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license: cc-by-sa-3.0 |
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--- |
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## Model Description |
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We present a large classification model trained on a manually curated real-world dataset that can be used as a new |
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benchmark for advancing research in toxicity detection and classification. |
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We started with the original weights from the [WavLM base plus](https://arxiv.org/abs/2110.13900) and fine-tuned it |
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with 2,374 hours of voice chat audio clips for multilabel classification. The audio clips are automatically labeled using a |
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synthetic data pipeline described in [our blog post](https://research.roblox.com/tech-blog/2024/06/deploying-ml-for-voice-safety). A single output can have multiple labels. |
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The model outputs a n by 6 output tensor where the inferred labels are `Profanity`, `DatingAndSexting`, `Racist`, |
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`Bullying`, `Other`, `NoViolation`. `Other` consists of policy violation categories with low prevalence such as drugs |
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and alcohol or self-harm that are combined into a single category. |
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We evaluated this model on a data set with human annotated labels that contained a total of 9,795 samples with the class |
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distribution shown below. Note that we did not include the "other" category in this evaluation data set. |
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|Class|Number of examples| Duration (hours)|% of dataset| |
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|---|---|---|---| |
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|Profanity | 4893| 15.38 | 49.95%| |
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|DatingAndSexting | 688 | 2.52 | 7.02% | |
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|Racist | 889 | 3.10 | 9.08% | |
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|Bullying | 1256 | 4.25 | 12.82% | |
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|NoViolation | 4185 | 9.93 | 42.73% | |
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If we set the same threshold across all classes and treat the model as a binary classifier across all 4 toxicity classes |
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(`Profanity`, `DatingAndSexting`, `Racist`, `Bullying`), we get a binarized average precision of 94.48%. The precision |
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recall curve is as shown below. |
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<p align="center"> |
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<img src="images/human_eval_pr_curve.png" alt="PR Curve" width="500"/> |
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</p> |
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## Usage |
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The dependencies for the inference file can be installed as follows: |
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``` |
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pip install -r requirements.txt |
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``` |
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The inference file contains useful helper functions to preprocess the audio file for proper inference. |
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To run the inference file, please run the following command: |
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``` |
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python inference.py --audio_file <your audio file path> --model_path <path to Huggingface model> |
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``` |
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You can get the model weights either by downloading from the model releases page [here](https://github.com/Roblox/voice-safety-classifier/releases/tag/vs-classifier-v1), or from HuggingFace under |
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[`roblox/voice-safety-classifier`](https://huggingface.co/Roblox/voice-safety-classifier). If `model_path` isn’t |
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specified, the model will be loaded directly from HuggingFace. |
