---
license: mit
viewer: false
task_categories:
- text-classification
- text-to-speech
---


Dataset for [phonikud](https://github.com/thewh1teagle/phonikud) model


The datasets contains millions of clean Hebrew sentences marked with nikud and additional phonetics marks.

The format is `text<TAB>phonemes`


## Changelog (knesset_nikud - 5 millions lines)

### v6

- Fix words with `Oto` such as `Otobus` or `Otomati`

### v5

- Fix 'Kshe' instead of 'Keshe'
- Fix ALL shva prefix in knesset

### v4

- Remove too long lines based on IQR forumula


### v3

- Add Shva Na by coding rules (eg `למנרי`)

### v2

- V1 converted to txt by syllables partitioning and marking hatama

### v1

- Base csv from Dicta with Hatama and nikud


## Changelog (hedc4 - 2 million lines)

### v1

- Made new dataset from https://huggingface.co/datasets/thewh1teagle/heb-text with text<TAB>phonemes


### 🛠️ Knesset Dataset Creation Steps

1. **Sourced Raw Text**  
   Downloaded Hebrew parliamentary tweets from the [IsraParlTweet dataset](https://huggingface.co/datasets/guymorlan/IsraParlTweet) (CC-BY-4.0).

2. **Normalized & Filtered**  
   - Applied Unicode NFD normalization.
   - Kept only clean sentences containing: `\n '!,.?אבגדהוזחטיךכלםמןנסעףפץצקרשת `  
   - Result: 7.8 million unvoweled sentences.

3. **Added Niqqud & Metadata**  
   - Used Dicta's diacritizer to add niqqud.
   - Extracted morphological metadata for each word (prefixes, POS, stress, etc.).
   - Saved to CSV: each row = full sentence, with aligned niqqud and metadata.

4. **Syllabification & Annotation**  
   - Marked stress and prefixes using the metadata.
   - Split words into syllables.

5. **Filtered Long Sentences**  
   - Removed outlier-length sentences using the IQR method.
   - Result: 5 million cleaned and processed sentences.

6. **Shva Handling**  
   - Applied heuristic rules to identify vocalic shva (e.g., in common prefixes like למנרי).
   - Note: Rules are approximate and not strictly based on Academy of Hebrew standards.

7. **Built Word Frequency Lexicon**  
   - Stripped prefixes from words.
   - Sorted unique forms by frequency of appearance.

8. **Manual Corrections**  
   - Identified ~1,000 high-frequency words with common stress/shva errors.
   - Corrected them manually to improve data quality.
   - These corrections impact hundreds of thousands to millions of occurrences.