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---
language:
- eng
pretty_name: Unsupervised Peoples Speech
tags:
- audio
- unsupervised
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- audio-language-identification
viewer: false
---
# Dataset Card for Unsupervised Peoples Speech
## Table of Contents
- [Dataset Card for Unuspervised Peoples Speech](#dataset-card-for-unsupervised-peoples-speech)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Relevant Statistics](#relevant-statistics)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
### Dataset Summary
The Unsupervised Peoples Speech Dataset is a compilation of audiofiles extracted from Archive.org that is licensed for academic and commercial usage under CC-BY and CC-BY-SA licenses. It includes more than one million hours of audio with a diverse set of speakers.
- **Point of Contact:** [MLCommons Datasets Discord](https://discord.gg/8ZVyxwpv)
## Dataset Structure
This dataset is a collection of audio files that have been stored as tar files, each containing a set of audio files. On average, each tar file is 5GB in size.
- All tar files are stored in either in the `audio` or `audio2` directories.
- The `licenses.jsonl` file contains the license information for each audio file.
## Relevant Statistics
#### Duration Distribution
Most of the audios range between 1 and 10 minutes in length, with only 14 of them exceeding the 100 hour mark.
#### Sample Rates
99% of the audio in the dataset has a 44.1Khz sample rate, and the remaining audio varies from the more common 16Khz, 24Khz and 48 Khz to custom sample rates.
## Dataset Creation
### Source Data
Data was downloaded via the archive.org API. No data inference was done. No preprocessing was done.
### Annotations
No manual annotation is done. We download only source audio. In particular, there is no "forced alignment" or "segmentation" done on this dataset.
## Considerations for Using the Data
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.
## Additional Information
### Licensing Information
The source data contains data under CC-BY-SA and CC-BY licenses. We license this dataset under https://creativecommons.org/licenses/by-sa/4.0/
### Citation Information
Please cite
```
@article{USP,
author={Daniel Galvez and
Ryan Hileman and
Rafael Mosquera and
Juan Ciro and
Kurt Bollacker and
Peter Mattson and
David Kanter},
title = {Unsupervised People's Speech (The Million Hour Audio Dataset)},
year = {2023},
url = {https://huggingface.co/datasets/MLCommons/peoples_speech},
}
``` |