Localization and Glossary Tools

Run, feed and check three review-first localization projects. Pick a multilingual model and a source language.

Three projects side by side#

Factor	Subtitle Localization Desk	UI String Localization Workbench	Glossary Hovercard Publisher
Builds	Reviewed WebVTT and an HTML desk	Reviewed locale JSON and a preview	Annotated HTML lesson
Language	Python 3.10+	Node 22.18 or newer	Node 22.18 or newer
Input keys	`vtt`, `translations`, `glossary`, `cps`	`locale`, `messages`, `proposals`, `values`	`glossary`, `lesson`
Outputs	`review.html`, `translation-decisions.json`, `reviewed.vtt`	`locale.json`, `unresolved-strings.json`, `report.html`	`report.html`, `glossary.json`, `coverage.json`
Review file	`--decisions decisions.json`	third argument `locale-review.json`	none
Hard gate	`reviewed.vtt` only when all cues approved	approve only with no issues	balanced HTML subset

Task	Code
Make a starter	`python3 scripts/project_test.py <project> --init <dir>`
Grade one stage	`python3 scripts/project_test.py <project> --stage 1 --path <dir> --strict`
Grade all stages	`python3 scripts/project_test.py <project> --all --path <dir> --strict`
Save a completion report	`--report completion.json`
Grade the reference	`python3 scripts/project_test.py <project> --all --solution --strict`

Symptom	Cause	Fix
`stale review`	Proposals or cps changed after review	Use the new `decisions.json` from `review.html`
`resolve glossary or acknowledge reading-budget overflow`	Approved cue misses a term or the budget	Add the term or set `override_budget` to true
`STYLE and REGION are outside the supported subset`	VTT has STYLE or REGION blocks	Remove them. NOTE blocks are ignored
`Placeholder multiset changed`	Translation drops or adds a `{name}`	Keep every placeholder of the source
`Stale or invalid translation decision`	Review translation differs from the proposal	Download a new `locale-review.json`
`Resolve translation issues or context before approving`	Ambiguous key not acknowledged	Set `acknowledgeContext` to true
`Ambiguous repeated phrase`	Same phrase in two terms	Give each phrase one term
`Unsupported HTML element`	Tag outside the allowed subset	Use the listed tags only

Setting	Default	Note
`cps`	17	Must be positive and finite
`schema_version`	1	In `translation-decisions.json`
`approved`	`False`	Every cue starts unapproved
`override_budget`	`False`	Needed for an over-budget approval
Proposal length	2000	Characters at most per proposal
`schemaVersion`	1	Required in `locale-review.json`
Output folder	`output`	Second argument of the Node CLIs
Exit code on error	2	The Node CLIs print the message

Command	What it does
`python3 cli.py samples/input.json --output demo-output`	Subtitle desk on the sample
`python3 cli.py <input.json> --output <dir>`	Subtitle desk on your input
`node cli.ts sample.json output`	UI workbench or glossary publisher
`node cli.ts <input.json> <dir> <review.json>`	UI workbench with your review
`node cli.ts --help`	Prints the argument line

{

"vtt": "WEBVTT\n\nmap\n00:00.000 --> 00:03.000\nOpen the map.\n...",

"translations": {

"map": "Abre el map.",glossary turns it into "Abre el mapa."

"label": "Escribe un nombre descriptivo ..."2 s at cps 12, budget 24

},

"glossary": {"map": "mapa", "marker": "marcador"},source to target

"cps": 12optional, the default is 17

}

Term id: lower case, matches ^[a-z][a-z0-9-]*$, and is unique.
Phrases: a term or alias must not repeat across the glossary, in any case.
Matching: matchTerms() tries the longest phrase first and needs word boundaries.
Skipped text: text inside pre, code and a stays untouched.
Attributes: only class, id, title, lang and href on a.
Links: href must start with https://, http:// or #.
Definition: a native button with aria-expanded shows a role="note" span.

Task	Code
Classification, 100 languages	`XLM-R-base` fine-tuned
Zero-shot classification	`joeddav/xlm-roberta-large-xnli`
Translation, 200 languages	`facebook/nllb-200-distilled-600M`
Low-resource language	`XLM-V` or a related-language fine-tune
Few-shot epochs	`num_train_epochs=5`
Few-shot learning rate	`learning_rate=2e-5`
Long-tail scripts	`byte_fallback=True`

Is English always the best source?: No. Language similarity predicts transfer better than corpus size.
Marathi target?: rank_source_languages() puts Hindi above English.
How many target labels?: 100 to 500 examples give 95 to 98 percent of the English baseline.
Why not a higher learning rate?: The multilingual alignment collapses to an English-only model.
What is the fertility tax?: Hindi can need 3 to 5 times the tokens of English.