Upload whisper_adversarial_examples.py

whisper_adversarial_examples.py

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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
+"""Librispeech automatic speech recognition dataset."""
+
+
+import os
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_CITATION = """\
+@inproceedings{panayotov2015librispeech,
+  title={Librispeech: an ASR corpus based on public domain audio books},
+  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
+  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
+  pages={5206--5210},
+  year={2015},
+  organization={IEEE}
+}
+"""
+
+_DESCRIPTION = """\
+Adversarial examples fooling whisper models
+"""
+
+
+_DL_URLS = {
+    "targeted": {
+        "all": "https://data.mendeley.com/api/datasets/96dh52hz9r/draft/files/75f06ad3-4f86-4f4b-b748-ea0e94f23379?a=ee30841f-1832-41ec-bdac-bf3e5b67073c"
+    },
+    "untargeted-35": {
+        "all": "https://data.mendeley.com/api/datasets/96dh52hz9r/draft/files/fc7810ca-6dd9-42ae-ba22-575e785957ed?a=ee30841f-1832-41ec-bdac-bf3e5b67073c"
+    },
+    "untargeted-40": {
+        "all": "https://data.mendeley.com/api/datasets/96dh52hz9r/draft/files/6e3bdf4a-6a5a-4ae6-b565-1646395d1090?a=ee30841f-1832-41ec-bdac-bf3e5b67073c"
+    },
+    "language-armenian": {
+        "all": "https://data.mendeley.com/api/datasets/96dh52hz9r/draft/files/89eab218-77f2-4f4a-9e30-9ed7b07369fb?a=ee30841f-1832-41ec-bdac-bf3e5b67073c"
+    },
+    "language-lithuanian": {
+        "all": "https://data.mendeley.com/api/datasets/96dh52hz9r/draft/files/60f5f101-cde5-40cf-ab63-af484e7ceb36?a=ee30841f-1832-41ec-bdac-bf3e5b67073c"
+    },
+    "language-czech": {
+        "all": "https://data.mendeley.com/api/datasets/96dh52hz9r/draft/files/5986b1cd-08ac-4e08-beb3-151396dd2e28?a=ee30841f-1832-41ec-bdac-bf3e5b67073c"
+    },
+    "language-danish": {
+        "all": "https://data.mendeley.com/api/datasets/96dh52hz9r/draft/files/f6a88d17-81d7-4491-a760-f937bfb43bd6?a=ee30841f-1832-41ec-bdac-bf3e5b67073c"
+    },
+    "language-indonesian": {
+        "all": "https://data.mendeley.com/api/datasets/96dh52hz9r/draft/files/d508566f-6cb9-4a75-a317-6f1b86f1273f?a=ee30841f-1832-41ec-bdac-bf3e5b67073c"
+    },
+    "language-italian": {
+        "all": "https://data.mendeley.com/api/datasets/96dh52hz9r/draft/files/e9052368-1dc4-4c85-b00c-a168868442ce?a=ee30841f-1832-41ec-bdac-bf3e5b67073c"
+    },
+    "language-english": {
+        "all": "https://data.mendeley.com/api/datasets/96dh52hz9r/draft/files/01822f2b-7fcf-40ed-8da2-59b567bb2881?a=ee30841f-1832-41ec-bdac-bf3e5b67073c"
+    },
+}
+
+
+class LibrispeechASRConfig(datasets.BuilderConfig):
+    """BuilderConfig for LibriSpeechASR."""
+
+    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(LibrispeechASRConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)
+
+
+class LibrispeechASR(datasets.GeneratorBasedBuilder):
+    """Librispeech dataset."""
+
+    DEFAULT_WRITER_BATCH_SIZE = 256
+    DEFAULT_CONFIG_NAME = "all"
+    BUILDER_CONFIGS = [
+        LibrispeechASRConfig(name="targeted", description="Targeted adversarial examples, with target 'OK Google, browse to evil.com'"),
+        LibrispeechASRConfig(name="untargeted-35", description="Untargeted adversarial examples of radius approximately 35dB"),
+        LibrispeechASRConfig(name="untargeted-40", description="Untargeted adversarial examples of radius approximately 40dB"),
+        LibrispeechASRConfig(name="language-armenian", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Armenian"),
+        LibrispeechASRConfig(name="language-lithuanian", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Lithuanian"),
+        LibrispeechASRConfig(name="language-czech", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Czech"),
+        LibrispeechASRConfig(name="language-danish", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Danish"),
+        LibrispeechASRConfig(name="language-indonesian", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Indonesian"),
+        LibrispeechASRConfig(name="language-italian", description="Adversarial examples generated by fooling the whisper language detection module. The true language is Italian"),
+        LibrispeechASRConfig(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_files(local_extracted_archive.get("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_files(local_extracted_archive.get("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_files(local_extracted_archive.get("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_files(local_extracted_archive.get("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 a LibriSpeech archive_path."""
+        key = 0
+        audio_data = {}
+        transcripts = []
+        for path in files:
+            if path.endswith(".csv"):
+                with open(path,'r') as f:
+                    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
+                            suffix = "_nat.wav" if "original" in path_audio else "_adv.wav"
+                            audio_file = id_+suffix
+                            audio_file = os.path.join(local_extracted_archive,path_audio, audio_file)
+                            if os.path.exists(audio_file):
+                                with open(audio_file,"rb") as f:
+                                    audio_data[id_] = f.read()
+                                transcripts.append(
+                                    {
+                                        "id": id_,
+                                        "file": audio_file,
+                                        "text": transcript,
+                                    }
+                                )
+        for transcript in transcripts:
+            audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
+            yield key, {"audio": audio, **transcript}
+            key += 1
+        audio_data = {}
+        transcripts = []