seamless_communication/src/seamless_communication/cli/toxicity/mutox

MuTox: MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector

MuTox, the first highly multilingual audio-based dataset with toxicity labels. The dataset consists of 20k audio utterances for English and Spanish, and 4k for the other 19 languages. To showcase the quality of this dataset, we train the MuTox audio-based toxicity classifier, which allows zero-shot toxicity detection across a broad range of languages. This classifier outperforms existing text-based trainable classifiers by more than 1% AUC, while increasing the language coverage from 8 to 100+ languages. When compared to a wordlist-based classifier that covers a similar number of languages, MuTox improves precision and recall by ∼2.5 times.

License

The mutox code and model are licensed under the MIT license (see MIT_LICENSE file at the root of seamless_communication). The mutox model depends on SONAR encoders, most are under the MIT license but a few are under CC-BY-NC license. See the SONAR repository for details.

Dataset Languages.

• English,
• Spanish,
• Arabic,
• Bengali,
• Mandarin Chinese,
• Dutch,
• French,
• German,
• Hindi,
• Indonesian,
• Italian,
• Japanese,
• Korean,
• Portuguese,
• Russian,
• Swahili,
• Tagalog,
• Thai,
• Turkish,
• Urdu,
• Vietnamese

Classifier details.

We use multi-modal and multilingual SONAR encoders from (Duquenne et al., 2023). For the classifier, we use variable input sizes for the 3 feedforward layers (1024, 512, and 128).

The predictions of the classifier can be interpreted as logits (i.e. after feeding them to a sigmoid transform they become probabilities). The 0 value can be used as a threshold, as it corresponds to the 50% predicted toxicity probability.

Classifier Quick Start

This introduces the MuTox speech toxicity model, this relies on computing the sonar embedding and then classifying it through the MuTox model. The cli/mutox/mutox.py provides an example of reading a TSV, computing the SONAR embedding and running the classifier on the results:

python -m seamless_communication.cli.toxicity.mutox.mutox_speech --lang fra --audio_column ref_tgt_audio /checkpoint/bokai/seamless/toxity_mitigation/exps_v5/joined_etox/fleurs/s2t/en-xx/fra.tsv /tmp/tesmortt.tsv

You can also work with text:

python -m seamless_communication.cli.toxicity.mutox.mutox_text --lang fra_Latn sentences.txt

You can also check the mutox example notebook in this directory.

Dataset

The dataset is available in this tsv file. The dataset is licensed under the MIT license (see MIT_LICENSE file at the root of seamless_communication).

The columns of the dataset are:

• id: a string id of the segment;
• lang: 3-letter language code;
• partition: one of train, dev, or devtest;
• public_url_segment: a string formatted as url:start:end, where start and end are indicated in milliseconds;
• audio_file_transcript: text transctiption of the segment;
• contains_toxicity, toxicity_types, perlocutionary_effects: annotation results as strings (see the paper for their explanation);
• label: an integer label, equal to 1 if contains_toxicity equals Yes and 0 otherwise;
• etox_result: toxic word (or multiple words, separated by |) detected by the Etox matcher;
• detoxify_score: toxicity probabilities predicted by the Detoxify system (float numbers between 0 and 1);
• mutox_speech_score, mutox_text_score, mutox_zero_shot_speech_score, mutox_zero_shot_text_score: MuTox predictions as float numbers with any value (they can be interpreted as logits, i.e. probabilities before a sigmoid transformation).

Citation

@misc{costajussà2023mutox,
      title={MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector},
      author={ Marta R. Costa-jussà, Mariano Coria Meglioli, Pierre Andrews, David Dale, Prangthip Hansanti, Elahe Kalbassi, Alex Mourachko, Christophe Ropers, Carleigh Wood},
      year={2023},
      eprint={},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}