100% On-Device Neural Pipeline • Zero Cloud Latency Pipeline Neuronal 100% en Dispositivo • Cero Nube

AI World Translator & Tutor
Private. Real-Time. Offline. Privado. En Tiempo Real. Sin Internet.

Engineered with quantized Llama 3.2 1B, Gemma 4 E2B QAT, and Whisper STT. Experience military-grade privacy, instant speech-to-speech translation, structured PDF layout recovery, and dynamic voice tutoring—all running locally on hardware. Diseñado con modelos cuantizados Llama 3.2 1B, Gemma 4 E2B QAT y Whisper STT. Experimenta máxima privacidad, traducción voz a voz instantánea, preservación estructural de PDFs y tutoría conversacional por voz ejecutada 100% de forma local.

🎮 Try Interactive SimulatorProbar Simulador Interactivo 📐 View ArchitectureVer Arquitectura
Conversational Core
Llama 3.2 (1B Q4_K_M)
30–45 tokens/sec for real-time speech dialogue.
Document Precision
Gemma 4 E2B QAT
Instruction-following for XML/PDF geometry reconstruction.
Zero-Latency Bypass
all-MiniLM-L6-v2
Vector semantic caching & voice command interception (<150ms).
Audio Pipeline
Whisper STT + Sherpa TTS
Reactive VAD and synchronized word-level timestamps.
Live Demo ExperienceExperiencia Interactiva

Experience The 4 Operational ModesExperimenta los 4 Modos Operativos

Interact with the simulated on-device neural subsystem directly in your browser. Interactúa directamente con el subsistema neuronal simulado en tu navegador.

● Input Ready
● Neural Output
La computación cuántica aprovecha la mecánica cuántica para resolver problemas complejos de forma exponencial.
Latency: 1.24s Engine: Llama 3.2 1B (Quantized Q4_K_M)
🇺🇸

Speaker A (English)

"Good morning! Can you guide me through the on-device translation pipeline?"
Click to Speak Clic para Hablar
🇪🇸

Speaker B (Español)

"¡Buenos días! Con gusto te explico cómo procesamos voz a voz 100% offline con Whisper y Llama."

INTERNATIONAL COMMERCIAL CONTRACT

Article 4.2 — Confidentiality Clause: Both parties agree that proprietary algorithms, models, and weights remain strictly sovereign and protected without cloud telemetry.

[Segmented with ML Kit OCR -> Processed via SQLite Page Queue]

📦
Virtual SQLite Memory Paging

Heavy 100-page PDFs are chunked into independent tasks, preventing Out-Of-Memory crashes on mobile.

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Gemma 4 Geometric Layout Reconstruction

Bounding boxes and XML nodes are reconstructed identically in the target output without formatting loss.

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Translation Memory (TM) 95% Match Hit

Common contractual paragraphs bypass LLM computation in 3 seconds per page via cosine vector search.

🤖

Anima AI Voice Tutor

Active In-Device Coach

Click 'Play Tutor Feedback' to hear synchronous word-level dynamic highlights.

Real-Time Linguistic Telemetry

✨ Semantic Accuracy: 98.4%

The feedback loop evaluates phonetic nuances with Whisper STT, constructs grammatical improvements via Llama 3.2, computes cosine distance with MiniLM embeddings, and highlights each spoken word synchronously using Sherpa-ONNX audio timestamps.

Engineering ArchitectureArquitectura de Ingeniería

Native Hybrid PipelinePipeline Híbrido Nativo

Zero external API dependencies. Full air-gap execution on ARM64 / Metal / Vulkan hardware. Cero dependencias de APIs externas. Ejecución 100% aislada en hardware ARM64 / Metal / Vulkan.

PHASE 01 // INGESTION
Whisper STT (ggml-tiny)
Real-time voice-to-text transcription with continuous Voice Activity Detection (VAD) without audio streaming leaks.
Quantized int8 • 25ms latency
PHASE 02 // BYPASS CHECK
Semantic Cache & Commands
all-MiniLM-L6-v2 vectors match queries against SQLite FTS5. If similarity >95%, LLM is bypassed in <150ms.
23MB Model • ONNX Runtime
PHASE 03 // NEURAL REASONING
Llama 3.2 1B & Gemma 4
Low-latency grammar correction, cultural idioms, and JSON-structured document page translation.
Metal / Vulkan Hardware JSI
PHASE 04 // SYNCHRONOUS VOICE
Sherpa-ONNX Neural TTS
Natural human voice synthesis with millisecond-exact word-level timestamps for UI karaoke illumination.
Piper int8 • Dynamic Highlights

📊 On-Device Performance Metrics (ARM64 Snapdragon 8 / Apple A15)

Subsystem Model Pipeline Average Latency User Impact
Semantic Cache Hit Whisper -> MiniLM -> SQLite TM 150ms Instantaneous response without invoking heavy LLMs.
Verbal Translation Whisper -> Llama 3.2 1B -> Sherpa TTS 1.2s – 1.6s Natural back-and-forth conversational dialogue flow.
Interactive AI Tutor Whisper -> Llama (x2) -> MiniLM -> TTS 1.8s – 2.2s Deep evaluation with phonetic score & karaoke highlights.
Structured PDF (TM Hit) ML Kit OCR -> MiniLM -> SQLite 3.0s / page Translates complex PDFs preserving exact geometries.
Glossary RAG Injection SQLite Vector Search -> Prompt Anchor +20ms Zero perceptible latency overhead during inference.