Stand-in import of elffuss/translator
One parentless commit with only the files that the open seed tasks touch and the modules they import. The full squashed import (SPEC 8.6) is a later step: this tree is not the cutoff tree. T2T-StandIn-Source-Commit: 66cbe8ecfb77846b914af0eea430a84f504ce2eb
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LICENSE
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LICENSE
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3
README.md
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3
README.md
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# translator
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Stand-in of the translator repository for the public testnet of Elffuss Token to Token (T2T): only the files that some open task touches, and the modules they import. The full import is a later step.
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diarize.mjs
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diarize.mjs
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// diarize.mjs — diarización por turnos 100% en el navegador (ONNX).
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// pyannote-segmentation-3.0 (fronteras habla/silencio) → wespeaker resnet34 (embeddings) →
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// clustering greedy por coseno con filtrado de singletons. Devuelve turnos etiquetados por hablante.
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//
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// LÍMITE HONESTO: separar voces SIMULTÁNEAS en un único canal mono NO es fiable (el hablante más
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// fuerte domina el embedding). Eso se resuelve en la UX con una captura por interlocutor. Aquí
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// resolvemos el 95% real: quién habló en cada turno.
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//
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//
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// LICENCIAS (auditado 2026-09-01) — ambos modelos permiten uso comercial, pero el segundo obliga:
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// · onnx-community/pyannote-segmentation-3.0 MIT
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// · onnx-community/wespeaker-voxceleb-resnet34-LM CC BY 4.0 → EXIGE ATRIBUCION VISIBLE.
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// El credito vive en el <footer> de index.html (translator y copilot). No lo quites.
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// NO meter aqui los modelos de Rev (reverb-diarization-v1/v2): licencia NO COMERCIAL.
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// const D = await loadDiarizer(TF, {device:'webgpu'});
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// const turns = await diarize(D, audioFloat32_16k, {joinTau:0.55, maxSpeakers:2});
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// // → [{start,end,speaker:'S1'|'S2', sim}]
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export async function loadDiarizer(TF, { device = 'wasm' } = {}) {
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TF.env.allowLocalModels = false;
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const seg = await TF.AutoModelForAudioFrameClassification.from_pretrained('onnx-community/pyannote-segmentation-3.0', { device });
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const segP = await TF.AutoProcessor.from_pretrained('onnx-community/pyannote-segmentation-3.0');
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const emb = await TF.AutoModel.from_pretrained('onnx-community/wespeaker-voxceleb-resnet34-LM', { device });
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const embP = await TF.AutoProcessor.from_pretrained('onnx-community/wespeaker-voxceleb-resnet34-LM');
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return { seg, segP, emb, embP };
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}
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const cos = (a, b) => { let dt = 0, na = 0, nb = 0; for (let i = 0; i < a.length; i++) { dt += a[i]*b[i]; na += a[i]*a[i]; nb += b[i]*b[i]; } return dt / (Math.sqrt(na)*Math.sqrt(nb) || 1); };
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const rms = (a) => { let s = 0; for (const v of a) s += v*v; return Math.sqrt(s/a.length); };
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async function embed(D, slice) {
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if (slice.length < 8000) { const q = new Float32Array(8000); q.set(slice); slice = q; }
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const o = await D.emb(await D.embP(slice));
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const t = o.embeddings || o.logits || Object.values(o)[0];
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return Array.from(t.data);
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}
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// Embedding de hablante de un clip (Float32 16 kHz) — para diarización ONLINE (etiquetar turno a turno).
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export async function speakerVec(D, audio) { return embed(D, audio); }
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export function cosine(a, b) { return cos(a, b); }
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// Clasificador online de hablante: acumula centroides y asigna cada intervención a S1/S2/…
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export function makeSpeakerBook({ tau = 0.5, maxSpeakers = 4 } = {}) {
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const cents = []; // {v, n, label}
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return {
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assign(vec) {
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let bi = -1, bv = -2;
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for (let i = 0; i < cents.length; i++) { const s = cos(vec, cents[i].v); if (s > bv) { bv = s; bi = i; } }
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if (bi >= 0 && bv >= tau) { const c = cents[bi]; for (let k = 0; k < c.v.length; k++) c.v[k] = (c.v[k]*c.n + vec[k])/(c.n+1); c.n++; return { label: c.label, sim: +bv.toFixed(2), isNew: false }; }
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if (cents.length >= maxSpeakers) { const c = cents[bi]; return { label: c.label, sim: +bv.toFixed(2), isNew: false }; }
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const label = 'S' + (cents.length + 1); cents.push({ v: vec.slice(), n: 1, label }); return { label, sim: bi >= 0 ? +bv.toFixed(2) : 0, isNew: true };
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},
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count() { return cents.length; },
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};
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}
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// audio: Float32Array mono 16 kHz
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export async function diarize(D, audio, { joinTau = 0.55, mergeTau = 0.5, minSpeechRms = 0.015, win = 1.0, step = 0.5, maxSpeakers = 4 } = {}) {
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// 1) regiones de habla (buenas fronteras cuando hay pausa entre turnos)
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const out = await D.seg(await D.segP(audio));
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const rp = D.segP.post_process_speaker_diarization(out.logits, audio.length)[0] || [];
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let regions = rp.filter(s => (s.end - s.start) >= 0.3).map(s => [+s.start, +s.end]).sort((a, b) => a[0]-b[0]);
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if (!regions.length) regions = [[0, audio.length/16000]];
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// 2) átomos: sub-ventanas 1.0s/0.5s con energía. Se descartan ventanas cortas (colas) que
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// degradan el embedding y generan hablantes fantasma.
