Few-Shot Contrastive Adaptation for Audio Abuse Detection in Low-Resource Indic Languages

The project addresses a critical gap in safety systems: the 'ASR bottleneck' where transcription errors in low-resource languages prevent accurate hate speech detection. By bypassing ASR and using CLAP-based audio embeddings, it preserves prosodic cues (tone, volume) essential for identifying abuse. However, the project currently lacks any significant adoption (0 stars) and is primarily a research artifact (paper-linked). Its defensibility is low because it relies on standard CLAP architectures and public/semi-public Indic datasets; the primary value is the specific fine-tuning recipe. Frontier labs (OpenAI, Google, Meta) are rapidly developing native multimodal models (e.g., GPT-4o, Gemini 1.5 Flash) that process audio directly and are increasingly focused on safety layers for regional languages. While this project provides a specialized solution for Indic languages—a niche often neglected by Western labs—the technical moat is shallow and likely to be absorbed by larger 'safety-as-a-service' providers or platform-level audio moderation tools within 12-24 months.