UniSpeech
Source: transformers

# UniSpeech## OverviewThe UniSpeech model was proposed in [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, MichaelZeng, Xuedong Huang .The abstract from the paper is the following:*In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with bothunlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastiveself-supervised learning are conducted in a multi-task learning manner. The resultant representations can captureinformation more correlated with phonetic structures and improve the generalization across languages and domains. Weevaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. Theresults show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speechrecognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over alltesting 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.*This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The Authors' code can befound [here](https://github.com/microsoft/UniSpeech/tree/main/UniSpeech).## Usage 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`].## Resources- [Audio classification task guide](../tasks/audio_classification)- [Automatic speech recognition task guide](../tasks/asr)## UniSpeechConfig[[autodoc]] UniSpeechConfig## UniSpeech specific outputs[[autodoc]] models.unispeech.modeling_unispeech.UniSpeechForPreTrainingOutput## UniSpeechModel[[autodoc]] UniSpeechModel    - forward## UniSpeechForCTC[[autodoc]] UniSpeechForCTC    - forward## UniSpeechForSequenceClassification[[autodoc]] UniSpeechForSequenceClassification    - forward## UniSpeechForPreTraining[[autodoc]] UniSpeechForPreTraining    - forward