wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Baevski et al., 2020, wav2vec 2.0 (Advances in Neural Information Processing Systems 33 (NeurIPS 2020))

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4.1 Datasets

What data did wav2vec 2.0 use?
Unlabelled: LibriSpeech audio (960 h) or LibriVox (LV-60k, 53.2k h). Labelled fine-tuning: 960 h, the 100 h clean subset, and Libri-light’s 10 h, 1 h and 10 min splits; TIMIT (5 h) for phoneme recognition with 39 phone classes.
- Baevski et al., 2020, wav2vec 2.0 (Advances in Neural Information Processing Systems 33 (NeurIPS 2020)), §4.1

Source

Alexei Baevski, Henry Zhou, Abdelrahman Mohamed and Michael Auli. 2020. wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations. In Advances in Neural Information Processing Systems 33 (NeurIPS 2020). https://papers.nips.cc/paper/2020/hash/92d1e1eb1cd6f9fba3227870bb6d7f07-Abstract.html