facebook/wav2vec2-base-960h · Hugging Face

Description

Summary

• We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.
• wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned.
• Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets.
• This demonstrates the feasibility of speech recognition with limited amounts of labeled data.