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 +1,62 @@
+/* Can we get real language detection out of Whisper in Transformers.js? */
+import { AutoProcessor, WhisperForConditionalGeneration, Tensor, env } from '@huggingface/transformers';
+import { synthesize } from '../src/speech/index.js';
+
+env.cacheDir = './.transformers-cache';
+const MODEL = 'onnx-community/whisper-base';
+
+const processor = await AutoProcessor.from_pretrained(MODEL);
+const model = await WhisperForConditionalGeneration.from_pretrained(MODEL, { dtype: 'q8' });
+const tok = processor.tokenizer;
+
+// Whisper emits one language token right after <|startoftranscript|>. Reading
+// that distribution is a single decoder step — far cheaper than transcribing
+// twice to see which language "looks better".
+const id = (t) => tok.encode(t, { add_special_tokens: false })[0];
+const sot = id('<|startoftranscript|>');
+const CANDIDATES = ['en', 'ta'];
+const langIds = CANDIDATES.map((c) => id(`<|${c}|>`));
+console.log('sot:', sot, '| language token ids:', JSON.stringify(Object.fromEntries(CANDIDATES.map((c, i) => [c, langIds[i]]))));
+
+function resample(a, from, to) {
+  const r = from / to;
+  const 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;
+}
+
+for (const [lang, text] of [
+  ['ta', 'மூவாயிரம் நானூற்று இருபத்தேழு புதிய லீட்கள் உள்ளன.'],
+  ['en', 'There are three thousand four hundred and twenty seven new leads.'],
+]) {
+  const spoken = await synthesize(text, lang);
+  const audio = resample(spoken.audio, spoken.sampling_rate, 16000);
+
+  const inputs = await processor(audio);
+  const t0 = Date.now();
+  const out = await model({
+    ...inputs,
+    decoder_input_ids: new Tensor('int64', BigInt64Array.from([BigInt(sot)]), [1, 1]),
+  });
+  const ms = Date.now() - t0;
+
+  const logits = out.logits;
+  const last = logits.dims[1] - 1;
+  const vocab = logits.dims[2];
+  const row = logits.data.slice(last * vocab, (last + 1) * vocab);
+
+  const scores = langIds.map((id) => Number(row[id]));
+  const max = Math.max(...scores);
+  const exp = scores.map((s) => Math.exp(s - max));
+  const sum = exp.reduce((a, b) => a + b, 0);
+  const probs = exp.map((e) => e / sum);
+  const best = probs.indexOf(Math.max(...probs));
+
+  console.log(`spoken ${lang} → detected ${CANDIDATES[best]} `
+    + `(${CANDIDATES.map((c, i) => `${c} ${probs[i].toFixed(3)}`).join(', ')}) in ${ms}ms `
+    + `${CANDIDATES[best] === lang ? '✅' : '❌'}`);
+}
+process.exit(0);