WeLe Agentic AI with Docker deployment

.env.example

@@ -58,11 +58,14 @@ ARTIFACT_TTL_HOURS=72
 
 # --- Voice (speech-to-speech, CPU, in-process) ---
 # Models are ONNX via Transformers.js — no GPU, no Python, no second service.
-#   STT  onnx-community/whisper-base   Tamil + English + language detection
+#   STT  onnx-community/whisper-tiny.en  English only; half the RAM of base
-#   TTS  assets/tts/mms-tts-tam        exported locally; no public ONNX exists
+#   TTS  Xenova/mms-tts-eng              from the Hub
-#   TTS  Xenova/mms-tts-eng            from the Hub
+# Tamil is built and tested (assets/tts/mms-tts-tam, exported locally — no
+# public ONNX exists). Enable it with VOICE_LANGUAGES=en,ta and a multilingual
+# STT_MODEL, but budget ~200 MB more resident for the extra voice.
+VOICE_LANGUAGES=en
 SPEECH_WARMUP=false
-STT_MODEL=onnx-community/whisper-base
+STT_MODEL=onnx-community/whisper-tiny.en
 # Below this confidence, the user's preferred language beats the detector.
 DETECT_CONFIDENCE=0.6
 

Dockerfile

@@ -1,19 +1,23 @@
 # ============================================
 # WeLe Agentic AI — production image
 #
-# Node service only. The voice service is deliberately NOT in this image: it
+# Speech runs in this same process: ONNX on CPU via Transformers.js. No GPU,
-# needs a CUDA GPU and several GB of RAM, and the target host has neither.
+# no Python, no second service.
-# See DEPLOY.md.
+#
+# node:20-slim, NOT alpine. onnxruntime-node ships glibc binaries and Alpine is
+# musl, so the native module fails at load with:
+#   Error loading shared library ld-linux-x86-64.so.2 (needed by libonnxruntime.so.1)
+# `sharp`, pulled in by Transformers.js, has the same constraint.
 # ============================================
 
-FROM node:20-alpine AS deps
+FROM node:20-slim AS deps
 WORKDIR /app
 COPY package*.json ./
 # `npm ci` builds exactly the lockfile, so a deploy can never silently pick up
 # a different dependency tree than the one that was tested.
 RUN npm ci --omit=dev
 
-FROM node:20-alpine
+FROM node:20-slim
 WORKDIR /app
 
 # Run unprivileged. The base image already ships a `node` user.

docker-compose.yml

@@ -28,6 +28,12 @@ services:
       - NODE_ENV=production
       - PORT=4000
 
+      # English-only keeps one TTS voice resident (~200 MB). Tamil is built and
+      # tested — VOICE_LANGUAGES=en,ta plus a multilingual STT_MODEL enables it,
+      # at roughly 200 MB more.
+      - VOICE_LANGUAGES=en
+      - STT_MODEL=onnx-community/whisper-tiny.en
+
       # Reuse the CRM's Redis by service name on the shared network.
       # Keys are namespaced with REDIS_PREFIX, so the two never collide.
       - REDIS_ENABLED=true
@@ -50,13 +56,22 @@ services:
     volumes:
       # Generated xlsx/pdf/pptx survive rebuilds; swept on a TTL by the app.
       - artifacts:/app/storage/artifacts
+      # Speech models are fetched from HuggingFace on first use (~200 MB).
+      # Without this they re-download on every restart and the first voice turn
+      # after a deploy stalls for a minute.
+      - speech_cache:/app/.transformers-cache
 
     # A 2 vCPU / 3.7 GB host already runs the CRM, chat-service, Redis and
     # Milvus. Capping this container keeps a runaway turn from starving them.
+    #
+    # 1 GB, not 768 MB: the speech models are resident once voice is used —
+    # measured 729 MB (Whisper tiny.en 415 MB + MMS-TTS English 203 MB + VAD
+    # 28 MB + the agent itself). 768 MB left no headroom, and an OOM kill takes
+    # text chat down with voice. Text-only sessions stay near 80 MB.
     deploy:
       resources:
         limits:
-          memory: 768M
+          memory: 1024M
 
     logging:
       driver: json-file
@@ -75,3 +90,4 @@ networks:
 
 volumes:
   artifacts:
+  speech_cache:

