results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech
recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all
testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task,
i.e., a relative word error rate reduction of 6% against the previous approach.
Tips:
UniSpeech is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please
  use [Wav2Vec2Processor] for the feature extraction.
UniSpeech model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
  decoded using [Wav2Vec2CTCTokenizer].
This model was contributed by patrickvonplaten. The Authors' code can be
