README.md

metadata

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

Dataset for 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 textphonemes

🛠️ Knesset Dataset Creation Steps

1. Sourced Raw Text
   Downloaded Hebrew parliamentary tweets from the IsraParlTweet dataset (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.