facebook/wav2vec2-large-960h-lv60-self
Automatic Speech Recognition
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956k
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Wav2Vec 2.0
A collection for the first release of Wav2Vec 2.0, a speech encoder that learns powerful representations from unlabelled audio data.
Note The Wav2Vec 2.0 "large" model pre-trained on 53k hours of un-labelled audio data from the LibriSpeech and LibriVox (LV) corpora, and fine-tuned on 960 hours of LibriSpeech ASR data. This is the most performant Wav2Vec 2.0 checkpoint from the initial release, obtaining 1.9/3.9% WER on the LibriSpeech test clean/other subsets respectively.
facebook/wav2vec2-large-960h
Automatic Speech Recognition • Updated • 80.5k • 28Note The Wav2Vec 2.0 "large" model pre-trained and fine-tuned on 960 hours of LibriSpeech ASR data.
facebook/wav2vec2-base-960h
Automatic Speech Recognition • Updated • 1.42M • 312Note The Wav2Vec 2.0 "base" model pre-trained and fine-tuned on 960 hours of LibriSpeech ASR data.
facebook/wav2vec2-base-100h
Automatic Speech Recognition • Updated • 1.39k • 6Note The Wav2Vec 2.0 "base" model pre-trained on 960 hours of un-labelled LibriSpeech ASR data, and fine-tuned on 100 hours of labelled LibriSpeech ASR data.
facebook/wav2vec2-large-lv60
Updated • 7.65k • 8Note The Wav2Vec 2.0 "large" model pre-trained on 53k hours of un-labelled data from the LibriSpeech and LibriVox (LV) corpora.
facebook/wav2vec2-large
Updated • 6.22k • 5Note The Wav2Vec 2.0 "large" model pre-trained on 960 hours of un-labelled LibriSpeech ASR data.
facebook/wav2vec2-base
Updated • 505k • 91Note The Wav2Vec 2.0 "base" model pre-trained on 960 hours of un-labelled LibriSpeech ASR data.
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
Paper • 2006.11477 • Published • 6Note The wav2vec 2.0 paper, accepted to NeurIPS 2020.