README.md

---
language:
- he
license: other
size_categories:
- 1M<n<10M
task_categories:
- audio-classification
- voice-activity-detection
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
dataset_info:
  features:
  - name: source
    dtype: string
  - name: episode
    dtype: string
  - name: uuid
    dtype: string
  - name: text
    dtype: string
  - name: attrs
    struct:
    - name: segments
      list:
      - name: avg_logprob
        dtype: float64
      - name: compression_ratio
        dtype: float64
      - name: end
        dtype: float64
      - name: id
        dtype: int64
      - name: no_speech_prob
        dtype: float64
      - name: seek
        dtype: int64
      - name: start
        dtype: float64
      - name: text
        dtype: string
  splits:
  - name: train
    num_bytes: 1290457176
    num_examples: 2183042
  download_size: 421521923
  dataset_size: 1290457176
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

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.

**audio-transcripts** contains transcriptions for each snippet in the audio-vad dataset.

The audio-base dataset contains data from the following sources:
* Geekonomy (Podcast, https://geekonomy.net)
* HaCongress (Podcast, https://hacongress.podbean.com/)
* Idan Eretz's YouTube channel (https://www.youtube.com/@IdanEretz)
* Moneytime (Podcast, https://money-time.co.il)
* Mor'e Nevohim (Podcast, https://open.spotify.com/show/1TZeexEk7n60LT1SlS2FE2?si=937266e631064a3c)
* Yozevitch's World (Podcast, https://www.yozevitch.com/yozevitch-podcast)
* NETfrix (Podcast, https://netfrix.podbean.com)
* On Meaning (Podcast, https://mashmaut.buzzsprout.com)
* Shnekel (Podcast, https://www.shnekel.live)
* Bite-sized History (Podcast, https://soundcloud.com/historia-il)
* Tziun 3 (Podcast, https://tziun3.co.il)
* Academia Israel (https://www.youtube.com/@academiaisrael6115)
* Shiluv Maagal (https://www.youtube.com/@ShiluvMaagal)

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}
}
```