scripts/t-footprint.mjs

@@ -0,0 +1,46 @@
+/* English-only footprint: does it fit the 768 MB container cap on AWS?
+
+   Loads exactly what an English-only deployment needs and reports RSS after
+   each stage, so the answer is measured rather than estimated.
+*/
+const mb = () => Math.round(process.memoryUsage().rss / 1048576);
+const step = (label) => console.log(`  ${label.padEnd(34)} RSS ${String(mb()).padStart(4)} MB`);
+
+step('baseline (node + agent code)');
+
+const { synthesize, transcribe, Endpointer } = await import('../src/speech/index.js');
+step('after importing speech module');
+
+// VAD
+const ep = new Endpointer();
+await ep.push(new Float32Array(16000));
+step('+ Silero VAD');
+
+// TTS English
+const spoken = await synthesize('There are three thousand four hundred and twenty seven new leads.', 'en');
+step('+ MMS-TTS English');
+
+// STT
+const resample = (a, from, to) => {
+  const r = from / to, out = new Float32Array(Math.floor(a.length / r));
+  for (let i = 0; i < out.length; i++) { const p = i * r, k = Math.floor(p); out[i] = a[k] + (a[Math.min(k + 1, a.length - 1)] - a[k]) * (p - k); }
+  return out;
+};
+const audio = resample(spoken.audio, spoken.sampling_rate, 16000);
+const heard = await transcribe(audio, 'en', 'en');
+step('+ Whisper base (STT)');
+
+// Steady state: a few turns, to see whether it keeps growing.
+for (let i = 0; i < 3; i++) {
+  await synthesize('Checking the leads now.', 'en');
+  await transcribe(audio, 'en', 'en');
+}
+step('after 3 more turns');
+
+console.log(`\n  transcript: ${JSON.stringify(heard.text)}`);
+console.log(`  TTS rate  : ${spoken.sampling_rate} Hz`);
+
+const peak = mb();
+const CAP = 768;
+console.log(`\n  peak ${peak} MB vs ${CAP} MB container cap → ${peak < CAP * 0.8 ? 'FITS ✅' : peak < CAP ? 'TIGHT ⚠️' : 'EXCEEDS ❌'}`);
+process.exit(0);

src/speech/index.js

@@ -7,10 +7,10 @@
 // one shift and should never have to touch a language menu.
 // ============================================
 import { Tensor } from '@huggingface/transformers';
-import { getSTT, getTTS, supportsTTS } from './models.js';
+import { getSTT, getTTS, supportsTTS, sttIsEnglishOnly, defaultLanguage } from './models.js';
 import logger from '../utils/logger.js';
 
-export { LANGUAGES, warmup, speechStatus } from './models.js';
+export { LANGUAGES, warmup, speechStatus, defaultLanguage } from './models.js';
 export { Endpointer, warmupVad } from './vad.js';
 
 const RATE = 16000;
@@ -67,7 +67,7 @@ async function detectLanguage(audio) {
  * @param {string} lang         'auto' | 'ta' | 'en'
  * @param {string} prefer       used when detection is unusable
  */
-export async function transcribe(audio, lang = 'auto', prefer = 'ta') {
+export async function transcribe(audio, lang = 'auto', prefer = defaultLanguage()) {
   if (!audio || audio.length < RATE / 5) {              // under 200 ms
     return { text: '', lang: prefer, note: 'too short' };
   }
@@ -80,7 +80,11 @@ export async function transcribe(audio, lang = 'auto', prefer = 'ta') {
   let detected = null;
   let confidence = null;
 
-  if (lang === 'auto') {
+  // An English-only checkpoint has no language tokens to read, and passing
+  // `language` to it is rejected — so "auto" simply means English there.
+  if (lang === 'auto' && sttIsEnglishOnly()) {
+    used = 'en';
+  } else if (lang === 'auto') {
     try {
       const d = await detectLanguage(audio);
       detected = d.lang;
@@ -95,7 +99,15 @@ export async function transcribe(audio, lang = 'auto', prefer = 'ta') {
     }
   }
 
-  const result = await stt(audio, { task: 'transcribe', language: used, return_timestamps: false });
+  // An English-only checkpoint rejects BOTH `task` and `language` — it has no
+  // other mode to select. Multilingual builds require the language, since they
+  // silently default to English otherwise.
+  const opts = { return_timestamps: false };
+  if (!sttIsEnglishOnly()) {
+    opts.task = 'transcribe';
+    opts.language = used;
+  }
+  const result = await stt(audio, opts);
   return finish(result, used, audio, t0, detected, confidence);
 }
 
