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* Models * Squashed commit of the following: commit 72278e1e931a16d0879acc77f65762f3364833d0 Author: anton-l <aglozhkov@gmail.com> Date: Fri Dec 10 21:45:08 2021 +0300 * Add unispeech heads * Add sd/sv automodels * Docs cleanup * Fix docstrings * rename xvector classes * examples * Tests cleanup * Style * Better checkpoints for tests * leftover docs * apply review suggestions * Style + init tests * Update unispeech-sat tdnn downsampling
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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UniSpeech-SAT
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The UniSpeech-SAT model was proposed in `UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware
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Pre-Training <https://arxiv.org/abs/2110.05752>`__ by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen,
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Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu .
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The abstract from the paper is the following:
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*Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled
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data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in
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speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In
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this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are
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introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to
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the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function.
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Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where
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additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed
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methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves
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state-of-the-art performance in universal representation learning, especially for speaker identification oriented
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tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training
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dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks.*
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Tips:
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- UniSpeechSat is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
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Please use :class:`~transformers.Wav2Vec2Processor` for the feature extraction.
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- UniSpeechSat model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
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decoded using :class:`~transformers.Wav2Vec2CTCTokenizer`.
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- UniSpeechSat performs especially well on speaker verification, speaker identification, and speaker diarization tasks.
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This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The Authors' code can be
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found `here <https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT>`__.
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UniSpeechSatConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechSatConfig
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:members:
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UniSpeechSat specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatBaseModelOutput
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:members:
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.. autoclass:: transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput
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:members:
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UniSpeechSatModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechSatModel
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:members: forward
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UniSpeechSatForCTC
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechSatForCTC
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:members: forward
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UniSpeechSatForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechSatForSequenceClassification
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:members: forward
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UniSpeechSatForAudioFrameClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechSatForAudioFrameClassification
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:members: forward
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UniSpeechSatForXVector
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechSatForXVector
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:members: forward
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UniSpeechSatForPreTraining
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechSatForPreTraining
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:members: forward
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