Church Translate, as it runs at the campuses

Live sermon translation into 100+ languages, running entirely on one Mac per
campus: audio in, voice detection, Whisper on the GPU, NLLB-200 in its own
process, and a built-in web server the congregation reads from on their
phones. No audio leaves the building.

This is the source only. What used to sit beside it — 14 GB of speech and
translation models, 8 GB of voice bundles, 6 GB of natural voices, the Electron
caches, and the app data holding the operator PIN and every service transcript
— is machine state and downloaded assets, and stays in the NAS backup where it
belongs. The vendored node, cloudflared and Windows ffmpeg binaries are out too
(533 MB, re-fetched for packaging); the browser-side runtime beside them is
kept, so a checkout still serves the congregation page.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

app/_bench-opus.mjs

@@ -0,0 +1,85 @@
+/*
+ * Opus-MT bilingual vs NLLB-600M, on THIS Mac, on sermon sentences.
+ *
+ * The claim under test: a 77M bilingual model matches or beats NLLB-200-
+ * distilled-600M on high-resource pairs at ~1/8 the size. Published chrF says
+ * yes; no published CPU latency comparison exists, and quality on preaching
+ * English is not what any leaderboard measures. So: measure both here.
+ */
+import { pipeline, env } from '@huggingface/transformers';
+import os from 'os'; import path from 'path';
+env.allowLocalModels = false;
+env.cacheDir = path.join(os.homedir(), 'Library/Application Support/Church Translate Models');
+
+const SENTENCES = [
+  'And I want you to understand that God has a plan for your life',
+  'Turn with me to the book of Romans chapter eight',
+  'He was faithful even when it cost him everything',
+  'Would you stand with me as we pray',
+  'The grace of God is not something you can earn',
+  'Peter denied him three times before the rooster crowed',
+];
+const PAIRS = [
+  { code: 'spa_Latn', opus: 'Xenova/opus-mt-en-es', name: 'Spanish' },
+  { code: 'fra_Latn', opus: 'Xenova/opus-mt-en-fr', name: 'French'  },
+  { code: 'zho_Hans', opus: 'Xenova/opus-mt-en-zh', name: 'Chinese' },
+];
+
+const t = (f) => { const s = Date.now(); return f().then((r) => [Date.now() - s, r]); };
+const out = (o) => (Array.isArray(o) ? o[0] : o).translation_text.trim();
+
+console.log('loading NLLB fp32…');
+const nllb = await pipeline('translation', 'Xenova/nllb-200-distilled-600M', { dtype: 'fp32' });
+await nllb(SENTENCES[0], { src_lang: 'eng_Latn', tgt_lang: 'spa_Latn' });
+
+const results = {};
+for (const p of PAIRS) {
+  const ts = [], txt = [];
+  for (const s of SENTENCES) {
+    const [ms, o] = await t(() => nllb(s, { src_lang: 'eng_Latn', tgt_lang: p.code }));
+    ts.push(ms); txt.push(out(o));
+  }
+  results[p.name] = { nllb: { ms: ts, txt } };
+}
+
+for (const dtype of ['fp32', 'q8']) {
+  for (const p of PAIRS) {
+    let pipe;
+    const t0 = Date.now();
+    try { pipe = await pipeline('translation', p.opus, { dtype }); }
+    catch (e) { console.log(p.name + ' ' + dtype + ': LOAD FAILED — ' + String(e.message).slice(0, 90)); continue; }
+    const load = Date.now() - t0;
+    await pipe(SENTENCES[0]);
+    const ts = [], txt = [];
+    for (const s of SENTENCES) { const [ms, o] = await t(() => pipe(s)); ts.push(ms); txt.push(out(o)); }
+    results[p.name]['opus_' + dtype] = { ms: ts, txt, load };
+    await pipe.dispose?.();
+  }
+}
+
+const mean = (a) => a.reduce((x, y) => x + y, 0) / a.length;
+console.log('\n=== speed, ms per sentence (mean of ' + SENTENCES.length + ') ===');
+console.log('language  |  NLLB fp32 | Opus fp32 | Opus q8 | best speedup');
+for (const p of PAIRS) {
+  const r = results[p.name];
+  const n = mean(r.nllb.ms);
+  const f = r.opus_fp32 ? mean(r.opus_fp32.ms) : NaN;
+  const q = r.opus_q8 ? mean(r.opus_q8.ms) : NaN;
+  const best = Math.min(f || Infinity, q || Infinity);
+  console.log(p.name.padEnd(9) + ' | ' + (n.toFixed(0) + 'ms').padStart(10) + ' | ' +
+    ((f ? f.toFixed(0) : '—') + 'ms').padStart(9) + ' | ' + ((q ? q.toFixed(0) : '—') + 'ms').padStart(7) +
+    ' | ' + (n / best).toFixed(2) + 'x');
+}
+
+console.log('\n=== output, sentence by sentence ===');
+SENTENCES.forEach((s, i) => {
+  console.log('\n"' + s + '"');
+  for (const p of PAIRS) {
+    const r = results[p.name];
+    console.log('  ' + p.name);
+    console.log('    NLLB      : ' + r.nllb.txt[i]);
+    if (r.opus_fp32) console.log('    Opus fp32 : ' + r.opus_fp32.txt[i]);
+    if (r.opus_q8)   console.log('    Opus q8   : ' + r.opus_q8.txt[i] +
+      (r.opus_fp32 && r.opus_q8.txt[i] !== r.opus_fp32.txt[i] ? '   <-- differs from fp32' : ''));
+  }
+});