| Property | Value |
|---|---|
| Base Model | facebook/wav2vec2-xls-r-300m |
| Task | Filipino Speech Recognition |
| Performance | 29.22% WER |
| Author | Khalsuu |
| Model Link | Hugging Face |
What is filipino-wav2vec2-l-xls-r-300m-official?
This is a specialized speech recognition model fine-tuned for the Filipino language, based on Facebook's wav2vec2-xls-r-300m architecture. The model demonstrates strong performance with a Word Error Rate (WER) of 29.22% on the evaluation set, making it suitable for Filipino speech-to-text applications.
Implementation Details
The model was trained using a carefully optimized training procedure with the following key specifications: Adam optimizer with β=(0.9,0.999), linear learning rate scheduling with warmup steps, and mixed precision training using Native AMP. The training process spanned 30 epochs with a learning rate of 0.0003 and a total batch size of 16.
- Gradient accumulation steps: 2
- Learning rate warmup steps: 500
- Training batch size: 8
- Evaluation batch size: 8
- Seed: 42
Core Capabilities
- Filipino speech recognition with 29.22% WER
- Efficient processing with mixed precision training
- Optimized for production deployment
- Based on the robust wav2vec2-xls-r-300m architecture
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in Filipino speech recognition, leveraging the powerful wav2vec2-xls-r-300m architecture while achieving a competitive WER of 29.22%. The training process shows consistent improvement, with the error rate decreasing from 59.87% to 29.22% over the training period.
Q: What are the recommended use cases?
The model is particularly suited for Filipino speech-to-text applications, including transcription services, voice assistants, and automated subtitling systems. Its relatively low WER makes it suitable for production environments where accurate Filipino speech recognition is required.