Datasets:

MLCommons
/

peoples_speech_v1.0

	---
	annotations_creators:
	- crowdsourced
	- machine-generated
	language_creators:
	- crowdsourced
	- machine-generated
	language:
	- en
	license:
	- cc-by-2.0
	- cc-by-2.5
	- cc-by-3.0
	- cc-by-4.0
	- cc-by-sa-3.0
	- cc-by-sa-4.0
	multilinguality:
	- monolingual
	pretty_name: People's Speech
	size_categories:
	- 1T<n
	source_datasets:
	- original
	task_categories:
	- automatic-speech-recognition
	task_ids:
	- speech-recognition
	- robust-speech-recognition
	- noisy-speech-recognition
	---

	# Dataset Card for People's Speech

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-instances)
	- [Data Splits](#data-instances)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)

	## Dataset Description

	- Homepage: https://mlcommons.org/en/peoples-speech/
	- Repository: https://github.com/mlcommons/peoples-speech
	- Paper: https://arxiv.org/abs/2111.09344
	- Leaderboard: [Needs More Information]
	- Point of Contact: [datasets@mlcommons.org](mailto:datasets@mlcommons.org)

	### Dataset Summary

	The People's Speech Dataset is among the world's largest English speech recognition corpus today that is licensed for academic and commercial usage under CC-BY-SA and CC-BY 4.0. It includes 30,000+ hours of transcribed speech in English languages with a diverse set of speakers. This open dataset is large enough to train speech-to-text systems and crucially is available with a permissive license.

	### Supported Tasks and Leaderboards

	[Needs More Information]

	### Languages

	English

	## Dataset Structure

	### Data Instances

	{
	"id": "gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac",
	"audio": {
	"path": "gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac"
	"array": array([-6.10351562e-05, ...]),
	"sampling_rate": 16000
	}
	"duration_ms": 14490,
	"text": "contends that the suspension clause requires a [...]"
	}

	### Data Fields

	{
	"id": datasets.Value("string"),
	"audio": datasets.Audio(sampling_rate=16_000),
	"duration_ms": datasets.Value("int32"),
	"text": datasets.Value("string"),
	}

	### Data Splits

	We provide the following configurations for the dataset: `cc-by-clean`, `cc-by-dirty`, `cc-by-sa-clean`, `cc-by-sa-dirty`, and `microset`. We don't provide splits for any of the configurations.

	## Dataset Creation

	### Curation Rationale

	See our [paper](https://arxiv.org/abs/2111.09344).

	### Source Data

	#### Initial Data Collection and Normalization

	Data was downloaded via the archive.org API. No data inference was done.

	#### Who are the source language producers?

	[Needs More Information]

	### Annotations

	#### Annotation process

	No manual annotation is done. We download only source audio with already existing transcripts.

	#### Who are the annotators?

	For the test and dev sets, we paid native American English speakers to do transcriptions. We do not know the identities of the transcriptionists for data in the training set. For the training set, we have noticed that some transcriptions are likely to be the output of automatic speech recognition systems.

	### Personal and Sensitive Information

	Several of our sources are legal and government proceedings, spoken histories, speeches, and so on. Given that these were intended as public documents and licensed as such, it is natural that the involved individuals are aware of this.

	## Considerations for Using the Data

	### Social Impact of Dataset

	The dataset could be used for speech synthesis. However, this requires careful cleaning of the dataset, as background noise is not tolerable for speech synthesis.

	The dataset could be used for keyword spotting tasks as well. In particular, this is good use case for the non-English audio in the dataset.

	Our sincere hope is that the large breadth of sources our dataset incorporates reduces existing quality of service issues today, like speech recognition system’s poor understanding of non-native English accents. We cannot think of any unfair treatment that come from using this dataset at this time.


	### Discussion of Biases

	Our data is downloaded from archive.org. As such, the data is biased towards whatever users decide to upload there.

	Almost all of our data is American accented English.

	### Other Known Limitations

	As of version 1.0, a portion of data in the training, test, and dev sets is poorly aligned. Specifically, some words appear in the transcript, but not the audio, or some words appear in the audio, but not the transcript. We are working on it.

	## Additional Information

	### Dataset Curators

	[Needs More Information]

	### Licensing Information

	We provide CC-BY and CC-BY-SA subsets of the dataset.

	### Citation Information

	Please cite:

	```
	@article{DBLP:journals/corr/abs-2111-09344,
	author = {Daniel Galvez and
	Greg Diamos and
	Juan Ciro and
	Juan Felipe Cer{\'{o}}n and
	Keith Achorn and
	Anjali Gopi and
	David Kanter and
	Maximilian Lam and
	Mark Mazumder and
	Vijay Janapa Reddi},
	title = {The People's Speech: {A} Large-Scale Diverse English Speech Recognition
	Dataset for Commercial Usage},
	journal = {CoRR},
	volume = {abs/2111.09344},
	year = {2021},
	url = {https://arxiv.org/abs/2111.09344},
	eprinttype = {arXiv},
	eprint = {2111.09344},
	timestamp = {Mon, 22 Nov 2021 16:44:07 +0100},
	biburl = {https://dblp.org/rec/journals/corr/abs-2111-09344.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	```