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transformers/docs/source/en/model_doc/unispeech.md

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+*This model was released on 2021-01-19 and added to Hugging Face Transformers on 2021-10-26.*
+
+# UniSpeech
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+The UniSpeech model was proposed in [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://huggingface.co/papers/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