99 lines
4.7 KiB
Markdown
99 lines
4.7 KiB
Markdown
 
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# Church Translate
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Live sermon translation into 100+ languages. One Mac app per campus. No servers,
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no deployment, no subscription.
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Congregants point a phone camera at a QR code, tap their language once, and read
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along — or let their phone read it aloud in their ear. No app, no account.
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Built because [Kaleo](https://withkaleo.com/) is $24–149/month and
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[OneAccord](https://www.oneaccord.ai/) starts at $150/month for five hours, and
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both are wrappers around cloud speech and translation APIs that an M-series Mac
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runs locally for nothing.
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---
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## How it works
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Everything is inside the one app. Install it on the Mac at a campus, point it at
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the pastor's microphone, and it serves the congregation page itself.
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```
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┌──────────────── one Mac, one campus ─────────────────┐
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│ │
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│ Dante ─┐ │
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│ Waves ─┼─► audio in ──► voice detection │
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│ M32 ───┘ │ │
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│ ▼ │
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│ Whisper (Mac GPU) │
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│ │ │
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│ NLLB-200 (separate process) │
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│ │ │
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│ built-in web server ──────────────┼──► phones on
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│ │ the WiFi
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└───────────────────────────────────────────────────────┘
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```
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**No audio ever leaves the Mac,** and nothing leaves the building unless you
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choose to publish it. Only finished sentences cross the network, so a full
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auditorium costs almost nothing to serve.
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Two details that are not preference but necessity: translation runs in its own
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plain-Node child process, because Chromium's allocator refuses the model's
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gigabyte-sized weight blocks and kills the process; and speech recognition runs
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on the GPU while translation runs on the CPU, because neither fits in the
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other's runtime.
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## What it costs
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| --- | --- |
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| Speech recognition | Whisper large-v3-turbo, on the Mac's GPU — **$0** |
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| Translation, 100+ languages | NLLB-200, on the Mac's CPU — **$0** |
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| Spoken audio | The listener's own phone — **$0**, at any scale |
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| Serving the page | The app itself — **$0** |
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| Dante input | Dante Virtual Soundcard, **~$50 once per campus** — or free via Waves or console USB |
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## Performance
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Measured on an M2 Max, from the end of a spoken sentence to captions on a phone.
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Partial text appears well before that, so it feels quicker than the numbers.
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| --- | --- |
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| Live preview | updates every **500–800 ms** while the sentence is still being spoken |
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| Finished sentence | **~1.6 s** behind the speaker |
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| Speech recognition | 4.9× real time (accurate model) |
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| Translation | ~390 ms per language, flat over 240 consecutive runs |
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| Memory | plateaus at 10–11 GB; no drift across a long service |
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Two speech models run at once: a fast one drives the live preview, an accurate
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one produces the line the congregation keeps. They run on separate tracks, so
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the preview costs the finished caption almost nothing.
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## Getting started
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- **[docs/SETUP.md](docs/SETUP.md)** — installing at a campus
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- **[docs/AUDIO.md](docs/AUDIO.md)** — Dante, Waves SoundGrid, console USB
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- **[docs/COMPANION.md](docs/COMPANION.md)** — the Stream Deck button
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- **[docs/SUNDAY.md](docs/SUNDAY.md)** — print this and tape it to the desk
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- **[docs/PUBLIC-ACCESS.md](docs/PUBLIC-ACCESS.md)** — optional, for people off-site
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```bash
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cd app && npm install && npm start # run from source
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cd app && npm run dist # build the .app
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```
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## Things worth knowing
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- **Only languages people are actually listening to get translated.** Nobody on
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Icelandic means no CPU spent on Icelandic. Pin a language on the operator page
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to have it ready before the first person arrives.
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- **Corrections beat retraining.** Operator page → Corrections →
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`have a cook => Habakkuk`. Whole-word, case-insensitive, instant.
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- **A late arrival sees the last 40 lines**, so walking in halfway still makes sense.
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- **Transcripts** are written per service, original and every translation, under
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`~/Library/Application Support/Church Translate/data/transcripts/`.
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- **Each campus is independent.** Nothing is shared, nothing syncs, and one
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campus being down has no effect on another.
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