Model Description
We present a large classification model trained on a manually curated real-world dataset that can be used as a new
benchmark for advancing research in toxicity detection and classification.
Our model is fine-tuned on the WavLM base plus with 2,374 hours of audio clips from
voice chat for multilabel classification. The audio clips are automatically labeled using a synthetic data pipeline
described in [our blog post](link to blog post here). A single output can have multiple labels.
The model outputs a n by 6 output tensor where the inferred labels are Profanity, DatingAndSexting, Racist,
Bullying, Other, NoViolation. Other consists of policy violation categories with low prevalence such as drugs
and alcohol or self-harm that are combined into a single category.
We evaluated this model on a data set with human annotated labels that contained a total of 9,795 samples with the class distribution shown below. Note that we did not include the "other" category in this evaluation data set.
| Class | Number of examples | Duration (hours) | % of dataset |
|---|---|---|---|
| Profanity | 4893 | 15.38 | 49.95% |
| DatingAndSexting | 688 | 2.52 | 7.02% |
| Racist | 889 | 3.10 | 9.08% |
| Bullying | 1256 | 4.25 | 12.82% |
| NoViolation | 4185 | 9.93 | 42.73% |
If we set the same threshold across all classes and treat the model as a binary classifier across all 4 toxicity classes
(Profanity, DatingAndSexting, Racist, Bullying), we get a binarized average precision of 94.48%. The precision
recall curve is as shown below.
Usage
The dependencies for the inference file can be installed as follows:
pip install -r requirements.txt
The inference file contains useful helper functions to preprocess the audio file for proper inference. To run the inference file, please run the following command:
python inference.py --audio_file <your audio file path> --model_path <path to Huggingface model>
You can get the model weights either by downloading from the model releases page here, or from HuggingFace under
roblox/voice-safety-classifier. If model_path isn’t
specified, the model will be loaded directly from HuggingFace.