ccc-wav2vec2-base-SUPERB
| Property | Value |
|---|---|
| Research Paper | Available Here |
| Authors | Vasista Sai Lodagala, Sreyan Ghosh, S. Umesh |
| Framework | PyTorch |
| Dataset | LibriSpeech ASR |
What is ccc-wav2vec2-base-SUPERB?
This is an advanced speech processing model that introduces a novel pre-training strategy called ccc-wav2vec 2.0. The model is built upon the wav2vec2 architecture but incorporates clustering and an augmentation-based cross-contrastive loss as its self-supervised objective. It's specifically designed to work with 16kHz sampled speech audio and has shown significant improvements over baseline wav2vec 2.0 models.
Implementation Details
The model implements a sophisticated approach to speech processing through:
- Clustering module to reduce the impact of similar negative examples
- Cross-contrastive loss computation between original and augmented samples
- Pre-training on LibriSpeech-960h dataset
- 16kHz audio sampling rate requirement
Core Capabilities
- Achieves 15.6% relative WER improvement on LibriSpeech test-clean
- 12.7% improvement on test-other sets
- 14.9% relative WER improvement on Switchboard data
- Robust performance without language model usage
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its clustering-based approach and cross-contrastive loss mechanism, which helps it better handle similar negative examples during training and provides improved robustness through augmentation-based learning.
Q: What are the recommended use cases?
This model is best suited for speech recognition tasks after fine-tuning. It requires creation of a tokenizer and fine-tuning on labeled text data, making it ideal for researchers and developers working on speech recognition applications requiring high accuracy.
