wav2vec2-lg-xlsr-en-speech-emotion-recognition
| Property | Value |
|---|---|
| Parameter Count | 316M |
| License | Apache 2.0 |
| Architecture | Wav2Vec2.0 XLSR |
| Accuracy | 82.23% |
What is wav2vec2-lg-xlsr-en-speech-emotion-recognition?
This is a fine-tuned speech emotion recognition model based on wav2vec2-large-xlsr-53-english, specifically trained to classify emotions in spoken English. The model can identify 8 distinct emotions: angry, calm, disgust, fearful, happy, neutral, sad, and surprised, making it valuable for various applications in emotion analysis and human-computer interaction.
Implementation Details
The model was trained using the RAVDESS dataset, comprising 1440 audio samples. Training utilized Adam optimizer with a learning rate of 0.0001, mixed precision training, and a linear learning rate scheduler over 3 epochs. The implementation achieved significant improvement in accuracy from 13.59% to 82.23% through the training process.
- Native AMP mixed precision training
- Gradient accumulation steps: 2
- Batch size: 8 (4 per step)
- Training epochs: 3
Core Capabilities
- Multi-class emotion classification across 8 categories
- High accuracy (82.23%) on evaluation set
- Support for English language audio processing
- Real-time emotion detection capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines the powerful wav2vec2 architecture with emotion recognition capabilities, achieving high accuracy while maintaining practical usability. Its fine-tuning on the RAVDESS dataset makes it particularly effective for English speech emotion analysis.
Q: What are the recommended use cases?
The model is ideal for applications in conversational AI, mental health monitoring, customer service analysis, and human-computer interaction where emotion detection from speech is crucial. It's particularly suitable for real-time emotion classification in English speech.
