Datasets:

agkphysics
/

AudioSet

	# Copyright (C) 2024 Aaron Keesing
	#
	# Permission is hereby granted, free of charge, to any person obtaining
	# a copy of this software and associated documentation files (the
	# “Software”), to deal in the Software without restriction, including
	# without limitation the rights to use, copy, modify, merge, publish,
	# distribute, sublicense, and/or sell copies of the Software, and to
	# permit persons to whom the Software is furnished to do so, subject to
	# the following conditions:
	#
	# The above copyright notice and this permission notice shall be
	# included in all copies or substantial portions of the Software.
	#
	# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,
	# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
	# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
	# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
	# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
	# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
	# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

	from itertools import chain
	import json
	import os
	import tarfile

	import pandas as pd
	import datasets


	_CITATION = """\
	@inproceedings{45857,
	title = {Audio Set: An ontology and human-labeled dataset for audio events},
	author = {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter},
	year = {2017},
	booktitle = {Proc. IEEE ICASSP 2017},
	address = {New Orleans, LA}
	}
	"""

	_DESCRIPTION = """\
	This repository contains the balanced training set and evaluation set of the AudioSet
	data, described here: https://research.google.com/audioset/dataset/index.html. The
	YouTube videos were downloaded in March 2023, and so not all of the original audios are
	available.
	"""

	_HOMEPAGE = "https://research.google.com/audioset/dataset/index.html"

	_LICENSE = "cc-by-4.0"

	_URL_PREFIX = "https://huggingface.co/datasets/agkphysics/AudioSet/resolve/main"

	_N_BAL_TRAIN_TARS = 10
	_N_UNBAL_TRAIN_TARS = 870
	_N_EVAL_TARS = 9


	def _iter_tar(path):
	"""Iterate through the tar archive, but without skipping some files, which the HF
	DL does.
	"""
	with open(path, "rb") as fid:
	stream = tarfile.open(fileobj=fid, mode="r\|*")
	for tarinfo in stream:
	file_obj = stream.extractfile(tarinfo)
	yield tarinfo.name, file_obj
	stream.members = []
	del stream


	class AudioSetDataset(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="balanced",
	version=VERSION,
	description="Balanced training and balanced evaluation set.",
	),
	datasets.BuilderConfig(
	name="unbalanced",
	version=VERSION,
	description="Full unbalanced training set and balanced evaluation set.",
	),
	]
	DEFAULT_CONFIG_NAME = "balanced"

	def _info(self) -> datasets.DatasetInfo:
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	citation=_CITATION,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	features=datasets.Features(
	{
	"video_id": datasets.Value("string"),
	"audio": datasets.Audio(sampling_rate=None, mono=True, decode=True),
	"labels": datasets.Sequence(datasets.Value("string")),
	"human_labels": datasets.Sequence(datasets.Value("string")),
	}
	),
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager):
	if self.config.data_dir:
	prefix = self.config.data_dir
	else:
	prefix = _URL_PREFIX
	prefix = prefix + "/data"

	_LABEL_URLS = {
	"bal_train": (
	f"{prefix}/balanced_train_segments.csv"
	if self.config.name == "balanced"
	else f"{prefix}/unbalanced_train_segments.csv"
	),
	"eval": f"{prefix}/eval_segments.csv",
	"ontology": f"{prefix}/ontology.json",
	}
	_DATA_URLS = {
	"bal_train": (
	[f"{prefix}/bal_train0{i}.tar" for i in range(_N_BAL_TRAIN_TARS)]
	if self.config.name == "balanced"
	else [
	f"{prefix}/unbal_train{i:03d}.tar"
	for i in range(_N_UNBAL_TRAIN_TARS)
	]
	),
	"eval": [f"{prefix}/eval0{i}.tar" for i in range(_N_EVAL_TARS)],
	}

	tar_files = dl_manager.download(_DATA_URLS)
	label_files = dl_manager.download(_LABEL_URLS)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"labels": label_files["bal_train"],
	"ontology": label_files["ontology"],
	"audio_files": chain.from_iterable(
	_iter_tar(x) for x in tar_files["bal_train"]
	),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"labels": label_files["eval"],
	"ontology": label_files["ontology"],
	"audio_files": chain.from_iterable(
	_iter_tar(x) for x in tar_files["eval"]
	),
	},
	),
	]

	def _generate_examples(self, labels, ontology, audio_files):
	with open(ontology) as fid:
	ontology_data = json.load(fid)
	id_to_name = {x["id"]: x["name"] for x in ontology_data}

	labels_df = pd.read_csv(
	labels,
	skiprows=3,
	header=None,
	skipinitialspace=True,
	names=["vid_id", "start", "end", "labels"],
	index_col="vid_id",
	)

	for path, fid in audio_files:
	vid_id = os.path.splitext(os.path.basename(path))[0]
	label_ids = labels_df.loc[vid_id, "labels"].split(",")
	human_labels = [id_to_name[x] for x in label_ids]
	example = {
	"video_id": vid_id,
	"labels": label_ids,
	"human_labels": human_labels,
	"audio": {"path": path, "bytes": fid.read()},
	}
	yield vid_id, example