| | --- |
| | 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. |
| | - The `lang_id_results.jsonl` file contains the predicted language for all files using Whisper Large V3. |
| | - The `vad_results.jsonl` file containes timestamps where voice was detected using Silero VAD. |
| |
|
| | ## 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}, |
| | } |
| | ``` |
