# 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 from datasets.tasks import AutomaticSpeechRecognition _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"), task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="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 = []