| --- |
| license: mit |
| viewer: false |
| task_categories: |
| - text-classification |
| - text-to-speech |
| --- |
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| Dataset for [phonikud](https://github.com/thewh1teagle/phonikud) model |
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| The datasets contains millions of clean Hebrew sentences marked with nikud and additional phonetics marks. |
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| The format is `text<TAB>phonemes` |
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| ## Changelog (knesset_nikud - 5 millions lines) |
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| ### v6 |
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| - Fix words with `Oto` such as `Otobus` or `Otomati` |
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| ### v5 |
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| - Fix 'Kshe' instead of 'Keshe' |
| - Fix ALL shva prefix in knesset |
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| ### v4 |
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| - Remove too long lines based on IQR forumula |
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| ### v3 |
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| - Add Shva Na by coding rules (eg `למנרי`) |
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| ### v2 |
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| - V1 converted to txt by syllables partitioning and marking hatama |
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| ### v1 |
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| - Base csv from Dicta with Hatama and nikud |
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| ## Changelog (hedc4 - 2 million lines) |
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| ### v1 |
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| - Made new dataset from https://huggingface.co/datasets/thewh1teagle/heb-text with text<TAB>phonemes |
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| ### 🛠️ Knesset Dataset Creation Steps |
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| 1. **Sourced Raw Text** |
| Downloaded Hebrew parliamentary tweets from the [IsraParlTweet dataset](https://huggingface.co/datasets/guymorlan/IsraParlTweet) (CC-BY-4.0). |
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| 2. **Normalized & Filtered** |
| - Applied Unicode NFD normalization. |
| - Kept only clean sentences containing: `\n '!,.?אבגדהוזחטיךכלםמןנסעףפץצקרשת ` |
| - Result: 7.8 million unvoweled sentences. |
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| 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. |
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| 4. **Syllabification & Annotation** |
| - Marked stress and prefixes using the metadata. |
| - Split words into syllables. |
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| 5. **Filtered Long Sentences** |
| - Removed outlier-length sentences using the IQR method. |
| - Result: 5 million cleaned and processed sentences. |
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| 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. |
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| 7. **Built Word Frequency Lexicon** |
| - Stripped prefixes from words. |
| - Sorted unique forms by frequency of appearance. |
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| 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. |
