hubert-base-superb-ks
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | SUPERB Benchmark Paper |
| Accuracy | 96.72% (test) |
| Task Type | Audio Classification |
What is hubert-base-superb-ks?
hubert-base-superb-ks is a specialized audio classification model based on the HuBERT architecture, specifically designed for keyword spotting tasks. It's built upon the hubert-base-ls960 foundation and has been optimized for detecting predefined keywords in 16kHz sampled speech audio.
Implementation Details
The model is implemented using PyTorch and the Transformers library, leveraging the Speech Commands dataset v1.0 for training. It's capable of classifying utterances into predefined word categories, including ten keyword classes, a silence class, and an unknown class for handling false positives.
- Built on hubert-base-ls960 architecture
- Optimized for 16kHz audio input
- Supports batch processing with attention masks
- Implements SUPERB benchmark standards
Core Capabilities
- Real-time keyword detection in speech
- Multi-class classification across 12 categories
- High accuracy (96.72% on test set)
- Efficient on-device processing support
- Robust audio feature extraction
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its high accuracy in keyword spotting while maintaining efficient processing capabilities suitable for on-device deployment. It's part of the SUPERB benchmark suite, ensuring standardized performance metrics and reliable comparison with other speech processing models.
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 speech command interfaces. It's particularly suitable when working with 16kHz audio and requiring real-time processing capabilities.
