TorchAudio 2.1: Advancing Speech Recognition, Self-Supervised Learning, and Audio Processing Components for Pytorch-Reference-Cited by-同舟云学术

TorchAudio 2.1: Advancing Speech Recognition, Self-Supervised Learning, and Audio Processing Components for Pytorch

Published:2023-12-16 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
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Author:

Hwang Jeff¹,Hira Moto¹,Chen Caroline¹,Zhang Xiaohui¹,Ni Zhaoheng¹,Sun Guangzhi²,Ma Pingchuan¹,Huang Ruizhe³,Pratap Vineel¹,Zhang Yuekai⁴,Kumar Anurag¹,Yu Chin-Yun⁵,Zhu Chuang⁴,Liu Chunxi¹,Kahn Jacob¹,Ravanelli Mirco⁶,Sun Peng⁴,Watanabe Shinji⁷,Shi Yangyang¹,Tao Yumeng¹

Affiliation:

1. Meta

2. University of Cambridge

3. Johns Hopkins University

4. NVIDIA

5. Queen Mary University of London

6. Concordia University

7. Carnegie Mellon University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10388490/10389614/10389648.pdf?arnumber=10389648

Reference49 articles.

1. TensorFlow: Large-scale machine learning on heterogeneous systems;Abadi;2015, Software available from tensorflow.org

2. Torchaudio: Building Blocks for Audio and Speech Processing

3. wav2vec 2.0: A framework for self-supervised learning of speech representations;Baevski;Advances in Neural Information Processing Systems,2020

4. HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units

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