wav2vec2-base-superb-ks
| Property | Value |
|---|---|
| Author | SUPERB |
| Task Type | Keyword Spotting |
| Accuracy | 96.43% |
| Paper | SUPERB: Speech processing Universal PERformance Benchmark |
What is wav2vec2-base-superb-ks?
wav2vec2-base-superb-ks is a specialized implementation of the wav2vec2 architecture designed specifically for keyword spotting tasks. Based on the wav2vec2-base model, it's been optimized to process 16kHz sampled speech audio for detecting specific keywords from a predefined set of words.
Implementation Details
The model is built upon the wav2vec2-base architecture and has been fine-tuned using the Speech Commands dataset v1.0. It supports classification into twelve classes: ten keyword classes, one silence class, and an unknown class for handling false positives. The model requires 16kHz audio input and includes built-in feature extraction capabilities.
- Pre-trained on 16kHz sampled speech audio
- Optimized for on-device deployment
- Supports batch processing and attention masking
- Includes integrated feature extraction pipeline
Core Capabilities
- High-accuracy keyword detection (96.43% on test set)
- Real-time speech processing
- Multi-class classification support
- Efficient audio feature extraction
- Compatible with transformers pipeline API
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specific optimization for keyword spotting tasks while maintaining high accuracy (96.43%). It's designed for practical deployment scenarios, particularly for on-device applications where both performance and response time are critical.
Q: What are the recommended use cases?
The model is ideal for applications requiring keyword detection in speech, such as voice-activated systems, smart home devices, and other speech interface applications. It's particularly suitable for scenarios where 16kHz audio processing is needed and where both accuracy and processing speed are important factors.
