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DISTIL-WHISPER
Fast, accurate speech recognition distilled from Whisper
MIT
ABOUT
OpenAI's Whisper provides state-of-the-art speech recognition but its large model sizes and high latency make it impractical for real-time or edge deployment. Distil-Whisper applies knowledge distillation — training a smaller student model to mimic the teacher's output — to produce a model that runs six times faster and uses half the parameters while retaining within 1% word error rate of the original. This makes accurate, multilingual speech transcription feasible on CPU, mobile devices, and low-power hardware without compromising quality.
INSTALL
pip install transformers accelerateINTEGRATION GUIDE
1. Transcribe audio in real-time on edge devices with limited compute and memory
2. Run speech recognition on CPU servers at reduced cost compared to full Whisper models
3. Build privacy-preserving voice interfaces that process audio locally without cloud APIs
4. Integrate fast, accurate transcription into live captioning and meeting recording pipelines
5. Deploy multilingual speech-to-text on mobile apps with minimal battery and latency impact
TAGS
speech-recognitionwhisperdistillationaudiohuggingfacetransformersedge