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 and multitask
supervision, the resulting models generalize well
to standard benchmarks and are often competitive
with prior fully supervised results but in a zeroshot transfer setting without the need for any finetuning. When compared to humans, the models
approach their accuracy and robustness. We are
releasing models and inference code to serve as
a foundation for further work on robust speech
processing.