of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and
pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech
recognition with limited amounts of labeled data.
Tips:
Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded
  using [Wav2Vec2CTCTokenizer].
This model was contributed by patrickvonplaten.
Wav2Vec2Config
[[autodoc]] Wav2Vec2Config
Wav2Vec2CTCTokenizer
[[autodoc]] Wav2Vec2CTCTokenizer
    - call
    - save_vocabulary
Wav2Vec2FeatureExtractor
[[autodoc]] Wav2Vec2FeatureExtractor
    - call
Wav2Vec2Processor
