  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 zero-shot transfer setting without the need
for any fine-tuning. 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.
