The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage

By Daniel Galvez, G. Diamos, Juan Ciro, Juan Felipe Cerón, Keith Achorn, Anjali Gopi, David Kanter, Maximilian Lam, Mark Mazumder, Vijay Janappa Reddi. 2021

149 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Evaluation & Benchmarks, Speech & Audio

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Evaluation & Benchmarks

In Speech & Audio

Citations in the corpus

Robust Speech Recognition via Large-Scale Weak Supervision (2022, neutral): “By relaxing the requirement of gold-standard human-validated transcripts, Chen et al. (2021) and Galvez et al. (2021) make use of sophisticated automated pipelines to scale weakly supervised speech recognition to 10,000 and 30,000 hours of noisier training data.”
Libriheavy: A 50,000 Hours ASR Corpus with Punctuation Casing and Context (2023, background): “In recent years, there are also large-scale corpora like GigaSpeech [9], People’s Speech [10] and MLS [11].”
WENETSPEECH: A 10000+ Hours Multi-Domain Mandarin Corpus for Speech Recognition (2021, background): “To close the gap between industrial system and academic research, we notice that several large-scale multi-domain English corpora, including The People’s Speech [24], MLS [25] and GigaSpeech [26], are made available recently.”
Adaptive Sampling Strategies to Construct Equitable Training Datasets (2022, methodology): “In addition to approaches tailored to improving the equity of models trained on static datasets, it is important to consider issues that arise at various stages of the training and deployment of statistical models, including constructing equitable training sets [27, 27, 31, 31, 44, 44, 51, 51], inte”
Textually Pretrained Speech Language Models (2023, methodology): “All SpeechLMs are optimized using a collection of publicly available academic speech datasets: LibriSpeech (LS) [Panayotov et al., 2015], LibriLight (LL) [Kahn et al., 2020], Spotify pod-casts [Clifton et al., 2020], People dataset [Galvez et al., 2021], and VoxPopuli [Wang et al., 2021a].”
Lhotse: a speech data representation library for the modern deep learning ecosystem (2021, methodology): “000 hours of recorded speech freely available with the recent releases of LibriSpeech [13], Multilingual LibriSpeech [15], LibriLight [9], GigaSpeech [7], SPGISpeech [12], The People’s Speech [8], and others.”