNBALANCED_TRAIN_FILENAME = f'unbalanced_train_segments_{partxx}_partial.{zip_type}'
                zip_file_url = f'https://huggingface.co/datasets/confit/audioset/resolve/main/unbalanced/{_UNBALANCED_TRAIN_FILENAME}'
                _save_path = os.path.join(
                    HF_DATASETS_CACHE, 'confit___audioset/unbalanced', VERSION, _UNBALANCED_TRAIN_FILENAME
                )
                download_file(zip_file_url, _save_path)
                logger.info(f"`{_UNBALANCED_TRAIN_FILENAME}` is downloaded to {_save_path}")

            main_zip_filename = f'unbalanced_train_segments_{partxx}_partial.zip'
            concat_zip_filename = f'unbalanced_train_segments_{partxx}_full.zip'
            _input_file = os.path.join(HF_DATASETS_CACHE, 'confit___audioset/unbalanced', VERSION, main_zip_filename)
            _output_file = os.path.join(HF_DATASETS_CACHE, 'confit___audioset/unbalanced', VERSION, concat_zip_filename)
            
            if not os.path.exists(_output_file):
                logger.info(f"Reassemble {_output_file} file")
                os.system(f"zip -q -F {_input_file} --out {_output_file}")
                part_zip_files = os.path.join(
                    HF_DATASETS_CACHE, 'confit___audioset/unbalanced', VERSION, f'unbalanced_train_segments_{partxx}_partial.*'
                )
                os.system(f"rm -rf {part_zip_files}")
            train_archive_path = dl_manager.extract(_output_file)
            logger.info(f"`{concat_zip_filename}` is downloaded to {train_archive_path}")
            
        
        zip_file_url = f'https://huggingface.co/datasets/confit/audioset/resolve/main/{_EVAL_FILENAME}'
        _save_path = os.path.join(
            HF_DATASETS_CACHE, 'confit___audioset/eval', VERSION
        )
        download_file(zip_file_url, os.path.join(_save_path, _EVAL_FILENAME))
        test_archive_path = dl_manager.extract(os.path.join(_save_path, _EVAL_FILENAME))
        logger.info(f"`eval_segments.zip` is extracted to {test_archive_path}")
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"archive_path": train_archive_path, "split": "train"}
            ), 
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"archive_path": test_archive_path, "split": "test"}
            ), 
        ]

    def _generate_examples(self, archive_path, split=None):
        extensions = ['.wav']

        if split == 'train':
            if self.config.name == 'balanced':
                train_metadata_csv = f"{_HOMEPAGE}/resolve/main/metadata/balanced_train_segments.csv"
            elif str(self.config.name).startswith('unbalanced-part'):
                train_metadata_csv = f"{_HOMEPAGE}/resolve/main/metadata/unbalanced_train_segments.csv"
            metadata_df = self._preprocess_metadata_csv(train_metadata_csv) # ['filename', 'ids']
        elif split == 'test':
            test_metadata_csv = f"{_HOMEPAGE}/resolve/main/metadata/eval_segments.csv"
            metadata_df = self._preprocess_metadata_csv(test_metadata_csv) # ['filename', 'ids']
        
        class_labels_indices_df = pd.read_csv(
            f"{_HOMEPAGE}/resolve/main/metadata/class_labels_indices.csv"
        ) # ['index', 'mid', 'display_name']
        mid2label = {
            row['mid']:row['display_name'] for idx, row in class_labels_indices_df.iterrows()
        }

        def default_find_classes(audio_path):
            fileid = Path(audio_path).name
            ids = metadata_df.query(f'filename=="{fileid}"')['ids'].values.tolist()
            ids = [
                mid2label.get(mid, None) for mid in flatten(ids)
            ]
            return ids

