Whisper-Small-FT-Common-Language-ID
| Property | Value |
|---|---|
| Base Model | OpenAI Whisper-small |
| Training Dataset | Common Language Dataset |
| Final Accuracy | 88.60% |
| Model URL | HuggingFace |
What is whisper-small-ft-common-language-id?
This model is a specialized fine-tuned version of OpenAI's Whisper-small, specifically optimized for language identification tasks. It represents a significant improvement over the base model, achieving an impressive 88.60% accuracy on the evaluation set through careful fine-tuning on the common language dataset.
Implementation Details
The model underwent a comprehensive training process over 10 epochs, utilizing the Adam optimizer with carefully tuned hyperparameters (β1=0.9, β2=0.999, ε=1e-08). The training employed a linear learning rate scheduler with a warmup ratio of 0.1 and an initial learning rate of 1e-05.
- Batch size configuration: Training batch size of 16 with gradient accumulation steps of 2, resulting in a total batch size of 32
- Training optimization: Implemented native AMP (Automatic Mixed Precision) training
- Validation metrics: Final validation loss of 0.6334 with consistent accuracy improvements throughout training
Core Capabilities
- Highly accurate language identification with 88.60% accuracy
- Optimized for common language detection tasks
- Efficient processing with mixed precision training support
- Stable training curve with continuous improvement over epochs
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized fine-tuning on language identification tasks, achieving high accuracy while maintaining the efficient architecture of Whisper-small. The careful optimization process and stable training curve demonstrate its reliability for production use.
Q: What are the recommended use cases?
The model is particularly suited for applications requiring accurate language identification, such as content categorization, multilingual processing pipelines, and automated language detection systems. Its optimized performance makes it ideal for both batch processing and real-time applications.
