XLSR-Wav2Vec Speech Emotion Recognition
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch |
| Dataset | AESDD |
| Overall Accuracy | 80.6% |
What is xlsr-wav2vec-speech-emotion-recognition?
This is a specialized speech emotion recognition model that leverages the XLSR-Wav2Vec architecture to classify emotions in speech audio. The model can detect five distinct emotions: anger, disgust, fear, happiness, and sadness, with particularly strong performance in detecting anger (82% precision) and disgust (85% precision).
Implementation Details
The model utilizes the Wav2Vec2 feature extractor and transforms raw audio input into emotion classifications. It's implemented using PyTorch and requires minimal preprocessing, making it accessible for real-world applications. The model processes audio files through a speech-to-array function and outputs probability scores for each emotion category.
- Supports multiple audio formats through torchaudio
- Includes automatic resampling capabilities
- Provides probability scores for all emotion classes
- Implements efficient batch processing
Core Capabilities
- Multi-class emotion classification across 5 categories
- High accuracy for anger detection (100% recall)
- Strong performance on disgust classification (96% recall)
- Real-time inference capabilities
- Robust feature extraction using Wav2Vec2
Frequently Asked Questions
Q: What makes this model unique?
The model combines the powerful XLSR-Wav2Vec architecture with emotion recognition capabilities, achieving impressive accuracy scores across different emotions. Its particularly strong performance in detecting anger and disgust makes it suitable for applications requiring precise emotion detection.
Q: What are the recommended use cases?
The model is well-suited for applications in customer service analysis, mental health monitoring, human-computer interaction, and automated content moderation where emotion detection from speech is crucial. Its balanced performance across different emotions makes it particularly valuable for real-world applications.
