hubert-base-superb-er
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | SUPERB: Speech processing Universal PERformance Benchmark |
| Task | Emotion Recognition |
| Input Format | 16kHz Speech Audio |
What is hubert-base-superb-er?
hubert-base-superb-er is a specialized speech emotion recognition model based on the HuBERT architecture. It's specifically trained on the IEMOCAP dataset to classify emotions in speech, achieving an accuracy of 63.59% on session1 evaluations. The model is part of the SUPERB benchmark suite, which evaluates speech processing tasks.
Implementation Details
The model is built upon facebook's hubert-base-ls960 and has been fine-tuned for emotion recognition tasks. It processes 16kHz sampled speech audio and can be easily integrated using the Transformers pipeline or used directly with HubertForSequenceClassification.
- Built on HuBERT base architecture
- Requires 16kHz audio input
- Supports batch processing with attention masks
- Implements sequence classification for emotion recognition
Core Capabilities
- Emotion classification from speech audio
- Handles multiple emotion classes with balanced performance
- Supports real-time audio processing
- Integrates seamlessly with the Transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines HuBERT's powerful speech understanding capabilities with emotion recognition training on the IEMOCAP dataset, making it specifically optimized for emotion detection in speech.
Q: What are the recommended use cases?
The model is ideal for applications requiring emotion detection in spoken content, such as customer service analytics, mental health applications, or interactive voice response systems. It works best with clear, 16kHz audio input.