@@ -115,11 +127,11 @@ function finish(result, used, audio, t0, detected, confidence) {
  * Synthesise one piece of text.
  * @returns {Promise<{audio: Float32Array, sampling_rate: number}>}
  */
-export async function synthesize(text, lang = 'ta') {
+export async function synthesize(text, lang = defaultLanguage()) {
   const clean = (text || '').trim();
   if (!clean) return null;
 
-  const use = supportsTTS(lang) ? lang : 'en';
+  const use = supportsTTS(lang) ? lang : defaultLanguage();
   const tts = await getTTS(use);
 
   const t0 = Date.now();

src/speech/models.js

@@ -35,20 +35,38 @@ env.cacheDir = process.env.SPEECH_CACHE_DIR || path.join(ROOT, '.transformers-ca
 // of mms-tts-tam exists. See scripts/export-tamil-tts.py.
 env.localModelPath = path.join(ROOT, 'assets/tts');
 
-const STT_MODEL = process.env.STT_MODEL || 'onnx-community/whisper-base';
+// whisper-tiny.en by default: measured, Whisper is the memory hog, not TTS.
+// base cost 554 MB of an 879 MB total, which overran the 768 MB container cap.
+// The .en build is half the size and, being English-only, cannot detect a
+// language — which is fine when VOICE_LANGUAGES is just `en`.
+const STT_MODEL = process.env.STT_MODEL || 'onnx-community/whisper-tiny.en';
 
-/** TTS voice per language. Tamil is local; English comes from the Hub. */
+/** True when the STT checkpoint is English-only and cannot identify languages. */
-const VOICES = {
+export const sttIsEnglishOnly = () => /\.en$/.test(STT_MODEL);
-  ta: { id: 'mms-tts-tam', local: true },
+
-  en: { id: 'Xenova/mms-tts-eng', local: false },
+/** TTS voice per language. Tamil is exported locally; English is on the Hub. */
+const ALL_VOICES = {
+  en: { id: 'Xenova/mms-tts-eng', local: false, label: 'English', native: 'English' },
+  ta: { id: 'mms-tts-tam', local: true, label: 'Tamil', native: 'தமிழ்' },
 };
 
+// Each extra language is a further ~200 MB resident. Enable only what the
+// deployment actually speaks — English alone on the current AWS box.
+const ENABLED = (process.env.VOICE_LANGUAGES || 'en')
+  .split(',').map((s) => s.trim()).filter((c) => ALL_VOICES[c]);
+
+const VOICES = Object.fromEntries(ENABLED.map((c) => [c, ALL_VOICES[c]]));
+
 export const LANGUAGES = [
-  { code: 'auto', label: 'Auto-detect', native: 'Auto' },
+  // Auto-detect is only offered when there is a choice to make AND the STT
-  { code: 'ta', label: 'Tamil', native: 'தமிழ்' },
+  // model can actually detect — offering it otherwise is a lie.
-  { code: 'en', label: 'English', native: 'English' },
+  ...(ENABLED.length > 1 && !sttIsEnglishOnly()
+    ? [{ code: 'auto', label: 'Auto-detect', native: 'Auto' }] : []),
+  ...ENABLED.map((c) => ({ code: c, label: ALL_VOICES[c].label, native: ALL_VOICES[c].native })),
 ];
 
+export const defaultLanguage = () => (LANGUAGES[0]?.code || 'en');
+
 const cache = new Map();
 let sttPromise = null;
 
@@ -81,8 +99,8 @@ export async function getSTT() {
   return sttPromise;
 }
 
-export async function getTTS(lang = 'ta') {
+export async function getTTS(lang) {
-  const voice = VOICES[lang] || VOICES.ta;
+  const voice = VOICES[lang] || VOICES[ENABLED[0]];
   const prev = env.allowRemoteModels;
   try {
     // Local folders must not be looked up on the Hub, and vice versa.
@@ -98,7 +116,7 @@ export async function getTTS(lang = 'ta') {
 export const supportsTTS = (lang) => Boolean(VOICES[lang]);
 
 /** Warm the models the deployment actually expects to use. */
-export async function warmup(langs = ['ta', 'en']) {
+export async function warmup(langs = ENABLED) {
   try {
     await getSTT();
     for (const l of langs) await getTTS(l);
@@ -111,6 +129,7 @@ export async function warmup(langs = ['ta', 'en']) {
 export function speechStatus() {
   return {
     stt_model: STT_MODEL,
+    english_only_stt: sttIsEnglishOnly(),
     tts_voices: Object.fromEntries(Object.entries(VOICES).map(([k, v]) => [k, v.id])),
     loaded: [...cache.keys()],
     languages: LANGUAGES,