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docs/source/model_doc/unispeech.mdx

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+<!--Copyright 2021 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# 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.*
+
+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](https://huggingface.co/patrickvonplaten). The Authors' code can be
+found [here](https://github.com/microsoft/UniSpeech/tree/main/UniSpeech).
+
+
+## UniSpeechConfig
+
+[[autodoc]] UniSpeechConfig
+
+## UniSpeech specific outputs
+
+[[autodoc]] models.unispeech.modeling_unispeech.UniSpeechBaseModelOutput
+
+[[autodoc]] models.unispeech.modeling_unispeech.UniSpeechForPreTrainingOutput
+
+## UniSpeechModel
+
+[[autodoc]] UniSpeechModel
+    - forward
+
+## UniSpeechForCTC
+
+[[autodoc]] UniSpeechForCTC
+    - forward
+
+## UniSpeechForSequenceClassification
+
+[[autodoc]] UniSpeechForSequenceClassification
+    - forward
+
+## UniSpeechForPreTraining
+
+[[autodoc]] UniSpeechForPreTraining
+    - forward