inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference
time, SEW reduces word error rate by 25-50% across different model sizes.
Tips:
SEW-D is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
SEWDForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
  using [Wav2Vec2CTCTokenizer].
This model was contributed by anton-l.
SEWDConfig
[[autodoc]] SEWDConfig
SEWDModel
[[autodoc]] SEWDModel
    - forward
SEWDForCTC
[[autodoc]] SEWDForCTC
    - forward
SEWDForSequenceClassification
[[autodoc]] SEWDForSequenceClassification
    - forward
