Datasets:
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
size_categories:
- 1T<n
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: People's Speech
tags:
- robust-speech-recognition
- noisy-speech-recognition
- speech-recognition
dataset_info:
- config_name: clean_sa
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration_ms
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 75267509124.558
num_examples: 257093
- name: validation
num_bytes: 2075929254.254
num_examples: 18622
- name: test
num_bytes: 3894954757.41
num_examples: 34898
download_size: 72518549222
dataset_size: 81238393136.222
- config_name: default
features:
- name: id
dtype: string
- name: audio
dtype: audio
- name: duration_ms
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 401733771193.124
num_examples: 1501271
- name: validation
num_bytes: 2459781412.24
num_examples: 18622
- name: test
num_bytes: 4324307722.96
num_examples: 34898
download_size: 398570070831
dataset_size: 408517860328.32404
- config_name: dirty_sa
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration_ms
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 163776914241.91
num_examples: 548014
- name: validation
num_bytes: 2075929254.254
num_examples: 18622
- name: test
num_bytes: 3894954757.41
num_examples: 34898
download_size: 149326092074
dataset_size: 169747798253.574
- config_name: test
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration_ms
dtype: int32
- name: text
dtype: string
splits:
- name: test
num_bytes: 3894954757.41
num_examples: 34898
download_size: 4087772459
dataset_size: 3894954757.41
- config_name: validation
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration_ms
dtype: int32
- name: text
dtype: string
splits:
- name: validation
num_bytes: 2075929254.254
num_examples: 18622
download_size: 2335244149
dataset_size: 2075929254.254
configs:
- config_name: clean_sa
data_files:
- split: train
path: clean_sa/train-*
- split: validation
path: clean_sa/validation-*
- split: test
path: clean_sa/test-*
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: dirty_sa
data_files:
- split: train
path: dirty_sa/train-*
- split: validation
path: dirty_sa/validation-*
- split: test
path: dirty_sa/test-*
- config_name: test
data_files:
- split: test
path: test/test-*
- config_name: validation
data_files:
- split: validation
path: validation/validation-*
Dataset Card for People's Speech
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional 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
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.
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}
}