wav2vec2-base-superb-er
| Property | Value |
|---|---|
| Author | SUPERB |
| Task | Emotion Recognition |
| Model Base | wav2vec2-base |
| Accuracy | 62.58% |
| Paper | SUPERB: Speech processing Universal PERformance Benchmark |
What is wav2vec2-base-superb-er?
wav2vec2-base-superb-er is a specialized speech emotion recognition model based on the wav2vec2 architecture. It's specifically designed to classify emotions from speech audio, trained on the IEMOCAP dataset as part of the SUPERB benchmark. The model works with 16kHz sampled speech audio and can classify utterances into four balanced emotion classes.
Implementation Details
The model builds upon the wav2vec2-base architecture and has been fine-tuned for emotion recognition tasks. It processes 16kHz audio input and outputs emotion classifications. The implementation supports both pipeline-based usage through Hugging Face's audio-classification pipeline and direct model usage with custom preprocessing.
- Built on wav2vec2-base pretrained model
- Requires 16kHz audio sampling rate
- Implements sequence classification architecture
- Uses Wav2Vec2FeatureExtractor for preprocessing
Core Capabilities
- Emotion classification from speech audio
- Handles variable-length audio inputs
- Provides confidence scores for predictions
- Achieves 62.58% accuracy on standard benchmarks
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for emotion recognition as part of the SUPERB benchmark, offering a standardized approach to speech emotion classification while leveraging the powerful wav2vec2 architecture.
Q: What are the recommended use cases?
The model is ideal for emotion analysis in spoken content, particularly in scenarios requiring real-time or batch processing of 16kHz audio. It's suitable for applications in conversational AI, customer service analysis, and speech emotion research.
