Speech Emotion Recognition with Whisper Large V3
| Property | Value |
|---|---|
| Base Model | OpenAI Whisper Large V3 |
| Task | Speech Emotion Recognition |
| Accuracy | 91.99% |
| Number of Emotions | 7 (Angry, Disgust, Fearful, Happy, Neutral, Sad, Surprised) |
| Model Link | HuggingFace |
What is speech-emotion-recognition-with-openai-whisper-large-v3?
This is a specialized emotion recognition model that builds upon OpenAI's Whisper Large V3 architecture to detect emotions in speech. The model has been fine-tuned on a diverse dataset combining RAVDESS, SAVEE, TESS, and URDU speech samples, achieving impressive accuracy of 92% across seven distinct emotional states. It represents a significant advancement in automated emotion detection from audio inputs.
Implementation Details
The model utilizes a sophisticated training approach with carefully tuned hyperparameters including a learning rate of 5e-05, gradient accumulation over 5 steps, and mixed precision training. Audio preprocessing is handled through Librosa, with the Whisper Feature Extractor standardizing inputs for consistent analysis.
- Training conducted over 25 epochs with effective batch size of 10
- Implements Adam optimizer with specialized beta parameters
- Features linear learning rate scheduling with 0.1 warmup ratio
- Achieves 92.30% precision and 91.99% recall scores
Core Capabilities
- Real-time emotion classification from speech input
- Support for 7 distinct emotional states
- Handles variable-length audio inputs up to 30 seconds
- GPU-accelerated inference with CUDA support
- Simple integration through HuggingFace Transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its high accuracy in emotion recognition (92%) and its foundation on the powerful Whisper Large V3 architecture. The balanced training dataset and careful hyperparameter tuning make it particularly robust for real-world applications.
Q: What are the recommended use cases?
The model is ideal for applications in sentiment analysis, customer service automation, mental health monitoring, and human-computer interaction where understanding emotional context is crucial. It's particularly suited for scenarios requiring real-time emotion detection from speech.
