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--- |
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license: mit |
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viewer: false |
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task_categories: |
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- text-classification |
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- text-to-speech |
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--- |
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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' |
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- 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** |
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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** |
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- Applied Unicode NFD normalization. |
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- Kept only clean sentences containing: `\n '!,.?אבגדהוזחטיךכלםמןנסעףפץצקרשת ` |
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- Result: 7.8 million unvoweled sentences. |
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3. **Added Niqqud & Metadata** |
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- Used Dicta's diacritizer to add niqqud. |
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- Extracted morphological metadata for each word (prefixes, POS, stress, etc.). |
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- Saved to CSV: each row = full sentence, with aligned niqqud and metadata. |
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4. **Syllabification & Annotation** |
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- Marked stress and prefixes using the metadata. |
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- Split words into syllables. |
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5. **Filtered Long Sentences** |
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- Removed outlier-length sentences using the IQR method. |
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- Result: 5 million cleaned and processed sentences. |
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6. **Shva Handling** |
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- Applied heuristic rules to identify vocalic shva (e.g., in common prefixes like למנרי). |
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- Note: Rules are approximate and not strictly based on Academy of Hebrew standards. |
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7. **Built Word Frequency Lexicon** |
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- Stripped prefixes from words. |
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- Sorted unique forms by frequency of appearance. |
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8. **Manual Corrections** |
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- Identified ~1,000 high-frequency words with common stress/shva errors. |
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- Corrected them manually to improve data quality. |
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- These corrections impact hundreds of thousands to millions of occurrences. |
