license: other
license_name: mixed-per-row
license_link: LICENSE
language:
- en
task_categories:
- automatic-speech-recognition
- text-generation
pretty_name: Linux Admin Sentence Bank
tags:
- asr
- fine-tuning
- read-aloud-sentences
- read-aloud
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: data/train.jsonl
- split: test
path: data/test.jsonl
Linux Admin Sentence Bank
English sentences about Linux desktop and server administration, built around names a speech model writes as an ordinary English word: Konsole (console), Okular (ocular), Thunar (thunder), LUKS (looks), Btrfs, GRUB, Chrony (crony). Where natural, a sentence also uses the ordinary word, so the model has to tell them apart from context.
Website: hebrish.app: get the Hebrish keyboard for Android and the desktop apps, see the results, and find every model, dataset and demo in one place.
These are sentences to read aloud for fine-tuning a speech-recognition model, not recordings. They feed a targeted fine-tune of NVIDIA Parakeet TDT 0.6B v2 (current model). A capture app shows these sentences one at a time to be read aloud; every take lands in Read-Aloud-Voice-Sentences and is copied into the matching audio subset. The recordings of these sentences are in Linux-Admin-Sentences.
This dataset gathers sentences from several places into one file for my own training, and it is reproduced here for convenience. Every row says where it came from, who wrote it and how it was changed. Credit for upstream sentences belongs to their authors (see "Attribution").
Size
317 train, 24 test (341 total). The test split is term-disjoint:
no target term in test appears in train, so an evaluation on it measures generalisation,
not memorisation.
Derivation: generated 341.
Sentence authors: claude (Claude Code subagent) 336, claude-opus-5.5 (Claude Code session) 5.
Columns
| Column | Meaning |
|---|---|
id |
sentence id: a hash of the text (same id as the capture app and the audio datasets' prompt_id) |
text |
the sentence, exactly as read. Punctuated and cased; numbers in digits for versions, sizes, percentages and money |
target_terms |
the words the sentence exists to teach |
term_origins |
for each target term, who proposed it: daniel (from my own repos and notes, or I asked for it), claude (an AI agent proposed it), or the public list it came from (openrouter, github-topics, cncf, pypi, stackoverflow) |
categories |
tech, linux, smart_home, homophone, numeric, … |
homophone_set |
homophone rows only: the set the context disambiguates |
split |
train or test |
derivation |
mined-verbatim (taken unchanged from a public dataset), mined-edited (taken, then lightly fixed for casing, digits or readability), generated (written by an LLM for this project, then reviewed by a second model), borrowed (taken whole from an earlier sentence set) |
sentence_author |
the upstream dataset, or the model that wrote the sentence |
source_dataset, source_licence, source_url, source_authors, source_date |
upstream provenance for mined and borrowed rows. source_url links the paper or post; source_authors lists arXiv authors (Reddit usernames are deliberately not copied) |
original_text |
the upstream text before editing (mined-edited rows) |
review_note |
the reviewer's note when a sentence was fixed |
Attribution and licence
Licensing is per row (source_licence). Rows without one were written for this project and
are CC-BY-4.0.
| Upstream | Licence |
|---|---|
| (none: every row was written for this project) | CC-BY-4.0 |
Mined sentences were selected by scripts/mine_tech_sentences.py in the project repository:
they are 8–30 words long, contain at least one named tech term, and come from newer posts first.
An AI reviewer then kept or dropped each one and fixed casing and numbers.
Updating
Upstream sources can be re-mined, and new sentences are added as the project grows. Ids are content hashes, so an edited sentence gets a new id.