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feat: fast on-device note translation with ML Kit, keeping the LLM as fallback

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Valutazione

Difficoltà
5/5
Tempo stimato
Più di una settimana
Idoneità per principianti
25/100
Tipo di issue
Funzionalità
Chiarezza
Abbastanza chiara
Stato di attività
Attiva
Stack tecnologico
dart, flutter
Ambito
machine-learning, mobile

Direzione di ricerca

Start by measuring cold and warm translation on a device as described in “Measure on a device,” then read the existing TranslateNoteUseCase → TranslationPrompt → GenerateOneShotUseCase path. Compare ML Kit and the current LLM on real notes, including language detection, downloads, storage, and protected text. Done means the supported-language path is fast, fallback cases still use the LLM, tests cover the choice, and a release APK builds.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

enhancement P2-Medium

🗂️ Backlog — not scheduled yet. Before building: connect a phone and measure speed first (section "Measure on a device"), and look for something better than the options below.

Translating a note today runs a one-shot prompt through the on-device LLM (TranslateNoteUseCase → TranslationPrompt → GenerateOneShotUseCase). It is slow: the audit measured prompt prefill at 19.9 s on CPU and ~7.9 s on GPU on a mid-range phone, before the first translated word appears — and it needs a chat model downloaded at all, competing with chat and background AI work for the same device.

ML Kit's on-device translation (google_mlkit_translation) is a dedicated translation model — "the same models used by the Google Translate app's offline mode" — and typically answers in well under a second once its language model is on the device.

Proposal: ML Kit first, LLM as the fallback

Keep the existing LLM path; put ML Kit in front of it where it applies.

  • Supported pairs → ML Kit. Instant, offline after the first download, no LLM.
  • Everything else → the current LLM path, unchanged.

What ML Kit covers — checked against our 22 languages

TranslationLanguage.all offers 22 languages. ML Kit supports 20 of them. Not supported: Malayalam (ml) and Punjabi (pa) — those must stay on the LLM path.

Caveats found while checking — each shapes the design

  • Models are downloaded, not bundled — ~30 MB per language, fetched from Google on first use, then fully offline. The first translation into a language needs a network and a visible download state (and probably a Wi-Fi-only option). Downloaded models should be listed/removable, like the AI models are today.
  • Everything pivots through English. Google: models are "trained to translate to and from English. When you translate between non-English languages, English is used as an intermediate translation, which can affect quality." Hindi → Tamil goes Hindi → English → Tamil.
  • Quality is stated as "casual and simple translations." Fine for reading a note; worth a manual comparison against the LLM on real notes before making it the default.
  • Code-mixed / romanised text (Hinglish). The current prompt handles it explicitly ("translate the meaning, not the spelling"). ML Kit expects a known source language in its native script. Route romanised or undetected input to the LLM.
  • The source language must be known. The LLM infers it; ML Kit needs it. Likely google_mlkit_language_id (on-device) — check its size and native dependencies before adding it.
  • Build dependencies checked: the Android com.google.mlkit:translate artifact brings no Firebase services (only firebase-components/firebase-encoders, already in the app through mobile_scanner), but it does pull in okhttp 3.0.0 for model downloads — check it resolves cleanly alongside anything else on the classpath. After adding it, build a release APK: ML Kit plugins have broken R8 before (#240's work).
  • Use the specific google_mlkit_translation package, not the google_ml_kit umbrella — the umbrella pulls in 21 ML Kit plugins.

Done when

Translating a note into a supported language uses ML Kit and shows the result in about a second once the model is present; the first use of a language shows a download state; Malayalam, Punjabi, romanised and undetectable text still translate through the LLM; a release APK builds; and the choice is covered by tests with the ML Kit side behind a fake, as OCR and labeling are.

Storage — download only what is used

Models are per language, downloaded on first use, deletable. Nobody stores all 20:

User Languages used ML Kit storage
Reads Hindi notes, translates to English Hindi (English may be built in — unverified) ~30–60 MB
Hindi + Tamil + English 2–3 ~60–90 MB
All 20 languages 20 ~600 MB — unrealistic, and deletable

Design to keep it small:

  1. Download a language only when the user first translates into it — never all up front.
  2. List downloaded languages in Settings with sizes and a delete button, like the AI models today.
  3. Keep the AI model as the zero-storage option — no download needed, just slower.
  4. Ask before downloading on mobile data (Wi-Fi only by default).

Other offline options researched

Option Our 22 languages Size Licence Flutter effort Verdict
ML Kit 20 (no Malayalam, Punjabi) ~30 MB per language free to use (closed Google models) existing plugin — a day or two use now, AI as fallback
Mozilla Bergamot (Firefox's offline translator) 21 (only Punjabi missing) 17–44 MB per direction MPL-2.0 no Flutter package — build the C++ engine for Android + iOS ourselves best single engine if native work is taken on later
IndicTrans2 (AI4Bharat) 11 — all 10 Indian languages + English ~260–380 MB per direction, but one model covers all Indian languages MIT heavy — custom ONNX + porting its preprocessing to Dart best Indian-language quality (on par with Google Translate)
NLLB-200 (Meta) 21+ ~2.5 GB non-commercial weights — ❌ cannot ship
TranslateGemma 4B 22 2–4 GB Gemma terms already supported ❌ slower than today (CPU-only, ~90 s before output)
OPUS-MT / Apple Translation too few Indian languages — — — ❌
AI model already on the phone (today) 22 0 MB extra — — slow: 7.9 s (GPU) – 19.9 s (CPU) before the first word

Nothing offline is meaningfully smaller at usable quality: ~17–30 MB per language/direction is the floor for real translation models.

Implementation notes from the research

  • ML Kit needs the source language. Add google_mlkit_language_id. It also catches Hinglish: Latin-script text identified as Hindi goes to the AI.
  • Romanised / code-mixed text is weak on every translation engine — all are trained on native script. Keep the AI path for it.
  • Protect hashtags, @mentions, URLs, emoji and line breaks. The AI prompt tells the model to keep them; a translation engine has no such instruction, so mask them before translating and restore after.
  • ML Kit and Bergamot pivot through English for Indian → Indian pairs; only IndicTrans2 translates between Indian languages directly.

Measure on a device before building

  • Speed, cold and warm, for English→Hindi, Hindi→English, Hindi→Tamil (pivoted) and English→Malayalam — against today's 7.9–19.9 s.
  • Language detection on one-sentence notes and Hinglish.
  • A native-speaker check of ~30 real notes per language, including hashtags, mentions and links.
  • The model download on mobile data, and storage as languages are added.

Sources

ML Kit languages · ML Kit translation · Mozilla translations · Bergamot on Android · IndicTrans2 · IndicTrans2 paper · NLLB-200 licence · TranslateGemma

Lingua principale
Dart
Stelle
2
Fork
2
Merge medio
1g 7h
PR unite (30g)
35

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