metadata

license: cc-by-4.0

Description

This dataset is a subset of LibriSpeech and Multilingual CommonVoice that have been adversarially modified to fool Whisper ASR model.

Original source code.

The raw tar files.

Configurations and splits

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
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
The language-<lang> configs contain adversarial examples in language <lang> that fool Whisper in predicting the wrong language. Split .contain inputs that Whisper perceives as <target_lang>, and split.original` contains the original inputs in language . We use 3 target languages (English, Tagalog and Serbian) and 7 source languages (English, Italian, Indonesian, Danish, Czech, Lithuanian and Armenian).

Usage

Here is an example of code using this dataset:

model_name="whisper-medium"
config_name="targeted"
split_name="whisper.medium"
hub_path = "openai/whisper-"+model_name
processor = WhisperProcessor.from_pretrained(hub_path)
model = WhisperForConditionalGeneration.from_pretrained(hub_path).to("cuda")

dataset = load_dataset("RaphaelOlivier/whisper_adversarial_examples",config_name ,split=split_name)

def map_to_pred(batch):
    input_features = processor(batch["audio"][0]["array"], return_tensors="pt").input_features
    predicted_ids = model.generate(input_features.to("cuda"))
    transcription = processor.batch_decode(predicted_ids, normalize = True)
    batch['text'][0] = processor.tokenizer._normalize(batch['text'][0])
    batch["transcription"] = transcription
    return batch

result = dataset.map(map_to_pred, batched=True, batch_size=1)

wer = load("wer")
for t in zip(result["text"],result["transcription"]):
    print(t)
print(wer.compute(predictions=result["text"], references=result["transcription"]))