ccc-wav2vec2-base
| Property | Value |
|---|---|
| Paper | Research Paper |
| Authors | Vasista Sai Lodagala, Sreyan Ghosh, S. Umesh |
| Framework | PyTorch, Transformers |
| Dataset | LibriSpeech ASR |
What is ccc-wav2vec2-base?
ccc-wav2vec2-base is an innovative speech processing model that introduces a novel pre-training strategy combining clustering and cross-contrastive learning. The model is specifically designed to work with 16kHz sampled speech audio and represents a significant advancement in self-supervised learning for speech recognition tasks.
Implementation Details
The model implements a unique pre-training approach called ccc-wav2vec 2.0, which incorporates two key components: a clustering module that reduces the impact of similar negative examples, and a cross-contrastive loss computed between original and augmented samples. This architecture has demonstrated remarkable improvements over the baseline wav2vec 2.0 model.
- Clustering-based negative example management
- Cross-contrastive loss between original and augmented samples
- Optimized for 16kHz audio processing
- Pre-trained on LibriSpeech-960h dataset
Core Capabilities
- 15.6% relative WER improvement on LibriSpeech test-clean
- 12.7% relative WER improvement on LibriSpeech test-other
- 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 distinctive feature is its innovative clustering and cross-contrastive learning approach, which significantly improves upon traditional wav2vec 2.0 performance without requiring a language model.
Q: What are the recommended use cases?
This model is ideal for speech recognition tasks, particularly when working with 16kHz audio. However, it requires fine-tuning with labeled data and a tokenizer for specific speech recognition applications.
