docs/source/en/model_doc/unispeech.md
This model was released on 2021-01-19 and added to Hugging Face Transformers on 2021-10-26.
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.
Wav2Vec2Processor] for the feature extraction.Wav2Vec2CTCTokenizer].[[autodoc]] UniSpeechConfig
[[autodoc]] models.unispeech.modeling_unispeech.UniSpeechForPreTrainingOutput
[[autodoc]] UniSpeechModel - forward
[[autodoc]] UniSpeechForCTC - forward
[[autodoc]] UniSpeechForSequenceClassification - forward
[[autodoc]] UniSpeechForPreTraining - forward