huggingface--transformers/docs/source/en/model_doc/speecht5.md

This model was published in HF papers on 2021-10-14 and contributed to Hugging Face Transformers on 2023-02-03.

SpeechT5

Overview

The SpeechT5 model was proposed in SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.

The abstract from the paper is the following:

Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets. After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder. Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text. To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder. Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification.

This model was contributed by Matthijs. The original code can be found here.

SpeechT5Config

autodoc SpeechT5Config

SpeechT5HifiGanConfig

autodoc SpeechT5HifiGanConfig

SpeechT5Tokenizer

autodoc SpeechT5Tokenizer - call - save_vocabulary - decode - batch_decode

SpeechT5FeatureExtractor

autodoc SpeechT5FeatureExtractor - call

SpeechT5Processor

autodoc SpeechT5Processor - call - pad - from_pretrained - save_pretrained - batch_decode - decode

SpeechT5Model

autodoc SpeechT5Model - forward

SpeechT5ForSpeechToText

autodoc SpeechT5ForSpeechToText - forward

SpeechT5ForTextToSpeech

autodoc SpeechT5ForTextToSpeech - forward - generate

SpeechT5ForSpeechToSpeech

autodoc SpeechT5ForSpeechToSpeech - forward - generate_speech

SpeechT5HifiGan

autodoc SpeechT5HifiGan - forward