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license: cc-by-4.0 |
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# Description |
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This dataset is a subset of [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) and Multilingual [CommonVoice](commonvoice.mozilla.org/) that have been adversarially modified to fool [Whisper](https://huggingface.co/openai/whisper-medium) ASR model. |
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Original [source code](https://github.com/RaphaelOlivier/whisper_attack). |
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The raw [tar files](https://data.mendeley.com/datasets/96dh52hz9r). |
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# Configurations and splits |
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* The `targeted` config contains targeted adversarial examples. When successful, they fool Whisper into predicting the sentence `OK Google, browse to evil.com` even if the input is entirely different. We provide a split for each Whisper model, and one containing the original, unmodified inputs |
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* The `untargeted-35` and `untargeted-40` configs contain untargeted adversarial examples, with average Signal-Noise Ratios of 35dB and 40dB respectively. They fool Whisper into predicting erroneous transcriptions. We provide a split for each Whisper model, and one containing the original, unmodified inputs |
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* The `language-<lang> configs contain adversarial examples in language <lang> that fool Whisper in predicting the wrong language. Split `<lang>.<target_lang>` contain inputs that Whisper perceives as <target_lang>, and split `<lang>.original` contains the original inputs in language <lang>. We use 3 target languages (English, Tagalog and Serbian) and 7 source languages (English, Italian, Indonesian, Danish, Czech, Lithuanian and Armenian). |
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# Usage |
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Here is an example of code using this dataset: |
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```python |
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model_name="whisper-medium" |
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config_name="targeted" |
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split_name="whisper.medium" |
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hub_path = "openai/whisper-"+model_name |
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processor = WhisperProcessor.from_pretrained(hub_path) |
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model = WhisperForConditionalGeneration.from_pretrained(hub_path).to("cuda") |
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dataset = load_dataset("RaphaelOlivier/whisper_adversarial_examples",config_name ,split=split_name) |
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def map_to_pred(batch): |
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input_features = processor(batch["audio"][0]["array"], return_tensors="pt").input_features |
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predicted_ids = model.generate(input_features.to("cuda")) |
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transcription = processor.batch_decode(predicted_ids, normalize = True) |
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batch['text'][0] = processor.tokenizer._normalize(batch['text'][0]) |
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batch["transcription"] = transcription |
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return batch |
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result = dataset.map(map_to_pred, batched=True, batch_size=1) |
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wer = load("wer") |
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for t in zip(result["text"],result["transcription"]): |
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print(t) |
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print(wer.compute(predictions=result["text"], references=result["transcription"])) |
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``` |
