Navneetsinghnegi/Hinglish-ASR-Code-Switching-Speech-Recognition-using-Whisper-LoRA

This project is a standard application of Parameter-Efficient Fine-Tuning (PEFT) using the LoRA technique on the OpenAI Whisper model. While it targets the specific and difficult niche of 'Hinglish' code-switching, the technical approach is a well-documented recipe found in Hugging Face tutorials and PEFT documentation. With 0 stars and forks and being only 10 days old, it represents a personal experiment or a reference implementation rather than a defensible product. The 'moat' in ASR is typically high-quality, proprietary datasets; this repository provides the code pattern but lacks a unique data advantage or novel architectural change. Frontier labs (OpenAI, Google) are rapidly improving multilingual performance and naturally capture code-switching as their base models scale. Furthermore, local specialized players like Sarvam AI or Bhashini (India's National Language Translation Mission) possess significantly larger datasets and more robust infrastructure for this specific use case, making this project's long-term viability as a standalone entity very low.