emotion-recognition-wav2vec2-IEMOCAP
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | SpeechBrain Paper |
| Framework | PyTorch / SpeechBrain |
| Accuracy | 78.7% (Avg: 75.3%) |
What is emotion-recognition-wav2vec2-IEMOCAP?
This is a specialized speech emotion recognition model that leverages the wav2vec2 architecture, fine-tuned on the IEMOCAP dataset using the SpeechBrain framework. The model processes audio input sampled at 16kHz to classify emotions in speech, making it particularly useful for affective computing applications.
Implementation Details
The model architecture combines convolutional and residual blocks with attentive statistical pooling for embedding extraction. It uses Additive Margin Softmax Loss during training and performs emotion classification using cosine distance between speaker embeddings. The system automatically handles audio normalization, including resampling and mono channel selection.
- Built on wav2vec2 base architecture
- Supports 16kHz audio input (single channel)
- Automatic audio normalization
- GPU inference support
Core Capabilities
- Real-time emotion recognition from speech
- Automatic audio preprocessing
- High accuracy (78.7%) on IEMOCAP test set
- Easy integration with SpeechBrain ecosystem
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful wav2vec2 architecture with SpeechBrain's robust training framework, achieving high accuracy in emotion recognition while providing simple deployment options.
Q: What are the recommended use cases?
The model is ideal for applications requiring emotion analysis from speech, such as call center analytics, human-computer interaction systems, and affective computing research. It's particularly suited for English language audio processing.
