Smart-Translate AI (On-Device Edition)

@Julian29

Hello, **** You can track the project’s progress using the tracker. I’m available to work 40 hours per week **** I bring 15+ years of proven experience in AI-driven mobile applications and on-device ML deployments, and I confidently understand your requirement for a fully offline, high-performance speech-to-speech translation app. Your goal is to build a scalable, privacy-focused, user-centric mobile solution that delivers real-time offline translation with optimal performance and energy efficiency. -->> Quantized SLM integration (Gemma / Llama / Phi) -->> Offline Whisper STT (Tiny/Base) optimization -->> Native TTS integration (Android/iOS) -->> NPU/GPU acceleration (A-series, Snapdragon, Dimensity) -->> <500ms latency & optimized model footprint I follow clean architecture, secure local processing, efficient model integration, and an agile workflow to ensure stable, production-ready delivery. I would approach your project by starting with wireframes and getting the UI/UX design completed before beginning the development phase. Let’s connect in chat, as I have a few technical queries to clarify before proceeding further. I am confident we can successfully implement this project from start to finish. Let’s build a platform that not only propels your business but also stands out prominently in the marketplace. Thanks & regards Julian

@corpmember29

Hello, {{{ I HAVE CREATED SIMILAR APPS BEFORE AND I CAN SHOW YOU }}} I can deliver a high-performance offline speech-to-speech and text translation mobile app using a native-first approach (Swift for iOS, Kotlin for Android) to fully leverage on-device acceleration (NPU/GPU). I have 10+ years of experience in mobile engineering, on-device AI, and performance-critical applications. I’ve worked with quantized SLMs (4-bit/8-bit), offline Whisper STT, and native TTS pipelines, and I’m comfortable deploying models via MediaPipe LLM Inference, TensorFlow Lite, and ONNX Runtime with hardware optimization for Apple A-series, Snapdragon, and Dimensity chipsets. I will implement the full offline workflow (STT → local SLM → TTS), focusing on <500ms latency, thermal stability, and battery efficiency, while keeping the total app size within 1.5–2GB. Code will be clean, well-documented, and structured for future model swaps or feature expansion. I WILL PROVIDE 2 YEARS FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. We will work with Agile methodology, and I will assist you from zero to publishing on stores, including profiling, optimization, and QA validation on target devices. I eagerly await your positive response. Thanks.

@kamran2012

Hello, I will build the on-device translation app natively in Swift and Kotlin with Whisper Tiny for STT, a quantized Gemma 2 2B for translation, and system TTS for audio output. I will run inference through MediaPipe with NPU delegation on both A-series and Snapdragon chips to hit the sub-500ms latency target while keeping thermal output stable for continuous use. One thing to address early: Whisper Tiny gives you the smallest footprint but struggles with accented speech in noisy environments. I will benchmark Whisper Base alongside Tiny during the first sprint so we can decide the accuracy-vs-size tradeoff before locking the final model into the build. Questions: 1) Which language pairs are needed at launch? 2) Should both platforms ship simultaneously, or iOS first then Android? 3) Is BLE listed for a companion device or a real-time audio streaming use case? Send me a message and we can go over the details. Best regards, Kamran

@mrresearcher99

Hi there, I’ve reviewed your Android app requirements and would love to collaborate on your project. With 5+ years of experience in native Android development, I specialize in building high-performance, user-friendly apps with clean UI, optimized architecture (MVVM/MVI), and seamless API integration. I’ll start with a clear project roadmap, provide regular progress updates, and ensure the app is thoroughly tested for stability and performance before launch. Let’s connect to discuss your app idea in detail — I’m ready to bring your vision to life! Best, Bhargav Android Developer | Kotlin & Java Expert

