Whisper Medium FLEURS Language ID
| Property | Value |
|---|---|
| Parameter Count | 308M |
| Model Type | Language Identification |
| License | Apache 2.0 |
| Accuracy | 88.05% |
| Framework | PyTorch |
What is whisper-medium-fleurs-lang-id?
This model is a specialized fine-tuned version of OpenAI's Whisper Medium model, specifically adapted for language identification tasks using the FLEURS dataset. It represents a significant advancement in audio processing capabilities, combining the robust architecture of Whisper with targeted training for language detection.
Implementation Details
The model utilizes a transformer-based architecture with FP16 precision for efficient processing. It was trained using a distributed multi-GPU setup with carefully tuned hyperparameters, including a learning rate of 3e-05 and a linear scheduler with 0.1 warmup ratio. The training process spanned 3 epochs with a total batch size of 32.
- Optimizer: Adam with betas=(0.9,0.999)
- Training batch size: 16 with gradient accumulation steps of 2
- Evaluation batch size: 32
- Advanced learning rate scheduling with warmup
Core Capabilities
- High-accuracy language identification (88.05% on evaluation)
- Efficient processing with FP16 precision
- Scalable performance for production environments
- Integration with popular ML frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized fine-tuning on the FLEURS dataset, achieving impressive accuracy while maintaining the robust capabilities of the Whisper architecture. The combination of FP16 precision and careful hyperparameter optimization makes it particularly efficient for production deployments.
Q: What are the recommended use cases?
The model is ideal for automated language identification in audio processing pipelines, multilingual content analysis, and speech processing applications where language detection is crucial. It's particularly well-suited for scenarios requiring high accuracy and efficient processing.
