# coding=utf-8 """AudioCaps dataset.""" import os import gzip import shutil import joblib import pathlib import logging import datasets 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 logger = logging.getLogger(__name__) logger.addHandler(RichHandler()) logger.setLevel(logging.INFO) VERSION = "0.0.1" # Cache location 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 AudioCapsConfig(datasets.BuilderConfig): """BuilderConfig for AudioCaps.""" def __init__(self, features, **kwargs): super(AudioCapsConfig, self).__init__(version=datasets.Version(VERSION, ""), **kwargs) self.features = features class AudioCaps(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ AudioCapsConfig( features=datasets.Features( { "file": datasets.Value("string"), "audio": datasets.Audio(sampling_rate=None), "caption": datasets.Value("string"), } ), name="audiocaps", description="", ), ] DEFAULT_CONFIG_NAME = "audiocaps" def _info(self): return datasets.DatasetInfo( description="", features=self.config.features, supervised_keys=None, homepage="", citation="", task_templates=None, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" extensions = ['.wav'] # Development sets def _download_and_extract(filename): DEV_URL = f'https://huggingface.co/datasets/confit/audiocaps/resolve/main/data/{filename}' _dev_save_path = os.path.join( HF_DATASETS_CACHE, 'confit___audiocaps/audiocaps', VERSION ) download_file( source=DEV_URL, dest=os.path.join(_dev_save_path, filename), unpack=True, dest_unpack=os.path.join(_dev_save_path, 'extracted', 'train'), ) joblib.Parallel(n_jobs=8, verbose=10)( joblib.delayed(_download_and_extract)( filename=_filename ) for _filename in tqdm([f'train{i}.zip' for i in range(1, 12+1)]) ) train_audio_paths = [] for _filename in [f'train{i}.zip' for i in range(1, 12+1)]: DEV_URL = f'https://huggingface.co/datasets/confit/audiocaps/resolve/main/data/{_filename}' _dev_save_path = os.path.join( HF_DATASETS_CACHE, 'confit___audiocaps/audiocaps', VERSION ) train_archive_path = os.path.join(_dev_save_path, 'extracted', 'train') _, _walker = fast_scandir(train_archive_path, extensions, recursive=True) train_audio_paths.extend(_walker) # Validation set VAL_URL = 'https://huggingface.co/datasets/confit/audiocaps/resolve/main/data/val.zip' _val_save_path = os.path.join( HF_DATASETS_CACHE, 'confit___audiocaps/audiocaps', VERSION ) _filename = 'val.zip' download_file( source=VAL_URL, dest=os.path.join(_val_save_path, _filename), unpack=True, dest_unpack=os.path.join(_val_save_path, 'extracted', 'validation'), ) validation_archive_path = os.path.join(_val_save_path, 'extracted', 'validation') _, validation_audio_paths = fast_scandir(validation_archive_path, extensions, recursive=True) # Evaluation set EVAL_URL = 'https://huggingface.co/datasets/confit/audiocaps/resolve/main/data/test.zip' _eval_save_path = os.path.join( HF_DATASETS_CACHE, 'confit___audiocaps/audiocaps', VERSION ) _filename = 'test.zip' download_file( source=EVAL_URL, dest=os.path.join(_eval_save_path, _filename), unpack=True, dest_unpack=os.path.join(_eval_save_path, 'extracted', 'test'), ) test_archive_path = os.path.join(_eval_save_path, 'extracted', 'test') _, test_audio_paths = fast_scandir(test_archive_path, extensions, recursive=True) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"audio_paths": train_audio_paths, "split": "train"} ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"audio_paths": validation_audio_paths, "split": "validation"} ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"audio_paths": test_audio_paths, "split": "test"} ), ] def _generate_examples(self, audio_paths, split=None): if split == 'train': metadata_df = pd.read_csv('https://huggingface.co/datasets/confit/audiocaps/raw/main/metadata/train.csv') elif split == 'validation': metadata_df = pd.read_csv('https://huggingface.co/datasets/confit/audiocaps/raw/main/metadata/val.csv') elif split == 'test': metadata_df = pd.read_csv('https://huggingface.co/datasets/confit/audiocaps/raw/main/metadata/test.csv') fileid2caption = {} for idx, row in metadata_df.iterrows(): fileid2caption[f"{row['audiocap_id']}.wav"] = row['caption'] # this filename doesn't have suffix for guid, audio_path in enumerate(audio_paths): fileid = Path(audio_path).name caption = fileid2caption.get(fileid) yield guid, { "id": str(guid), "file": audio_path, "audio": audio_path, "caption": caption, } 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) if os.path.exists(dest_unpack): logger.info(f"{dest_unpack} already exists. Skipping extraction") else: 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)