Dataset Card for Unsupervised Peoples Speech

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.

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 Structure

Audio folders

Folders with the raw audio. We split this into two directories because Hugging Face does not support more than 10,000 files in a single directory.

Dataset Creation

Source Data

Initial Data Collection and Normalization

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

Preprocessing

No preprocessing was done.

Annotations

Annotation process

No manual annotation is done. We download only source audio.

In particular, there is no "forced alignment" or "segmentation" done on this dataset.

Personal and Sensitive Information

Several of our sources are legal and government proceedings, spoken stories, 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

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.

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},
}