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# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""Adversarial examples against Whisper"""
import os
import datasets
_DESCRIPTION = """\
Adversarial examples fooling whisper models
"""
_DL_URLS = {
"targeted": {
"all": "https://data.mendeley.com/public-files/datasets/96dh52hz9r/files/10432840-4a07-49fa-8320-0af2a8288435/file_downloaded"
},
"untargeted-35": {
"all": "https://data.mendeley.com/public-files/datasets/96dh52hz9r/files/516787a5-4832-4432-9138-9f01cccc4875/file_downloaded"
},
"untargeted-40": {
"all": "https://data.mendeley.com/public-files/datasets/96dh52hz9r/files/ed7127c6-9769-4db5-ab5a-98e9ce15a6ae/file_downloaded"
},
"language-armenian": {
"all": "https://data.mendeley.com/public-files/datasets/96dh52hz9r/files/57a8301c-a3de-4f34-a321-6cbdec5b7d55/file_downloaded"
},
"language-lithuanian": {
"all": "https://data.mendeley.com/public-files/datasets/96dh52hz9r/files/b8dc1e63-d308-45e8-b16c-98ca4ac3e939/file_downloaded"
},
"language-czech": {
"all": "https://data.mendeley.com/public-files/datasets/96dh52hz9r/files/8e5246e6-dfad-4d4c-aa1e-091cf24d975c/file_downloaded"
},
"language-danish": {
"all": "https://data.mendeley.com/public-files/datasets/96dh52hz9r/files/15a27ffe-8ad3-4a92-adfc-ac1c6a7b230b/file_downloaded"
},
"language-indonesian": {
"all": "https://data.mendeley.com/public-files/datasets/96dh52hz9r/files/ad3366b1-21a4-4ad4-9755-8a1d3775db62/file_downloaded"
},
"language-italian": {
"all": "https://data.mendeley.com/public-files/datasets/96dh52hz9r/files/1729f188-ae9f-4a29-a8da-9597c1f2d0cc/file_downloaded"
},
"language-english": {
"all": "https://data.mendeley.com/public-files/datasets/96dh52hz9r/files/7d09cf90-af7d-4d33-914a-3002ea956a53/file_downloaded"
},
}
class AdvWhisperASRConfig(datasets.BuilderConfig):
"""BuilderConfig for AdvWhisperASR."""
def __init__(self, **kwargs):
"""
Args:
data_dir: `string`, the path to the folder containing the files in the
downloaded .tar
citation: `string`, citation for the data set
url: `string`, url for information about the data set
**kwargs: keyword arguments forwarded to super.
"""
super(AdvWhisperASRConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)
class AdvWhisperASR(datasets.GeneratorBasedBuilder):
"""whisper_adversarial_examples dataset."""
DEFAULT_WRITER_BATCH_SIZE = 256
DEFAULT_CONFIG_NAME = "all"
BUILDER_CONFIGS = [
AdvWhisperASRConfig(name="targeted", description="Targeted adversarial examples, with target 'OK Google, browse to evil.com'"),
AdvWhisperASRConfig(name="untargeted-35", description="Untargeted adversarial examples of radius approximately 35dB"),
AdvWhisperASRConfig(name="untargeted-40", description="Untargeted adversarial examples of radius approximately 40dB"),
AdvWhisperASRConfig(name="language-armenian", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Armenian"),
AdvWhisperASRConfig(name="language-lithuanian", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Lithuanian"),
AdvWhisperASRConfig(name="language-czech", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Czech"),
AdvWhisperASRConfig(name="language-danish", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Danish"),
AdvWhisperASRConfig(name="language-indonesian", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Indonesian"),
AdvWhisperASRConfig(name="language-italian", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Italian"),
AdvWhisperASRConfig(name="language-english", description="Adversarial examples generated by fooling the whisper language detection module. The true language is English")
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"text": datasets.Value("string"),
"id": datasets.Value("string"),
}
),
supervised_keys=("file", "text"),
)
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
# (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
models = [
'whisper-tiny',
'whisper-tiny.en',
'whisper-base',
'whisper-base.en',
'whisper-small',
'whisper-small.en',
'whisper-medium',
'whisper-medium.en',
'whisper-large',
]
seeds = {
"targeted":2000,
"untargeted-35": 235,
"untargeted-40":240,
"language-armenian":1030,
"language-lithuanian":1030,
"language-czech":1030,
"language-danish":1030,
"language-indonesian":1030,
"language-italian":1030,
"language-english":1030
}
folders = {
"targeted":"cw",
"untargeted-35": "pgd-35",
"untargeted-40":"pgd-40",
"language-armenian":"hy-AM",
"language-lithuanian":"lt",
"language-czech":"cs",
"language-danish":"da",
"language-indonesian":"id",
"language-italian":"it",
"language-english":"en"
}
targets = [("english","en"), ("tagalog","tl"), ("serbian","sr")]
if "language-" in self.config.name:
lang = self.config.name.split("language-")[-1]
splits = [
datasets.SplitGenerator(
name=lang+"."+target[0],
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("all"),
"files": dl_manager.iter_archive(archive_path["all"]),
"path_audio": os.path.join(folders[self.config.name]+"-"+target[1],"whisper-medium",str(seeds[self.config.name]),"save")
},
) for target in targets
] + [
datasets.SplitGenerator(
name="original",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("all"),
"files": dl_manager.iter_archive(archive_path["all"]),
"path_audio": folders[self.config.name]+"-original"
},
)
]
else:
splits = [
datasets.SplitGenerator(
name=model.replace("-","."),
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("all"),
"files": dl_manager.iter_archive(archive_path["all"]),
"path_audio": os.path.join(folders[self.config.name],model,str(seeds[self.config.name]),"save")
},
) for model in models
] + [
datasets.SplitGenerator(
name="original",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("all"),
"files": dl_manager.iter_archive(archive_path["all"]),
"path_audio": os.path.join(folders[self.config.name],"original")
},
)
]
return splits
def _generate_examples(self, files, local_extracted_archive,path_audio):
"""Generate examples from an extracted path."""
key = 0
suffix = "_nat.wav" if "original" in path_audio else "_adv.wav"
audio_data = {}
transcripts = []
for t in files:
path, f = t
if path.endswith(".wav"):
if path_audio in path and path.endswith(suffix):
id_ = path.split("/")[-1][: -len(suffix)]
audio_data[id_] = f.read()
elif path.endswith(".csv"):
for line in f:
if line:
line = (line.decode("utf-8") if isinstance(line,bytes) else line)
line=line.strip().split(",")
id_ = line[0]
transcript=line[-1]
transcript = transcript[:-1] if transcript[-1]=='\n' else transcript
audio_file = id_+suffix
audio_file = (
os.path.join(local_extracted_archive,path_audio, audio_file)
if local_extracted_archive else audio_file
)
transcripts.append(
{
"id": id_,
"file": audio_file,
"text": transcript,
}
)
for transcript in transcripts:
if transcript["id"] in audio_data:
audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
yield key, {"audio": audio, **transcript}
key += 1
audio_data = {}
transcripts = [] |