| Property | Value |
|---|---|
| Parameter Count | 315M parameters |
| License | Apache 2.0 |
| Framework | PyTorch |
| Model Type | Speech Recognition |
| Best Validation CER | 13.32% |
What is wav2vec2-xls-r-300m-phoneme?
The wav2vec2-xls-r-300m-phoneme is a sophisticated speech recognition model built upon Facebook's wav2vec2-xls-r-300m architecture. This model has been specifically fine-tuned for phoneme recognition tasks, demonstrating impressive performance with a Character Error Rate (CER) of 13.32%.
Implementation Details
The model utilizes the Transformers framework and implements native AMP (Automatic Mixed Precision) training. It was trained using the Adam optimizer with carefully tuned hyperparameters (β1=0.9, β2=0.999, ε=1e-08) and implements a linear learning rate scheduler with 2000 warmup steps.
- Training batch size: 32 (8 base × 4 gradient accumulation steps)
- Learning rate: 3e-05
- Training steps: 7000
- Mixed precision training enabled
Core Capabilities
- Phoneme-level speech recognition
- Support for multiple languages (XLS-R architecture)
- Efficient inference with PyTorch backend
- Optimized for production deployment via Inference Endpoints
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimization for phoneme recognition tasks while leveraging the powerful XLS-R architecture, achieving a notable CER of 13.32% through careful fine-tuning and training procedures.
Q: What are the recommended use cases?
The model is particularly suited for phoneme-level speech recognition tasks, especially in applications requiring multilingual capabilities. It's ideal for automatic speech recognition systems, pronunciation analysis, and linguistic research.