Distil-wav2vec2
| Property | Value |
|---|---|
| Model Size | 197.9 MB |
| Original Paper | wav2vec 2.0 |
| Author | OthmaneJ |
| Implementation | GitHub Repository |
What is distil-wav2vec2?
Distil-wav2vec2 is a compressed version of the original wav2vec2 speech recognition model, achieving remarkable efficiency improvements while maintaining competitive performance. This distilled model is 45% smaller than the original wav2vec2-base, requiring only 197.9MB of storage compared to the original 360MB.
Implementation Details
The model demonstrates impressive performance metrics while significantly reducing computational requirements. On the Librispeech test sets, it achieves a Word Error Rate (WER) of 9.83% on test-clean and 22.66% on test-other. Processing speed shows notable improvements, with batch processing (size 64) taking 0.4006s on CPU and 0.0046s on GPU, compared to the base model's 0.4919s and 0.0082s respectively.
- 45% reduction in model size
- 2x faster inference speed
- Competitive WER scores on benchmark datasets
- Optimized for both CPU and GPU deployment
Core Capabilities
- Efficient speech recognition processing
- Balanced trade-off between model size and accuracy
- Suitable for resource-constrained environments
- Compatible with standard wav2vec2 pipelines
Frequently Asked Questions
Q: What makes this model unique?
The model's primary strength lies in its efficient design, offering significant size and speed improvements while maintaining reasonable accuracy levels. This makes it particularly valuable for applications where resource constraints are a concern.
Q: What are the recommended use cases?
This model is ideal for applications requiring quick speech recognition processing, especially in environments with limited computational resources. It's particularly suitable for mobile applications, edge devices, or scenarios where rapid processing is prioritized over maximum accuracy.
