init

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

@@ -0,0 +1,102 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2021-10-12 and added to Hugging Face Transformers on 2021-10-26.*
+
+# UniSpeech-SAT
+
+<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-SAT model was proposed in [UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware
+Pre-Training](https://huggingface.co/papers/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 unsupervisedly 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.*
+
+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).
+
+## Usage 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.
+
+> [!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)
+
+## 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