The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage
Daniel Galvez
G. Diamos
Juan Ciro
Juan Felipe Cerón
Keith Achorn
Anjali Gopi
David Kanter
Maximilian Lam
Mark Mazumder
Vijay Janapa Reddi

Abstract
The People's Speech is a free-to-download 30,000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset). The data is collected via searching the Internet for appropriately licensed audio data with existing transcriptions. We describe our data collection methodology and release our data collection system under the Apache 2.0 license. We show that a model trained on this dataset achieves a 9.98% word error rate on Librispeech's test-clean test set.Finally, we discuss the legal and ethical issues surrounding the creation of a sizable machine learning corpora and plans for continued maintenance of the project under MLCommons's sponsorship.
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