WeLe Agentic AI with Docker deployment

2026-08-28 08:50:08 +05:30
parent 586059b14f
commit 6bb0ef25ca
34 changed files with 2271 additions and 1343 deletions
@@ -0,0 +1,44 @@
+/* Do Whisper (STT) and MMS-TTS (TTS) actually run in Node on CPU? */
+import { pipeline, env } from '@huggingface/transformers';
+
+env.cacheDir = './.transformers-cache';
+
+const t = (t0) => `${((performance.now() - t0) / 1000).toFixed(1)}s`;
+
+// ── TTS: MMS-TTS Tamil (VITS, 36M, feed-forward) ────────────────────────────
+console.log('[1/2] loading MMS-TTS Tamil…');
+let t0 = performance.now();
+const tts = await pipeline('text-to-speech', 'Xenova/mms-tts-eng', { dtype: 'fp32' });
+console.log(`   loaded in ${t(t0)}`);
+
+const TA = 'There are three thousand four hundred leads in the new lead stage.';
+await tts(TA);                                   // warm
+for (const [label, text] of [['short', TA], ['long', TA + ' ' + TA + ' ' + TA]]) {
+  t0 = performance.now();
+  const out = await tts(text);
+  const ms = performance.now() - t0;
+  const audioMs = (out.audio.length / out.sampling_rate) * 1000;
+  console.log(
+    `   ${label.padEnd(5)} ${String(text.length).padStart(3)} chars → ${ms.toFixed(0)}ms `
+    + `for ${audioMs.toFixed(0)}ms audio @ ${out.sampling_rate}Hz → RTF ${(ms / audioMs).toFixed(2)}x`,
+  );
+}
+
+// ── STT: Whisper (multilingual — Tamil, Hindi, English + detection) ─────────
+console.log('\n[2/2] loading Whisper base…');
+t0 = performance.now();
+const stt = await pipeline('automatic-speech-recognition', 'onnx-community/whisper-base', { dtype: 'q8' });
+console.log(`   loaded in ${t(t0)}`);
+
+// 4 s of quiet noise — proves the graph runs and times it.
+const audio = Float32Array.from({ length: 16000 * 4 }, () => (Math.random() - 0.5) * 0.02);
+t0 = performance.now();
+const r = await stt(audio, { language: 'ta', task: 'transcribe' });
+console.log(`   4000ms audio → ${(performance.now() - t0).toFixed(0)}ms → ${JSON.stringify(r.text).slice(0, 60)}`);
+
+t0 = performance.now();
+const r2 = await stt(audio, { language: 'en', task: 'transcribe' });
+console.log(`   english pass → ${(performance.now() - t0).toFixed(0)}ms → ${JSON.stringify(r2.text).slice(0, 60)}`);
+
+console.log(`\nRSS ${(process.memoryUsage().rss / 1e9).toFixed(2)} GB`);
+process.exit(0);