5.3k citations · 5.6k across the 5 of their papers we have counts for
8 papers
Robust Speech Recognition via Large-Scale Weak Supervision
Alec Radford, Jong Wook Kim, Tao Xu +3
We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual…
Text and Code Embeddings by Contrastive Pre-Training
Arvind Neelakantan, Tao Xu, Raul Puri +22
Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use c…
Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications
Sandhini Agarwal, Gretchen Krueger, Jack Clark +3
Recently, there have been breakthroughs in computer vision ("CV") models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLI…
Learning Transferable Visual Models From Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy +9
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usab…
Jukebox: A Generative Model for Music
Prafulla Dhariwal, Heewoo Jun, Christine Payne +3
We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discre…
Adversarial Learning for Improved Onsets and Frames Music Transcription
Jong Wook Kim, Juan Pablo Bello
Automatic music transcription is considered to be one of the hardest problems in music information retrieval, yet recent deep learning approaches have achieved substantial improvem…