Whisper Enhanced ML
| Property | Value |
|---|---|
| Model Base | Whisper Small |
| Author | nurzhanit |
| WER Score | 22.35% |
| Framework | PyTorch 2.5.0 |
| Hugging Face | Model Repository |
What is whisper-enhanced-ml?
Whisper-enhanced-ml is a specialized speech recognition model that builds upon the Whisper Small architecture. Fine-tuned on the Common Voice 11.0 dataset, this model demonstrates robust performance with a Word Error Rate (WER) of 22.35%, making it particularly effective for speech recognition tasks.
Implementation Details
The model was trained using a carefully crafted optimization strategy, employing the Adam optimizer with beta values of (0.9, 0.999) and epsilon of 1e-08. The training process consisted of 500 steps with a linear learning rate scheduler and 50 warmup steps. The learning rate was set to 1e-05, with batch sizes of 16 for training and 8 for evaluation.
- Training conducted over 100 epochs
- Achieved final training loss of 0.0003
- Consistent WER performance of 22.3549 after epoch 30
- Implements latest Transformers (4.40.0) and PyTorch (2.5.0) frameworks
Core Capabilities
- Speech recognition with competitive WER performance
- Optimized for Common Voice dataset applications
- Stable performance metrics across evaluation phases
- Efficient training convergence within 300 steps
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its optimized performance on the Common Voice 11.0 dataset, achieving a stable WER of 22.35% through careful fine-tuning and hyperparameter optimization. The quick convergence and stability in performance metrics make it particularly reliable for speech recognition tasks.
Q: What are the recommended use cases?
This model is best suited for speech recognition applications, particularly those working with Common Voice-like datasets. It's especially effective in scenarios requiring reliable transcription services with consistent error rates around 22%.
