|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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, |
|
) |
|
import pandas as pd |
|
|
|
|
|
class ARCTICHSConfig(BuilderConfig): |
|
def __init__( |
|
self, |
|
name, |
|
**kwargs, |
|
): |
|
super( |
|
ARCTICHSConfig, |
|
self, |
|
).__init__( |
|
name=name, |
|
**kwargs, |
|
) |
|
|
|
if self.name.endswith("_symmetric"): |
|
self.is_symmetric = True |
|
self.part = "_".join(self.name.split("_")[:-1]) |
|
else: |
|
self.is_symmetric = False |
|
self.part = self.name |
|
|
|
|
|
class ARCTICHSDataset(GeneratorBasedBuilder): |
|
DEFAULT_CONFIG_NAME = "cmu_us" |
|
|
|
BUILDER_CONFIGS = [ |
|
ARCTICHSConfig(name=name) |
|
for name in ( |
|
"cmu_non-us", |
|
"cmu_us", |
|
"l2", |
|
"cmu_non-us_symmetric", |
|
"cmu_us_symmetric", |
|
"l2_symmetric", |
|
) |
|
] |
|
|
|
def get_audio_archive_path( |
|
self, |
|
) -> Path: |
|
return Path("data") / self.config.part / "splits" / f"test.tar.gz" |
|
|
|
def get_metadata_paths( |
|
self, |
|
) -> Dict[str, Path]: |
|
if self.config.part == "cmu_non-us": |
|
return { |
|
speaker: Path("data") / self.config.part / "pairs" / f"{speaker}.csv" |
|
for speaker in ( |
|
"ahw", |
|
"aup", |
|
"awb", |
|
"axb", |
|
"fem", |
|
"gka", |
|
"jmk", |
|
"ksp", |
|
"rxr", |
|
"slp", |
|
) |
|
} |
|
elif self.config.part == "cmu_us": |
|
return { |
|
speaker: Path("data") / self.config.part / "pairs" / f"{speaker}.csv" |
|
for speaker in ( |
|
"aew", |
|
"bdl", |
|
"clb", |
|
"eey", |
|
"ljm", |
|
"lnh", |
|
"rms", |
|
"slt", |
|
) |
|
} |
|
elif self.config.part == "l2": |
|
return { |
|
speaker: Path("data") / self.config.part / "pairs" / f"{speaker}.csv" |
|
for speaker in ( |
|
"aba", |
|
"asi", |
|
"bwc", |
|
"ebvs", |
|
"erms", |
|
"hjk", |
|
"hkk", |
|
"hqtv", |
|
"lxc", |
|
"mbmps", |
|
"ncc", |
|
"njs", |
|
"pnv", |
|
"rrbi", |
|
"ska", |
|
"svbi", |
|
"thv", |
|
"tlv", |
|
"tni", |
|
"txhc", |
|
"ybaa", |
|
"ydck", |
|
"ykwk", |
|
"zhaa", |
|
) |
|
} |
|
|
|
def _info(self) -> DatasetInfo: |
|
return DatasetInfo( |
|
description="ARCTIC Human-Synthetic test dataset", |
|
features=Features( |
|
{ |
|
"audio": Audio(sampling_rate=16000), |
|
"label": Value("string"), |
|
} |
|
), |
|
supervised_keys=None, |
|
homepage="https://huggingface.co/datasets/realnetworks-kontxt/arctic-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={}", |
|
"}", |
|
) |
|
), |
|
) |
|
|
|
def _split_generators( |
|
self, |
|
download_manager: DownloadManager, |
|
) -> List[SplitGenerator]: |
|
archive_iterable = self.get_audio_archive_path() |
|
archive_iterable = download_manager.download(archive_iterable) |
|
archive_iterable = download_manager.iter_archive(archive_iterable) |
|
|
|
speaker_to_metadata_path = self.get_metadata_paths() |
|
speaker_to_metadata_path = download_manager.download(speaker_to_metadata_path) |
|
|
|
return [ |
|
SplitGenerator( |
|
name=Split.TEST, |
|
gen_kwargs={ |
|
"archive_iterable": archive_iterable, |
|
"speaker_to_metadata_path": speaker_to_metadata_path, |
|
}, |
|
), |
|
] |
|
|
|
def _generate_examples( |
|
self, |
|
archive_iterable: ArchiveIterable, |
|
speaker_to_metadata_path: Dict[str, Path], |
|
) -> Iterable[Tuple[int, Dict[str, Any]]]: |
|
speaker_to_symmetric = dict() |
|
for speaker, metadata_path in speaker_to_metadata_path.items(): |
|
df = pd.read_csv(metadata_path).astype( |
|
{ |
|
"name": str, |
|
"has_human_and_synthetic": bool, |
|
} |
|
) |
|
|
|
symmetric_names = df[df["has_human_and_synthetic"]]["name"].tolist() |
|
symmetric_names = set(symmetric_names) |
|
if len(symmetric_names) != 0: |
|
speaker_to_symmetric[speaker] = symmetric_names |
|
|
|
current_index = 0 |
|
for audio_path, audio_file in archive_iterable: |
|
path = Path(audio_path) |
|
|
|
name = path.name |
|
|
|
|
|
|
|
|
|
|
|
|
|
label = path.parent.name |
|
|
|
speaker = path.parent.parent.name |
|
|
|
if not self.config.is_symmetric or ( |
|
speaker in speaker_to_symmetric |
|
and name in speaker_to_symmetric[speaker] |
|
): |
|
audio = { |
|
"path": audio_path, |
|
"bytes": audio_file.read(), |
|
} |
|
|
|
yield current_index, { |
|
"audio": audio, |
|
"label": label, |
|
} |
|
|
|
current_index += 1 |
|
|