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const atoms = [];
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for (const [rs, re] of regions) {
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for (let t = rs; t < re - 0.15; t += step) {
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const s = t, e = Math.min(t + win, re);
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if (e - s < 0.6) continue;
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const sl = audio.slice(Math.floor(s*16000), Math.ceil(e*16000));
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if (rms(sl) < minSpeechRms) continue;
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atoms.push({ s, e, emb: await embed(D, sl) });
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}
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}
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if (!atoms.length) return [];
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// 3) clustering greedy por coseno (con duración acumulada por cluster)
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const C = [], N = [], DUR = [];
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for (const a of atoms) {
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let bi = -1, bv = -2;
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for (let i = 0; i < C.length; i++) { const s = cos(a.emb, C[i]); if (s > bv) { bv = s; bi = i; } }
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if (bi >= 0 && bv >= joinTau) { for (let k = 0; k < C[bi].length; k++) C[bi][k] = (C[bi][k]*N[bi] + a.emb[k])/(N[bi]+1); N[bi]++; DUR[bi] += (a.e - a.s); a.c = bi; }
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else { C.push(a.emb.slice()); N.push(1); DUR.push(a.e - a.s); a.c = C.length - 1; }
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}
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// 4) clusters reales = ≥2 ventanas Y ≥1s de habla (los singleton/colas caen)
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let real = C.map((c, i) => ({ i, c, n: N[i], dur: DUR[i] })).filter(r => r.n >= 2 && r.dur >= 1.0);
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if (!real.length) real = C.map((c, i) => ({ i, c, n: N[i], dur: DUR[i] })).sort((a, b) => b.dur - a.dur).slice(0, 1);
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// fusiona centroides parecidos
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for (let i = 0; i < real.length; i++) for (let j = i+1; j < real.length; j++) {
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||||||
|
if (real[j] && cos(real[i].c, real[j].c) >= mergeTau) { real[i].n += real[j].n; real[j] = null; }
|
||||||
|
}
|
||||||
|
real = real.filter(Boolean).sort((a, b) => b.n - a.n).slice(0, maxSpeakers);
|
||||||
|
|
||||||
|
// 5) etiqueta cada átomo al centroide real más cercano; orden de aparición → S1,S2,…
|
||||||
|
const order = []; const labelOf = new Map();
|
||||||
|
const nearest = (e) => { let bi = 0, bv = -2; for (let k = 0; k < real.length; k++) { const s = cos(e, real[k].c); if (s > bv) { bv = s; bi = k; } } return { k: bi, sim: bv }; };
|
||||||
|
for (const a of atoms) { const { k } = nearest(a.emb); if (!labelOf.has(k)) { order.push(k); labelOf.set(k, 'S' + order.length); } }
|
||||||
|
// 6) construye turnos fusionando átomos contiguos del mismo hablante
|
||||||
|
const turns = [];
|
||||||
|
for (const a of atoms) { const { k, sim } = nearest(a.emb); const spk = labelOf.get(k);
|
||||||
|
const last = turns[turns.length - 1];
|
||||||
|
if (last && last.speaker === spk && a.s - last.end <= step + 0.3) { last.end = a.e; last.sim = Math.max(last.sim, +sim.toFixed(2)); }
|
||||||
|
else turns.push({ start: +a.s.toFixed(2), end: +a.e.toFixed(2), speaker: spk, sim: +sim.toFixed(2) });
|
||||||
|
}
|
||||||
|
return turns;
|
||||||
|
}
|
||||||
1
tests/harness/VERSION
Normal file
1
tests/harness/VERSION
Normal file
@@ -0,0 +1 @@
|
|||||||
|
1.0.0
|
||||||
Reference in New Issue
Block a user