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

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].

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
• 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