Smart-Translate AI (On-Device Edition)
Budget: $15 – $25 USD
1. Project Objective
To build a high-performance mobile application capable of seamless speech-to-speech and text translation without an internet connection.
2. Technical Stack
AI Models: * Translation (NMT/LLM): Quantized (4-bit/8-bit) SLMs, specifically Gemma 2 2B, Llama 3.2 (1B/3B), or Phi-3.5 Mini.
Speech-to-Text (STT): Offline Whisper models (Tiny or Base variants).
Text-to-Speech (TTS): Integration of system libraries (Google TTS for Android / AVFoundation for iOS).
Deployment Frameworks: * Inference Engines: Google MediaPipe LLM Inference API, TensorFlow Lite, or ONNX Runtime.
Hardware Acceleration: Mandatory optimization for NPU and GPU (Apple A-series, Snapdragon, and Dimensity) to ensure energy efficiency.
Development Platforms: Native development (Swift for iOS, Kotlin for Android) is prioritized for maximum performance.
3. Workflow & Performance KPIs
Process: Input (Voice/Text) → Offline STT → Local SLM Processing → Output (Text/Audio).
Latency: End-to-end text translation latency must be under 500ms.
Footprint: Total application size (including models) should not exceed 1.5GB - 2GB.
Efficiency: The app must maintain stable thermal performance and low battery consumption during continuous use (15-20 minutes).
4. Deployment Environment
Android: Version 10 or higher, 4GB+ RAM, ARM64 architecture.
iOS: iPhone 12 (A14 Bionic) or newer to ensure NPU compatibility.
To build a high-performance mobile application capable of seamless speech-to-speech and text translation without an internet connection.
2. Technical Stack
AI Models: * Translation (NMT/LLM): Quantized (4-bit/8-bit) SLMs, specifically Gemma 2 2B, Llama 3.2 (1B/3B), or Phi-3.5 Mini.
Speech-to-Text (STT): Offline Whisper models (Tiny or Base variants).
Text-to-Speech (TTS): Integration of system libraries (Google TTS for Android / AVFoundation for iOS).
Deployment Frameworks: * Inference Engines: Google MediaPipe LLM Inference API, TensorFlow Lite, or ONNX Runtime.
Hardware Acceleration: Mandatory optimization for NPU and GPU (Apple A-series, Snapdragon, and Dimensity) to ensure energy efficiency.
Development Platforms: Native development (Swift for iOS, Kotlin for Android) is prioritized for maximum performance.
3. Workflow & Performance KPIs
Process: Input (Voice/Text) → Offline STT → Local SLM Processing → Output (Text/Audio).
Latency: End-to-end text translation latency must be under 500ms.
Footprint: Total application size (including models) should not exceed 1.5GB - 2GB.
Efficiency: The app must maintain stable thermal performance and low battery consumption during continuous use (15-20 minutes).
4. Deployment Environment
Android: Version 10 or higher, 4GB+ RAM, ARM64 architecture.
iOS: iPhone 12 (A14 Bionic) or newer to ensure NPU compatibility.