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Sawt: my Levantine notebook for Arabizi lessons, voice practice, and real-life situations.
EducationPython · HTMLGitHub @yusifhussamiOnlineOwner unconfirmed
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The maker's introduction consists of the line above. Read the repository README for more details. Repository README ↗
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Read the full README ↗Sawt is a practice notebook. After class you paste new words with meanings. Before the next class you talk with those words, or write a sentence with one of them. The app UI stays English. In Settings you choose the language you’re learning — Levantine Arabic (Arabizi, Arabic speech) or Japanese (romaji, Japanese speech).
Locally it runs on this computer with SQLite. On Vercel each person signs in, and their words live in Postgres. Sentence checks, Talk replies, and speech go to OpenRouter.
python3 -m lexicon.serve
Open http://127.0.0.1:8765. No Clerk sign-in is required for that local server.
baza5 = fancy or practice - tadreeb. Japanese: hello - konnichiwa or mizu = water (romaji). Preview shows the split before you save.Arabizi digits (Arabic): 2 ء/أ, 3 ع, 3' غ, 5 خ, 6 ط, 7 ح, 8 ق, 9 ص, 9' ض. Long ee and oo stay as ee and oo. كيفك is keefak. Japanese mode uses Hepburn romaji on the page and Japanese script for speech.
The next Preply or Arabic lesson shows in the side column after you paste the private Google Calendar iCal link. That link stays in the database. It is not printed on the page and it is not included in errors.
The app stays in Python: static HTML in public/, API routes in api/, shared logic in lexicon/. There are 12 function files under api/ (Vercel Hobby’s cap). /api/next-lesson is rewritten to api/calendar.py; lesson topics live in api/topics.py.
6543) for Vercel. Set that as DATABASE_URL.CLERK_PUBLISHABLE_KEY and CLERK_JWKS_URL (the JWKS URL from the Clerk dashboard).OPENROUTER_API_KEY (or GEMINI_API_KEY).Optional local packages for Postgres and JWT checks:
python3 -m pip install -r requirements.txt
To point local serve at the same Supabase database, put DATABASE_URL in .env. Without it, local serve keeps using lexicon.db.
127.0.0.1 only works on the computer running the notebook. On the same Wi‑Fi:
python3 -m lexicon.serve --host 0.0.0.0
Then open http:// followed by this computer's local IP and :8765. Anyone on that address can use your words and your API key, so keep it off public networks. The hosted Vercel URL is the better way to share accounts. Speech uses Gemini Flash Lite TTS, about a cent a minute.
Copy .env.example to .env for local values. On Vercel, set the same names in project env.
| Call | Model |
|---|---|
| Sentence check and Talk | google/gemini-3.5-flash-lite |
| Speech (TTS) | google/gemini-3.8-flash-lite-tts |
| Name | What it holds |
|---|---|
OPENROUTER_API_KEY | OpenRouter key (header only) |
DATABASE_URL | Supabase Postgres URI (hosted; optional locally) |
CLERK_PUBLISHABLE_KEY | Browser sign-in |
CLERK_JWKS_URL | Server JWT check |
lexicon.db | Local SQLite when DATABASE_URL is unset |
data/vocabulary.csv is the older sheet export. It has Arabizi and English only, no account details.
Python 3.9 is enough for the local server with no extra packages. Hosted deploy uses requirements.txt.
Why each model, the voice loop, and the Vercel shape: docs/decisions.
python3 -m unittest discover -s tests
The notebook replaced the day-to-day way of practicing, but the earlier commands still load the sheet and run a drill in the terminal:
python3 -m lexicon.load data/vocabulary.csv lexicon.db
python3 -m lexicon.drill data/vocabulary.csv lexicon.db