hubert-large-superb-er
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | SUPERB Paper |
| Task | Emotion Recognition |
| Architecture | HuBERT Large |
What is hubert-large-superb-er?
hubert-large-superb-er is a specialized emotion recognition model based on the HuBERT-Large architecture, fine-tuned specifically for the SUPERB (Speech processing Universal PERformance Benchmark) emotion recognition task. It's designed to process 16kHz speech audio and classify emotions into four balanced categories using the IEMOCAP dataset.
Implementation Details
The model is built upon facebook's hubert-large-ll60k and has been optimized for emotion recognition tasks. It utilizes the Transformers architecture and can be easily implemented using the HuggingFace pipeline for audio classification or used directly with the HubertForSequenceClassification class.
- Requires 16kHz sampled speech input
- Supports batch processing with attention masks
- Achieves 67.62% accuracy on the IEMOCAP dataset
- Implements PyTorch backend for efficient processing
Core Capabilities
- Real-time emotion classification from speech
- Handles variable-length audio inputs
- Provides probability distributions across emotion classes
- Supports both pipeline and direct model usage
Frequently Asked Questions
Q: What makes this model unique?
This model is part of the SUPERB benchmark suite and has been specifically optimized for emotion recognition tasks, making it particularly effective for analyzing emotional content in speech. It builds upon the robust HuBERT-Large architecture while focusing on practical emotion classification applications.
Q: What are the recommended use cases?
The model is ideal for applications requiring emotion analysis from speech, such as call center analytics, mental health monitoring, human-computer interaction, and social robotics. It's particularly suited for scenarios where high-quality 16kHz audio input is available.
