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Copy file name to clipboardexpand all lines: README.md
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## Model Description
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The model is finetuned on the [WavLM base plus](https://arxiv.org/abs/2110.13900) with 2,374 hours of audio clips from
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voice chat for multilabel classification.
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The audio clips are automatically labeled using a synthetic data pipeline described in [our blog post](link to blog post here).
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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 dataset with human annotated labels that contained a total of 9795 samples with the class
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distribution shown below. Note that we did not include the "other" category in this evaluation dataset.
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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 (`Profanity`, `DatingAndSexting`, `Racist`, `Bullying`), we get a binarized average precision of 94.48%. The precision recall curve is as shown below.
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 `roblox/voice-safety-classifier`.
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If `model_path` isn’t specified, the model will be loaded directly from HuggingFace.
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