        _, _walker = fast_scandir(archive_path, extensions, recursive=True)

        for guid, audio_path in enumerate(_walker):
            if Path(audio_path).name == 'YmW3S0u8bj58.wav':
                continue
            yield guid, {
                "id": str(guid),
                "file": audio_path, 
                "audio": audio_path, 
                "sound": default_find_classes(audio_path), 
                "label": default_find_classes(audio_path), 
            }


def flatten(list2d):
    return list(itertools.chain.from_iterable(list2d))


def fast_scandir(path: str, exts: tp.List[str], recursive: bool = False):
    # Scan files recursively faster than glob
    # From github.com/drscotthawley/aeiou/blob/main/aeiou/core.py
    subfolders, files = [], []

    try:  # hope to avoid 'permission denied' by this try
        for f in os.scandir(path):
            try:  # 'hope to avoid too many levels of symbolic links' error
                if f.is_dir():
                    subfolders.append(f.path)
                elif f.is_file():
                    if os.path.splitext(f.name)[1].lower() in exts:
                        files.append(f.path)
            except Exception:
                pass
    except Exception:
        pass

    if recursive:
        for path in list(subfolders):
            sf, f = fast_scandir(path, exts, recursive=recursive)
            subfolders.extend(sf)
            files.extend(f)  # type: ignore

    return subfolders, files


def download_file(
    source,
    dest,
    unpack=False,
    dest_unpack=None,
    replace_existing=False,
    write_permissions=False,
):
    """Downloads the file from the given source and saves it in the given
    destination path.
     Arguments
    ---------
    source : path or url
        Path of the source file. If the source is an URL, it downloads it from
        the web.
    dest : path
        Destination path.
    unpack : bool
        If True, it unpacks the data in the dest folder.
    dest_unpack: path
        Path where to store the unpacked dataset
    replace_existing : bool
        If True, replaces the existing files.
    write_permissions: bool
        When set to True, all the files in the dest_unpack directory will be granted write permissions.
        This option is active only when unpack=True.
    """
    class DownloadProgressBar(tqdm):
        """DownloadProgressBar class."""

        def update_to(self, b=1, bsize=1, tsize=None):
            """Needed to support multigpu training."""
            if tsize is not None:
                self.total = tsize
            self.update(b * bsize - self.n)

    # Create the destination directory if it doesn't exist
    dest_dir = pathlib.Path(dest).resolve().parent
    dest_dir.mkdir(parents=True, exist_ok=True)
    if "http" not in source:
        shutil.copyfile(source, dest)

    elif not os.path.isfile(dest) or (
        os.path.isfile(dest) and replace_existing
    ):
        logger.info(f"Downloading {source} to {dest}")
        with DownloadProgressBar(
            unit="B",
            unit_scale=True,
            miniters=1,
            desc=source.split("/")[-1],
        ) as t:
            urllib.request.urlretrieve(
                source, filename=dest, reporthook=t.update_to
            )
    else:
        logger.info(f"{dest} exists. Skipping download")

    # Unpack if necessary
    if unpack:
        if dest_unpack is None:
            dest_unpack = os.path.dirname(dest)
        logger.info(f"Extracting {dest} to {dest_unpack}")
        # shutil unpack_archive does not work with tar.gz files
        if (
            source.endswith(".tar.gz")
            or source.endswith(".tgz")
            or source.endswith(".gz")
        ):
            out = dest.replace(".gz", "")
            with gzip.open(dest, "rb") as f_in:
                with open(out, "wb") as f_out:
                    shutil.copyfileobj(f_in, f_out)
        else:
            shutil.unpack_archive(dest, dest_unpack)
        if write_permissions:
            set_writing_permissions(dest_unpack)


def set_writing_permissions(folder_path):
    """
    This function sets user writing permissions to all the files in the given folder.
    Arguments
    ---------
    folder_path : folder
        Folder whose files will be granted write permissions.
    """
    for root, dirs, files in os.walk(folder_path):
        for file_name in files:
            file_path = os.path.join(root, file_name)
            # Set writing permissions (mode 0o666) to the file
            os.chmod(file_path, 0o666)