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--- |
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language: |
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- eng |
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pretty_name: Unsupervised Peoples Speech |
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tags: |
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- audio |
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- unsupervised |
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task_categories: |
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- automatic-speech-recognition |
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- audio-classification |
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task_ids: |
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- audio-language-identification |
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viewer: false |
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--- |
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# Dataset Card for Unsupervised Peoples Speech |
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## Table of Contents |
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- [Dataset Card for Unuspervised Peoples Speech](#dataset-card-for-unsupervised-peoples-speech) |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Dataset Structure](#dataset-structure) |
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- [Relevant Statistics](#relevant-statistics) |
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- [Dataset Creation](#dataset-creation) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Additional Information](#additional-information) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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## Dataset Description |
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### Dataset Summary |
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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. |
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- **Point of Contact:** [MLCommons Datasets Discord](https://discord.gg/8ZVyxwpv) |
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## Dataset Structure |
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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. |
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- All tar files are stored in either in the `audio` or `audio2` directories. |
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- The `licenses.jsonl` file contains the license information for each audio file. |
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## Relevant Statistics |
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#### Duration Distribution |
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Most of the audios range between 1 and 10 minutes in length, with only 14 of them exceeding the 100 hour mark. |
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#### Sample Rates |
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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. |
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## Dataset Creation |
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### Source Data |
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Data was downloaded via the archive.org API. No data inference was done. No preprocessing was done. |
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### Annotations |
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No manual annotation is done. We download only source audio. In particular, there is no "forced alignment" or "segmentation" done on this dataset. |
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## Considerations for Using the Data |
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Our data is downloaded from archive.org. As such, the data is biased towards whatever users decide to upload there. |
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Almost all of our data is American accented English. |
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## Additional Information |
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### Licensing Information |
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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/ |
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### Citation Information |
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Please cite |
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``` |
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@article{USP, |
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author={Daniel Galvez and |
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Ryan Hileman and |
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Rafael Mosquera and |
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Juan Ciro and |
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Kurt Bollacker and |
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Peter Mattson and |
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David Kanter}, |
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title = {Unsupervised People's Speech (The Million Hour Audio Dataset)}, |
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year = {2023}, |
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url = {https://huggingface.co/datasets/MLCommons/peoples_speech}, |
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} |
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``` |