Wav2Vec 2.0
Self-supervised speech representation model by Meta for ASR.
About
Wav2Vec 2.0 by Meta AI is a self-supervised model that learns speech representations from unlabeled audio, then fine-tunes for automatic speech recognition with as little as ten minutes of labeled data. It is distributed through the fairseq sequence-modeling toolkit and became a foundation for many later speech systems. Pretrained checkpoints are provided for several languages. Released under the MIT license.
Reviews (0)
Leave a Review
No reviews yet. Be the first to review!
Details
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Advanced (4/5)
- License
- MIT
- Minimum VRAM
- 8 GB
- Added
- Apr 3, 2026
Related Tools
Convolution-augmented transformer for speech recognition in ESPnet toolkit.
End-to-end speech processing toolkit covering ASR, TTS, and speech translation.
CLI tool that transcribes audio 10x faster using pipeline optimizations.
Established speech recognition toolkit used in research and production systems.
Open-source speaker diarization and voice activity detection toolkit.
Multilingual ASR model by NVIDIA supporting 4 languages with translation.