@technologybeach

With a robust background in Full-Stack Development and Mobile App Engineering, I am confident in my ability to spearhead your Smart-Translate AI project. Fluent in Swift and Kotlin, I have extensive experience deploying high-performing applications across various platforms. Furthermore, my proficiency in TensorFlow Lite, Google TTS for Android, and AVFoundation for iOS make me an ideal candidate for integrating the components crucial to on-device translation. My track record of delivering scalable and intuitive solutions aligns perfectly with the requirements of this project. I understand the importance of optimizing for NPU and GPU, ensuring energy efficiency without compromising performance. Having deployed complex applications within limited size constraints, I can guarantee that the total application size will not exceed 2GB while providing seamless functionality. In conclusion, I would bring a potent combination of technical expertise and a problem-solving mindset to deliver an optimized translation application. My dedication to stable thermal performance, low battery consumption, and providing smooth offline text-to-speech capabilities would result in reliable experiences for users even without an internet connection. Let's translate your vision into a reality with Smart-Translate AI (On-Device Edition).

@OutsourceMan

⭐⭐⭐⭐⭐ We at CnELIndia, led by Raman Ladhani, can ensure the successful delivery of your high-performance offline translation app by combining deep expertise in mobile app development and AI model deployment. Our approach includes native iOS (Swift) and Android (Kotlin) development optimized for NPU/GPU acceleration, ensuring latency under 500ms and stable thermal performance. We will integrate quantized SLMs like Gemma 2 2B, Llama 3.2, or Phi-3.5 Mini with offline Whisper STT and system TTS, keeping the total app footprint within 2GB. Raman Ladhani’s experience in ML and NLP will guide model selection, fine-tuning, and efficient inference on-device. CnELIndia will also implement rigorous testing across iOS and Android devices, ensuring seamless speech-to-speech and text translation with minimal battery consumption, delivering a robust, user-ready application.

@n2rtechnologies

Hello, I am excited about the opportunity to develop the Smart-Translate AI application. My experience in creating high-performance mobile applications positions me well to deliver a seamless speech-to-speech translation experience that you envision. I understand the importance of real-time processing and user-friendly interfaces in such applications, ensuring that the end product meets your expectations for performance and usability. I am committed to building a solution that not only meets your specifications but also enhances user engagement through intuitive design and functionality. I look forward to discussing how I can contribute to your project and deliver the high-quality application you need. Regards, Nurul Hasan

@einnovention

With our deep expertise and specialization in native mobile app development, your project 'Smart-Translate AI (On-Device Edition)' is right up our alley. Our team at Einnovention has over 248 successful projects under its belt, and we're proud to highlight our consistent 4.9/5 rating on Freelancer. Partnering with us means you'll be working with a highly trusted professional with proven capabilities of delivering high-quality, scalable, and innovative solutions - a perfect match for your project objectives. We understand the great emphasis that you've put on efficiency and performance benchmarks like end-to-end translation latency, application size limit, and stable thermal performance. Our team has hands-on experience in precisely the areas you require - deploying iOS apps compatible with Apple A-series, and Snapdragon or Dimensity accelerated Android apps to guarantee energy efficiency. Additionally, we've consistently demonstrated the ability to keep app sizes in check without compromising functionality - a trait that will come handy in ensuring your app's storage requirements don't exceed the 2GB threshold.

@rbtech1984

Hello There!!! ⭐⭐⭐⭐(Smart-Translate AI On-Device Edition)⭐⭐⭐⭐ Project understanding: I understand you need a high-performance mobile app for offline speech-to-speech and text translation, using compact AI models, optimized for NPU/GPU on iOS and Android, with minimal latency, low battery consumption, and a small app footprint. Services mentioned here based on project details ⚜ Native iOS (Swift) and Android (Kotlin) app development ⚜ Integrate offline STT using Whisper Tiny/Base models ⚜ Implement local translation using quantized Gemma 2, Llama 3.2, or Phi-3.5 Mini models ⚜ Integrate TTS with AVFoundation (iOS) and Google TTS (Android) ⚜ Optimize inference with TensorFlow Lite, ONNX Runtime, or MediaPipe LLM API ⚜ Ensure hardware acceleration, low latency (<500ms), and energy efficiency ⚜ Maintain app size under 2GB and stable thermal/battery performance I have 9+ years experience in mobile development, ML, and NLP, and have built offline AI-powered apps before. I plan to use optimized quantized models with TFLite/ONNX, native code, and hardware acceleration to deliver smooth, offline translations. Excited to discuss and bring this advanced translation app to life! Warm Regards, Farhin B.

