Datasets:

njand
/

latin-asr-post-processing-dataset

dataset_info:
  features:
    - name: source
      dtype: string
    - name: tokens
      list: string
    - name: tags
      list: string
  splits:
    - name: train
      num_bytes: 273218408
      num_examples: 341968
    - name: test
      num_bytes: 14469157
      num_examples: 17999
  download_size: 268889691
  dataset_size: 287687565
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
license: mit
task_categories:
  - token-classification
language:
  - la
size_categories:
  - 100K<n<1M

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.

Quick Stats

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

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 (?)

Data Preprocessing Pipeline

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.

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).

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.

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).

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.