wav2vec 2.0: Self-supervised speech representations for data-efficient ASR
wav2vec 2.0 is a self-supervised framework for learning rich, contextualized speech representations directly from raw audio using masked prediction with a contrastive objective. By pre-training on large unlabeled corpora and fine-tuning with limited labeled data, wav2vec 2.0 powers data-efficient automatic speech recognition and other speech processing tasks while reducing dependence on transcriptions and scaling effectively to diverse languages and domains.
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