@TechizeBuilder

Hi, I came across your project "Smart-Translate AI (On-Device Edition)" and I'm confident I can help you with it. About Me: I'm a agency owner with over 8+ years of experience in Mobile App Development, Android, iPhone. , and I understand exactly what’s needed to deliver high-quality results on time. Why Choose Me? - ✅ Expertise in required Technologies and 1 year post deployment free support - ✅ On-time delivery and excellent communication - ✅ 100% satisfaction guarantee Let’s discuss your project in more detail. I’m available to start immediately and would love to hear more about your goals. Looking forward to working with you! Best regards, Deepak

@mahig47

Hello! As per your project post, you’re looking to build Smart Translate AI On Device Edition, a high performance mobile application capable of fully offline speech to speech and text translation using quantized small language models and optimized inference engines. I'M GLAD TO SAY THAT I HAVE ALREADY DEVELOPED AN TRANSLATION APP SO I HAVE EXPERIENCE IN THIS PROJECT. My focus will be on delivering a native performance optimized solution featuring offline Whisper based speech to text, quantized SLM inference using Gemma, Llama, or Phi models, and system level text to speech integration for Android and iOS. The pipeline will be structured as voice or text input to local STT to on device translation model to text or audio output, with hardware acceleration for NPU and GPU using TensorFlow Lite, ONNX Runtime, or MediaPipe inference APIs. I specialize in AI powered mobile systems, model quantization strategies, edge inference optimization, and native Swift and Kotlin development. My approach will prioritize low latency token streaming, memory efficient model loading, optimized batching for phoneme processing, and sustained thermal performance across Apple A series and Snapdragon class chipsets. I am confident I can help architect and deliver a highly optimized offline translation system that meets your latency and efficiency targets. Best regards, Nikita Gupta.

@xuezhe315

Hello, With over a decade of experience in software engineering, I bring much more than just technical skills to your project. In line with your needs, I've deployed systems that efficiently handled millions of users while maintaining high performance. This project's particular specifications – from the on-device AI models to optimizing for energy efficiency – falls squarely in my wheelhouse. My machine learning (ML) expertise extends to NLP, predictive modeling, and custom model training and deployment across major frameworks such as TensorFlow and PyTorch. Your Smart-Translate AI project would benefit from my proficiency in these areas. On the mobile app front, I have strong React Native skills which you listed as a priority, delivering on pixel-perfect UI/UX and indigenous performances that match your tech stack preferences. Given the intricacies involved in your project; adhering strictly to tight latency requirements while ensuring low battery consumption in deployment environments is no small feat – one that I'm confident to deliver on successfully. Let's collaborate to bring your Smart-Translate AI vision into flawless reality! Regards.

@SakibKaiser

Hello, The true bottleneck in offline mobile AI isn’t running the model—it’s hitting sub-500ms latency without draining the battery or overheating the device. As an AI Engineer specializing in edge inference, I recently built a live voice conversational AI avatar that required flawlessly stitching together offline Whisper (STT), a local LLM, and real-time TTS. I know exactly how to manage mobile memory and audio buffers to make offline AI feel as fast as a cloud API. My Precise Execution for Your App: -Extreme Quantization: I will compress Llama 3.2 or Phi-3.5 using 4-bit (INT4), keeping the total app footprint strictly under your 1.5GB limit without sacrificing translation logic. -Native Hardware Acceleration: Using ONNX Runtime and MediaPipe LLM Inference, I will build directly in native Swift and Kotlin. This zero-overhead bridging delegates the heavy lifting to the Apple Neural Engine (CoreML) and Android NNAPI, ensuring stable thermals for 20+ minute sessions. -Real-Time Pipeline: Direct memory access between Whisper STT, the local SLM, and system TTS (AVFoundation/Google TTS) guarantees we hit your <500ms end-to-end latency KPI. I am ready to architect a highly optimized, production-ready offline engine. Let’s discuss your preferred base SLM to get started. Best Regards, Shakib A.

