Datasets:
Tasks:
Audio Classification
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
voice-anti-spoofing
License:
File size: 5,931 Bytes
b2d92b1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
import os
import datasets
_CITATION = """\
@InProceedings{Todisco2019,
Title = {{ASV}spoof 2019: {F}uture {H}orizons in {S}poofed and {F}ake {A}udio {D}etection},
Author = {Todisco, Massimiliano and
Wang, Xin and
Sahidullah, Md and
Delgado, H ́ector and
Nautsch, Andreas and
Yamagishi, Junichi and
Evans, Nicholas and
Kinnunen, Tomi and
Lee, Kong Aik},
booktitle = {Proc. of Interspeech 2019},
Year = {2019}
}
"""
_DESCRIPTION = """\
This is a database used for the Third Automatic Speaker Verification Spoofing
and Countermeasuers Challenge, for short, ASVspoof 2019 (http://www.asvspoof.org)
organized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor
Delgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman,
and Andreas Nautsch in 2019.
"""
_HOMEPAGE = "https://datashare.ed.ac.uk/handle/10283/3336"
_LICENSE = "http://opendatacommons.org/licenses/by/1.0/"
_URLS = {
"LA": "https://datashare.ed.ac.uk/bitstream/handle/10283/3336/LA.zip",
"PA": "https://datashare.ed.ac.uk/bitstream/handle/10283/3336/PA.zip",
}
class ASVSpoof2019(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="LA", version=VERSION, description="Logical access (LA)"),
datasets.BuilderConfig(name="PA", version=VERSION, description="Physical access (PA)"),
]
DEFAULT_CONFIG_NAME = "LA"
def _info(self):
if self.config.name == "LA":
features = datasets.Features(
{
"speaker_id": datasets.Value("string"),
"audio_file_name": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"system_id": datasets.Value("string"),
"key": datasets.ClassLabel(names=["bonafide", "spoof"]),
}
)
else:
features = datasets.Features(
{
"speaker_id": datasets.Value("string"),
"audio_file_name": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"environment_id": datasets.Value("string"),
"attack_id": datasets.Value("string"),
"key": datasets.ClassLabel(names=["bonafide", "spoof"]),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=("audio", "key"),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"metadata_filepath": os.path.join(
data_dir,
self.config.name,
f"ASVspoof2019_{self.config.name}_cm_protocols",
f"ASVspoof2019.{self.config.name}.cm.train.trn.txt",
),
"audios_dir": os.path.join(
data_dir, self.config.name, f"ASVspoof2019_{self.config.name}_train", "flac"
),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"metadata_filepath": os.path.join(
data_dir,
self.config.name,
f"ASVspoof2019_{self.config.name}_cm_protocols",
f"ASVspoof2019.{self.config.name}.cm.dev.trl.txt",
),
"audios_dir": os.path.join(
data_dir, self.config.name, f"ASVspoof2019_{self.config.name}_dev", "flac"
),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"metadata_filepath": os.path.join(
data_dir,
self.config.name,
f"ASVspoof2019_{self.config.name}_cm_protocols",
f"ASVspoof2019.{self.config.name}.cm.eval.trl.txt",
),
"audios_dir": os.path.join(
data_dir, self.config.name, f"ASVspoof2019_{self.config.name}_eval", "flac"
),
},
),
]
def _generate_examples(self, metadata_filepath, audios_dir):
with open(metadata_filepath) as f:
for i, line in enumerate(f.readlines()):
if self.config.name == "LA":
speaker_id, audio_file_name, _, system_id, key = line.strip().split()
result = {
"speaker_id": speaker_id,
"audio_file_name": audio_file_name,
"system_id": system_id,
"key": key,
}
elif self.config.name == "PA":
speaker_id, audio_file_name, environment_id, attack_id, key = line.strip().split()
result = {
"speaker_id": speaker_id,
"audio_file_name": audio_file_name,
"environment_id": environment_id,
"attack_id": attack_id,
"key": key,
}
result["audio"] = os.path.join(audios_dir, audio_file_name + ".flac")
yield i, result
|