Wav2Vec2 Speech Emotion Recognition Model
| Property | Value |
|---|---|
| Base Model | facebook/wav2vec2-base |
| Training Dataset | RAVDESS (1,440 samples) |
| Accuracy | ~65% |
| Model Hub | HuggingFace |
What is wav2vec2-base_speech_emotion_recognition?
This is a specialized speech emotion recognition model built on Facebook's Wav2Vec2 architecture. It's fine-tuned to identify 8 distinct emotions in speech: neutral, calm, happy, sad, angry, fearful, disgust, and surprised. The model processes raw audio input at 16kHz and leverages advanced speech processing capabilities to detect emotional nuances in human voice.
Implementation Details
The model was trained for 10 epochs using a learning rate of 3e-5 with warmup steps and weight decay. It employs dropout regularization (attention_dropout=0.1, hidden_dropout=0.1) and processes audio in batches of 4 with gradient accumulation. The training achieved an F1 score of ~0.63 and validation loss of ~1.2.
- Input: Raw audio files (.wav) at 16kHz sampling rate
- Output: Emotion classification with confidence scores
- Available in FP16 format for optimized inference
- GPU-compatible with CUDA support
Core Capabilities
- Real-time emotion classification from speech
- Multi-class emotion detection across 8 categories
- Probability distribution across all emotion classes
- Efficient processing with optional FP16 quantization
Frequently Asked Questions
Q: What makes this model unique?
The model combines Wav2Vec2's powerful speech processing capabilities with emotion recognition, offering a balanced approach between accuracy and computational efficiency. Its FP16 quantization option makes it suitable for deployment in resource-constrained environments.
Q: What are the recommended use cases?
The model is ideal for emotion analysis in controlled audio environments, such as customer service analysis, voice assistant enhancement, and research applications. However, it's important to note that performance may vary with real-world, noisy audio due to its training on acted speech.
