Available into 3 audio variants
- audio_type: "silence" - Background silence
- audio_type: "noise_a" - Background noise variant A
- audio_type: "noise_b" - Background noise variant B

Metadata Schema

{
  "ID": 1,
  "audio": "audio/filename.wav",
  "original_id": 0,
  "user_id": "hash_string",
  "lang": "de",
  "vulnerability_type": "stereotype",
  "audio_type": "speech",
  "modality": "speech",
  "user_text": "text input or question",
  "transcript": "transcribed speech content"
}

Features

ID: unique entry identifier
audio: Path to input audio file
user_id: Unique identifier for user
lang: Source language
original_id: original dataset identifier from M-ALERT and SHADES datasets
vulnerability_type: Type of vulnerability being tested (stereotype or unsafe request)
attack_type: Type of attack (non_adversarial)
audio_type: Type of audio (speech / silence / noise_a / noise_b)
user_text: User's text input
modality: harmful content modality (in speech or text)
gender: User's gender
age_group: User's age group
mother_tongue: User's mother tongue
ethnicity: User's ethnicity
transcript: Speech transcript of the audio input

Stats by Language

lang	total	speech	silence	noise_a	noise_b
de	519	135	128	128	128
en	1359	342	339	339	339
es	354	85	89	90	90
fr	401	95	102	102	102
it	781	184	199	199	199

Splits

The dataset can be loaded with different configurations:

from datasets import load_dataset

# Load German language config
dataset = load_dataset("path/to/repo", name="de", split="test")

# Load English language config
dataset = load_dataset("path/to/repo", name="en", split="test")

Bias, Risks, and Limitations

Consent: All voice recordings and associated participant metadata in this dataset come from individuals who gave explicit, informed consent for their data to be published and used for research purposes.
Harmful content by design: RedVox is a red-teaming dataset and is intended solely for safety and robustness evaluation, not as representative or endorsed text/speech.
Speaker privacy and re-identification risk: Users must not attempt to re-identify, or deanonymize any speaker in this dataset, whether through manual inspection, automated techniques, or cross-referencing with other data sources.

Citation

If you use this dataset in your research, please cite the following paper:

@misc{savoldi2026redvoxsafetyfairnessgaps,
      title={RedVox: Safety and Fairness Gaps in Speech Models Across Languages}, 
      author={Beatrice Savoldi and Sara Papi and Wafa Aissa and Matteo Negri and Luisa Bentivogli},
      year={2026},
      eprint={2606.26968},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.26968}
}

Code and Evaluation

For code, evaluation scripts, and additional resources related to this dataset, see: hlt-mt/redvox

License

RedVox is derived from two source datasets, and each subset is released under the same license as its original source.

SHADES subset (vulnerability_type: stereotype) — derived from SHADES and released under the same Montreal Data License as the original. Use requires acceptance of the Acceptable Use Policy in that license (no harmful, discriminatory, or law-enforcement/immigration use; no use to identify further training data; distribution only under the same terms; attribution to SHADES required).
M-ALERT subset (all other entries, vulnerability_type: unsafe_request*`) — derived from M-ALERT and released under the same CC BY-NC-SA 4.0 license as the original (non-commercial use, attribution required, share-alike).

This repository is gated. Requesting access requires agreeing to use RedVox exclusively for non-commercial AI safety and fairness research, and to comply with the Acceptable Use Policy.

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Paper for FBK-MT/RedVox

RedVox: Safety and Fairness Gaps in Speech Models Across Languages

Paper • 2606.26968 • Published Jun 25 • 14