| Property | Value |
|---|---|
| Author | 0xb1 |
| Base Model | facebook/wav2vec2-base |
| Final Accuracy | 97.59% |
| Model URL | Hugging Face |
What is wav2vec2-base-finetuned-speech_commands-v0.02?
This model is a specialized fine-tuned version of Facebook's wav2vec2-base, specifically optimized for speech command recognition. It demonstrates exceptional performance with a 97.59% accuracy on the speech_commands dataset, making it highly reliable for voice command applications.
Implementation Details
The model was trained using a carefully tuned configuration with Adam optimizer (betas=0.9,0.999, epsilon=1e-08) and a linear learning rate scheduler. Training was conducted over 5 epochs with a batch size of 128 (32 base batch size with 4 gradient accumulation steps).
- Learning rate: 3e-05 with 0.1 warmup ratio
- Training batch size: 32 (effective 128 with gradient accumulation)
- Evaluation batch size: 32
- Training epochs: 5
Core Capabilities
- High accuracy speech command recognition (97.59%)
- Progressive improvement in validation loss from 0.7316 to 0.1170
- Stable training curve with consistent performance gains
- Optimized for production deployment
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional accuracy on speech commands, achieving 97.59% accuracy through careful fine-tuning of the wav2vec2-base architecture. The training process shows consistent improvement across epochs, indicating robust learning and generalization.
Q: What are the recommended use cases?
The model is particularly well-suited for speech command recognition tasks, voice-controlled applications, and automated speech processing systems where high accuracy is crucial. It's optimized for production environments requiring reliable voice command interpretation.