Hugging Face researchers have created a smaller version of their pre-trained speech recognition model called Distil-Whisper to address the challenges of deploying large models in resource-constrained environments. They used a pseudo-labelling method to create a dataset and applied knowledge distillation to derive Distil-Whisper. The new model achieves faster speed, fewer parameters, and mitigates errors in challenging acoustic conditions while maintaining competitive performance. The research highlights the use of pseudo-labelling and knowledge distillation for compressing transformer-based models in speech recognition.
Hugging Face Researchers Introduce Distil-Whisper: A Compact Speech Recognition Model Bridging the Gap in High-Performance, Low-Resource Environments
Hugging Face researchers have developed a practical solution for deploying large pre-trained speech recognition models in resource-constrained environments. They have created an open-source dataset through pseudo-labelling and used it to distil a smaller version of the Whisper model, called Distil-Whisper.
Key Features of Distil-Whisper:
- Whisper model pre-trained on 680,000 hours of noisy internet speech data
- Compact version derived through knowledge distillation using pseudo-labelling
- Retains resilience in challenging acoustic conditions
- Mitigates hallucination errors in long-form audio
- Significantly enhances speed and reduces parameters compared to the original Whisper model
- Achieves less than 1% word error rate (WER) on out-of-distribution test data in a zero-shot scenario
Distil-Whisper offers remarkable benefits in terms of speed and parameter reduction, making it more practical for low-latency deployment. It maintains competitive performance while reducing model size and improving efficiency.
Future Research Opportunities:
There are promising opportunities for further research in audio domain knowledge distillation and pseudo-labelling for compressing transformer-based models in speech recognition. Exploring various filtering methods and thresholds can optimize transcription quality and downstream model performance. Additionally, investigating alternative compression techniques can lead to even greater model compression without sacrificing performance.
For more information, you can check out the paper and GitHub repository.
If you are interested in AI solutions for your company, consider leveraging the benefits of Distil-Whisper. To explore AI opportunities, connect with us at hello@itinai.com. Stay updated on AI insights by following us on Telegram or Twitter @itinaicom.
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