of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to
a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong
individual models. Analysis shows that the latent discrete speech representations are shared across languages with
increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing
XLSR-53, a large model pretrained in 53 languages.
Tips:
XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be
  decoded using [Wav2Vec2CTCTokenizer].
XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to Wav2Vec2's documentation page.
