njand/latin-asr-post-processing-dataset

Latin ASR Post-Processing Dataset

A sequence-labeling dataset built for fine-tuning BERT-style models (e.g., latin-bert) on Inverse Text Normalization (ITN) — restoring capitalization and punctuation on raw, lowercased Latin text (such as njand/wav2vec2-xls-r-latin ASR outputs).

Compiled from 2,141 files in the CLTK Latin Library and augmented with transcripts from the njand/llpsi-speech-dataset (currently private). Cleaned and transformed through a specialized classical Latin normalization pipeline.

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Quick Stats

• Tokens: ~11 million
• Samples: >350k lines
• Task: Token Classification / Sequence Labeling
• Splits: 95% Train (341,968 samples) / 5% Test (17,999 samples)

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Data Schema

Each example pairs lowercased, normalized tokens with a joint Casing + Trailing Punctuation target tag (14 possible combinations):

Input Sentence: “Dicam plane, Caesar, quod sentio.”

{
  "source": "cicero/lig.txt",
  "tokens": ["dicam", "plane", "caesar", "quod", "sentio"],
  "tags": ["TITLE_NONE", "LOWER_COMMA", "TITLE_COMMA", "LOWER_NONE", "LOWER_PERIOD"]
}

• Casing Labels: TITLE, LOWER
• Punctuation Labels: NONE, PERIOD (.), COMMA (,), COLON (:), SEMICOLON (;), EXCLAMATION (!), QUESTION (?)

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Data Preprocessing Pipeline

1. Filtering & Noise Removal:

• Strips editorial headnotes, metadata, apparatus criticus, dates, section numbers, bracketed markers, and English notes.
• Drops incomplete sentence fragments, lines containing ALL-CAPS words, or unparseable lines.

2. Praenomina Expansion:

• Abbreviated Roman praenomina (e.g., M., C., Sex., Agr., etc.) are contextually expanded and inflected to agree grammatically with the following word (e.g., M. Tullio → Marco Tullio).

3. Dynamic Roman Numeral Expansion:

• Valid, grammatically indeclinable Roman numerals are dynamically converted to Latin cardinal words (e.g., XIV → quattuordecim), matching original casing.
• Vocabulary collisions with real Latin words (e.g., i, vi) are safely preserved.

4. Classical Orthography (i/u Normalization):

• Strips all macrons and diacritics.
• Expands ligatures (æ/œ → ae/oe).
• Normalizes orthography to strict classical standards: converts consonantal v to u and j to i (e.g., uva → uua, jam → iam).
• Normalizes compound -iacere verb forms (e.g., ejicio → eicio, conjicere → conicere).

5. Sentence Splitting & Probabilistic Merging:

• Sentence boundaries are tokenized using NLTK.
• Adjacent sentences within paragraphs are probabilistically merged (p = 0.50, max 1,000 characters) to simulate longer context windows.