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wav2vec 2.0

wav2vec 2.0 learns from raw audio to power data-efficient ASR via self-supervised, contrastive training.

Machine Learning Updated 5 minutes ago
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wav2vec 2.0

wav2vec 2.0's Top Features

Self-supervised learning from unlabeled raw audio
Operates directly on raw waveforms (no hand-crafted features)
Produces highly contextualized speech representations
Pre-train on unlabeled data, then fine-tune with labels
Contrastive learning objective over masked audio
Improved phoneme discrimination for phonetic tasks
Enables strong ASR with less labeled data
Scales efficiently to large speech datasets
Reduces dependence on transcriptions for low-resource settings
General-purpose speech features for multiple downstream tasks

Frequently asked questions about wav2vec 2.0

wav2vec 2.0 is a self-supervised framework that learns useful speech representations directly from raw audio without labeled data.

It was developed by researchers at Facebook AI Research (FAIR), as indicated on the arXiv abstract page.

It masks portions of the audio and uses a contrastive objective to predict masked content, learning contextualized features from unlabeled speech.

It achieves strong performance with far less labeled data by leveraging large amounts of unlabeled audio through self-supervised pre-training.

Yes. The learned representations can be fine-tuned for ASR, improving performance with reduced labeled data requirements.

Enhancing speech processing tasks—especially ASR—by pre-training on unlabeled audio and fine-tuning with limited labels.

The abstract does not specify availability; consult the full paper or linked resources on arXiv for details.

Unlabeled raw audio is used for self-supervised pre-training; labeled data is used for downstream fine-tuning.

A contrastive learning objective over masked audio representations that enables effective self-supervised learning.

The abstract does not specify language restrictions; the method is general and can be applied wherever unlabeled audio is available.

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Category
Machine Learning
Tags
speech recognition
self-supervised learning
audio processing
machine learning
contextualized speech representations

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