| --- |
| language: |
| - en |
| license: mit |
| task_categories: |
| - text-generation |
| size_categories: |
| - n<1K |
| tags: |
| - transliteration |
| - diacritization |
| - arabic |
| - asr-correction |
| - sermon |
| - islamic-education |
| pretty_name: logical-transcripts |
| --- |
| |
| # logical-transcripts |
|
|
| Golden paired dataset for training models to transliterate Arabic Latin text into |
| scholarly diacritized form — built from a single recorded Islamic lecture |
| (Chapter 24, Lecture 16) with a raw ASR transcript and a human-polished scholarly |
| transcript. |
|
|
| Two artifacts are stored separately for provenance and review: |
|
|
| | File | Rows | Purpose | |
| |------|------|---------| |
| | `train.jsonl` | 203 | **Golden** — quality-filtered pairs for training | |
| | `bronze.jsonl` | 773 | **Bronze** — every aligned sentence pair before filtering, tagged with `category` + `diacritics` count for provenance/review | |
|
|
| ## Golden quality criteria |
|
|
| Rows in `train.jsonl` meet **both**: |
|
|
| 1. `input != output` (no identity rows) |
| 2. Output contains **≥ 2 distinct diacritized letters** — each diacritic-carrying base |
| letter counts once (`ā`, `ḥ`, `ṣ`, …), plus the `ʿ` / `ʾ` hamza-ʿayn spacing |
| modifier letters. |
|
|
| Markdown asterisks from the source transcript are stripped from outputs. |
|
|
| ## Schema |
|
|
| ```json |
| { |
| "instruction": "Transliterate the following Arabic Latin text to scholarly diacritized form:", |
| "input": "There was no athan.", |
| "output": "There was no aẓān." |
| } |
| ``` |
|
|
| The `instruction`/`input`/`output` schema follows the standard instruction-tuning |
| convention, so it can be concatenated with other transliteration datasets for |
| training. |
|
|
| ## Task |
|
|
| The `input` is raw, un-diacritized Arabic-as-spoken-in-Latin-script (including ASR |
| artifacts: stutters, mis-heard words, run-on sentences). The `output` is the |
| scholarly diacritized transliteration (macrons, sub-dots, hamza/ʿayn, word |
| corrections, cleaned punctuation). Rows therefore train **diacritization + ASR |
| correction** jointly — a broader task than a clean-input transliteration baseline. |
|
|
| ## Statistics |
|
|
| - 203 golden rows, 773 bronze rows |
| - Golden input length: median 90 chars, max 1737 |
| - Diacritic-letter distribution: 2×85, 3×53, 4×22, 5×23, 6×8, 7×9, 8×3 |
|
|
| ## Provenance |
|
|
| Source material: 8 `{input_text, output_text}` chunk pairs extracted from a single |
| recorded Islamic lecture (Chapter 24, Lecture 16). The concatenated inputs exactly |
| reconstruct the raw ASR transcript; the concatenated outputs exactly reconstruct the |
| polished scholarly transcript. Sentence alignment is anchored on output sentence |
| boundaries via character-level difflib mapping. |
|
|
| ## Reproduction |
|
|
| ```bash |
| python3 scripts/convert_golden.py |
| ``` |
|
|
| Writes `train.jsonl` (golden) and `bronze.jsonl`. |
|
|
