metadata
license: other
task_categories:
- audio-classification
- voice-activity-detection
language:
- he
size_categories:
- 1K<n<10K
extra_gated_prompt: >-
You agree to the following license terms:
This material and data is licensed under the terms of the Creative Commons
Attribution 4.0 International License (CC BY 4.0), The full text of the CC-BY
4.0 license is available at https://creativecommons.org/licenses/by/4.0/.
Notwithstanding the foregoing, this material and data may only be used,
modified and distributed for the express purpose of training AI models, and
subject to the foregoing restriction. In addition, this material and data may
not be used in order to create audiovisual material that simulates the voice
or likeness of the specific individuals appearing or speaking in such
materials and data (a “deep-fake”). To the extent this paragraph is
inconsistent with the CC-BY-4.0 license, the terms of this paragraph shall
govern.
By downloading or using any of this material or data, you agree that the
Project makes no representations or warranties in respect of the data, and
shall have no liability in respect thereof. These disclaimers and limitations
are in addition to any disclaimers and limitations set forth in the CC-BY-4.0
license itself. You understand that the project is only able to make available
the materials and data pursuant to these disclaimers and limitations, and
without such disclaimers and limitations the project would not be able to make
available the materials and data for your use.
extra_gated_fields:
I have read the license, and agree to its terms: checkbox
ivrit.ai is a database of Hebrew audio and text content.
audio-base contains the raw, unprocessed sources.
audio-vad contains audio snippets generated by applying Silero VAD (https://github.com/snakers4/silero-vad) to the base dataset. v1 data is generated using silero-vad's default parameters. v2 data is generated using min_speech_duration_ms=2000 (milliseconds), and max_speech_duration_s=30 (seconds).
audio-transcripts contains transcriptions for each snippet in the audio-vad dataset.
You can find the full list of sources in this dataset under https://www.ivrit.ai/en/credits.
Paper: https://arxiv.org/abs/2307.08720
If you use our datasets, the following quote is preferable:
@misc{marmor2023ivritai,
title={ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development},
author={Yanir Marmor and Kinneret Misgav and Yair Lifshitz},
year={2023},
eprint={2307.08720},
archivePrefix={arXiv},
primaryClass={eess.AS}
}