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* unispeech * add copy from * remove hubert copy from * finish for today * add unispeech-sat * adapt more * up * up * up * up * add modeling * add tests * up * up * finish * up * Apply suggestions from code review * up * up * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * up * up Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
89 lines
4.1 KiB
ReStructuredText
89 lines
4.1 KiB
ReStructuredText
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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
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The UniSpeech model was proposed in `UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
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<https://arxiv.org/abs/2101.07597>`__ by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael
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Zeng, Xuedong Huang .
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The abstract from the paper is the following:
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*In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both
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unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive
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self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture
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information more correlated with phonetic structures and improve the generalization across languages and domains. We
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evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The
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results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech
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recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all
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testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task,
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i.e., a relative word error rate reduction of 6% against the previous approach.*
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Tips:
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- UniSpeech is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please
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use :class:`~transformers.Wav2Vec2Processor` for the feature extraction.
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- UniSpeech 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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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>`__.
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UniSpeechConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechConfig
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:members:
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UniSpeech specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.models.unispeech.modeling_unispeech.UniSpeechBaseModelOutput
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:members:
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.. autoclass:: transformers.models.unispeech.modeling_unispeech.UniSpeechForPreTrainingOutput
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:members:
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UniSpeechModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechModel
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:members: forward
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UniSpeechForCTC
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechForCTC
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:members: forward
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UniSpeechForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechForSequenceClassification
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:members: forward
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UniSpeechForPreTraining
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.UniSpeechForPreTraining
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:members: forward
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