Datasets:
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# Copyright 2024 RealNetworks
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
from typing import (
Any,
Dict,
Iterable,
List,
Tuple,
)
from datasets import (
Audio,
BuilderConfig,
DatasetInfo,
Features,
GeneratorBasedBuilder,
Split,
SplitGenerator,
Value,
)
from datasets.download.download_manager import (
ArchiveIterable,
DownloadManager,
)
class FLEURSHSConfig(BuilderConfig):
def __init__(
self,
name,
**kwargs,
):
super(
FLEURSHSConfig,
self,
).__init__(
name=name,
**kwargs,
)
class FLEURSHSDataset(GeneratorBasedBuilder):
DEFAULT_CONFIG_NAME = "en_us"
BUILDER_CONFIGS = [
FLEURSHSConfig(name=name)
for name in (
"de_de",
"en_us",
"es_419",
"fr_fr",
"it_it",
"nl_nl",
"pl_pl",
"sv_se",
)
]
def get_audio_archive_path(
self,
split: str,
) -> Path:
return Path("data") / self.config.name / "splits" / f"{split}.tar.gz"
def _info(self) -> DatasetInfo:
return DatasetInfo(
description="FLEURS Human-Synthetic classification dataset",
features=Features(
{
"audio": Audio(sampling_rate=16000),
"label": Value("string"),
}
),
supervised_keys=None,
homepage="https://huggingface.co/datasets/realnetworks-kontxt/fleurs-hs",
license="CC BY 4.0",
citation="\n".join(
(
"@inproceedings{dropuljic-ssdww2v2ivls",
"author={Dropuljić, Branimir and Šuflaj, Miljenko and Jertec, Andrej and Obadić, Leo}",
"booktitle={2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)}",
"title={Synthetic speech detection with Wav2Vec 2.0 in various language settings}",
"year={2024}",
"volume={}",
"number={}",
"pages={1-5}",
"keywords={Synthetic speech detection;text-to-speech;wav2vec 2.0;spoofing attack;multilingualism}",
"doi={}", # TODO: Add DOI once known
"}",
)
),
)
def _split_generators(
self,
download_manager: DownloadManager,
) -> List[SplitGenerator]:
archive_iterables = {
split: str(self.get_audio_archive_path(split=split))
for split in (
"train",
"dev",
"test",
)
}
archive_iterables = download_manager.download(archive_iterables)
archive_iterables = {
split: download_manager.iter_archive(path)
for split, path in archive_iterables.items()
}
return [
SplitGenerator(
name=Split.TRAIN,
gen_kwargs={
"archive_iterable": archive_iterables["train"],
},
),
SplitGenerator(
name=Split.VALIDATION,
gen_kwargs={
"archive_iterable": archive_iterables["dev"],
},
),
SplitGenerator(
name=Split.TEST,
gen_kwargs={
"archive_iterable": archive_iterables["test"],
},
),
]
def _generate_examples(
self,
archive_iterable: ArchiveIterable,
) -> Iterable[Tuple[int, Dict[str, Any]]]:
current_index = 0
for audio_path, audio_file in archive_iterable:
audio = {
"path": audio_path,
"bytes": audio_file.read(),
}
# Samples are located in one of 2 folders:
# - 'human'
# - 'synthetic`
#
# Therefore the label is the name of their parent folder
label = Path(audio_path).parent.name
yield current_index, {
"audio": audio,
"label": label,
}
current_index += 1
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