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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`.
|