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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

docs/source/en/model_doc/unispeech-sat.md

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+
+# UniSpeech-SAT
+
+## Overview
+
+The UniSpeech-SAT model was proposed in [UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware
+Pre-Training](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen,
+Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu .
+
+The abstract from the paper is the following:
+
+*Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled
+data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in
+speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In
+this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are
+introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to
+the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function.
+Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where
+additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed
+methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves
+state-of-the-art performance in universal representation learning, especially for speaker identification oriented
+tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training
+dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks.*
+
+Tips:
+
+- UniSpeechSat 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.
+- UniSpeechSat model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
+  decoded using [`Wav2Vec2CTCTokenizer`].
+- UniSpeechSat performs especially well on speaker verification, speaker identification, and speaker diarization tasks.
+
+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-SAT).
+
+## Documentation resources
+
+- [Audio classification task guide](../tasks/audio_classification)
+- [Automatic speech recognition task guide](../tasks/asr)
+
+## UniSpeechSatConfig
+
+[[autodoc]] UniSpeechSatConfig
+
+## UniSpeechSat specific outputs
+
+[[autodoc]] models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput
+
+## UniSpeechSatModel
+
+[[autodoc]] UniSpeechSatModel
+    - forward
+
+## UniSpeechSatForCTC
+
+[[autodoc]] UniSpeechSatForCTC
+    - forward
+
+## UniSpeechSatForSequenceClassification
+
+[[autodoc]] UniSpeechSatForSequenceClassification
+    - forward
+
+## UniSpeechSatForAudioFrameClassification
+
+[[autodoc]] UniSpeechSatForAudioFrameClassification
+    - forward
+
+## UniSpeechSatForXVector
+
+[[autodoc]] UniSpeechSatForXVector
+    - forward
+
+## UniSpeechSatForPreTraining
+
+[[autodoc]] UniSpeechSatForPreTraining
+    - forward