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ThinkSpark ultra-lightweight dual-encoder architecture

ThinkSpark-4.4M — ultra-lightweight multilingual "thinking-sound" predictor

4,445,809 parameters (~4.4M). Smaller than a JPEG. A byte-level dual-encoder that predicts the human backchannel / thinking sound a voice agent should murmur in the gap between the user's speech (STT) and the agent's reply (TTS) — hmm, अच्छा, એક સેકન્ડ, ええと — in the right language, native script, register and emotion.

No GPU at inference. 2–8 ms on CPU. 259-row byte vocab, zero OOV across every script. Ships as ONNX into the Kupe voice worker (thinking_sounds.mode = "auto").

What it predicts

Given the user's current utterance (input, primary) + the past conversation (context, any language/mix), it outputs:

language (22): hi, mr, bn, gu, pa, ta, te, kn, ml, or, as, ur, hi_en, en, es, fr, de, pt, ja, zh, ar, ru
intent — 9 agent super-reactions: thinking, clarify, acknowledge, disagree, react_positive, empathize, soothe, impatient, silence
register, emotion, filler_type (sound / word / sound_word / words / none)

A curated filler dictionary then samples the actual spoken spark for (language, intent, filler_type) — or silence when the model says no_filler.

Test metrics

metric	value
intent acc / top-2 / macro-F1	0.440 / 0.640 / 0.340
language acc	0.967
emotion / filler_type acc	0.444 / 0.536

Usage

from huggingface_hub import snapshot_download
from thinkspark.infer import ThinkSparkPredictor

local = snapshot_download("anuj-inavlabs/ThinkSpark-4.4M")
pred = ThinkSparkPredictor(local, f"{local}/filler_dictionary.json")

pred.predict(
    input_text="अरे यार फिर से वही दिक्कत",
    context="User: रिफंड नहीं आया\nAgent: sir I am checking\nUser: कितनी बार बोलूँ",
)
# -> {'spark': 'माफ़ कीजिए', 'intent': 'soothe', 'language': 'hi', ...}

Architecture

Byte tokenizer (UTF-8, vocab 259) → input encoder (5 layers, d=192) + context encoder (3 layers) → input cross-attends into context (input = query, context = key/value) → 5 heads. Trained with focal loss + class-balancing; native-script language is pinned by a script guard at inference.

Generated by kupe-thinkspark; data via Sarvam. Native-speaker QA recommended for urban/code-mixed and low-resource fillers.

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