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UniSpeech
Overview
The UniSpeech model was proposed in UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data 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.
This model was contributed by patrickvonplaten. The Authors' code can be
found here.
Usage 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].
Resources
Audio classification task guide
Automatic speech recognition task guide
UniSpeechConfig
[[autodoc]] UniSpeechConfig
UniSpeech specific outputs
[[autodoc]] models.unispeech.modeling_unispeech.UniSpeechForPreTrainingOutput
UniSpeechModel
[[autodoc]] UniSpeechModel
- forward
UniSpeechForCTC
[[autodoc]] UniSpeechForCTC
- forward
UniSpeechForSequenceClassification
[[autodoc]] UniSpeechForSequenceClassification
- forward
UniSpeechForPreTraining
[[autodoc]] UniSpeechForPreTraining
- forward |