yanghaha0908/FastHuBERT
Official implementation for Fast-HuBERT: An Efficient Training Framework for Self-Supervised Speech Representation Learning
This project helps researchers and developers accelerate the pre-training of self-supervised speech recognition models. It takes raw audio data or speech features as input and outputs a highly efficient, pre-trained speech representation model. Scientists and engineers working on speech processing applications, especially those constrained by computational resources, would use this.
No commits in the last 6 months.
Use this if you need to train self-supervised speech models significantly faster without compromising performance.
Not ideal if you are looking for a ready-to-use speech recognition system and are not involved in model training or research.
Stars
97
Forks
4
Language
Python
License
MIT
Category
Last pushed
Nov 20, 2024
Commits (30d)
0
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