# UniSpeech

## Overview The 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, Michael Zeng, 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 both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture information more correlated with phonetic structures and improve the generalization across languages and domains. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The 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.* This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The Authors' code can be found [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`]. > [!NOTE] > The `head_mask` argument is ignored when using all attention implementation other than "eager". If you have a `head_mask` and want it to have effect, load the model with `XXXModel.from_pretrained(model_id, attn_implementation="eager")` ## 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