@Shaiwan

❗❕‼️⁉️ Hello ❗❕‼️⁉️ You need a high-performance offline speech-to-speech and text translation app with quantized LLMs, Whisper STT, and NPU/GPU optimization for iOS and Android. I HAVE SOME QUESTIONS REGARDING THE PROJECT SEND ME A MESSAGE FOR MORE DISCUSSION ❗❕❗❕❗❕ What I offer: ⇆ ⇆ ⇆ ★ Model selection and 4/8-bit quantization (Gemma 2, Llama 3.2, Phi-3.5 Mini) for mobile inference ★ Offline Whisper Tiny/Base integration with real-time streaming STT pipeline ★ Native Swift (iOS) & Kotlin (Android) development for low-latency performance ★ TensorFlow Lite / ONNX Runtime / MediaPipe inference optimization for NPU & GPU ★ Footprint optimization under 2GB, latency <500ms, with battery and thermal efficiency ★ End-to-end pipeline: Voice/Text input → SLM processing → TTS output with offline support ⇆ ⇆ ⇆ ➷➷➷ With 7+ years in mobile AI/ML development, I’ve delivered on-device LLM and speech apps with hardware acceleration and optimized inference. Deep expertise in performance tuning ensures low latency, energy efficiency, and scalable architecture. First, benchmark devices and select optimal quantized models. Second, implement native offline STT → LLM → TTS pipeline. Third, optimize performance, footprint, and battery usage for production. Let’s chat to discuss device targets and workflow priorities. Best Regards, Shaiwan Sheikh

@arbu1499

Hi, This is a serious edge-AI build, and I appreciate how clearly you’ve defined the constraints—offline STT (Whisper Tiny/Base), quantized SLMs like Gemma 2 2B / Llama 3.2 / Phi-3.5 Mini, and strict latency (<500ms) with a 1.5–2GB footprint. I’m comfortable architecting a native Swift (iOS) and Kotlin (Android) solution using ONNX Runtime or TensorFlow Lite, optimizing for NPU/GPU acceleration (A14+ and Snapdragon/Dimensity) to maintain thermal stability and battery efficiency during 15–20 minute sessions. I’d focus on aggressive quantization, memory mapping, async pipelines (STT → SLM → TTS), and hardware delegation to meet your KPIs cleanly. This is exactly the kind of performance-first system design challenge I enjoy tackling. Looking forward for your positive response in the chatbox. Best Regards, Arbaz T

@kishanbwts

Your goal to build a fully offline, low-latency speech-to-speech translator on mobile devices is clear. To meet the 500ms latency and 2GB size limits, I would focus on efficient quantization of the Gemma 2 and Llama 3.2 models paired with carefully chosen Tiny Whisper for STT. I’ve worked on similar on-device translation apps using TensorFlow Lite and ONNX optimized for Snapdragon NPUs, which helped cut latency by about 30% and keep the app size lean. A key point is optimizing the inference pipeline to avoid bottlenecks between STT, SLM processing, and TTS. Have you considered a streaming approach for Whisper output to start translation before full STT completion? Also, do you have specific audio formats or user cases in mind that could influence buffer sizes and model input tuning? For GPU/NPU acceleration, I’d leverage Metal on iOS and MediaPipe APIs on Android to balance power and speed, avoiding thermal spikes during 20-minute sessions. Native Swift/Kotlin builds will unlock performance benefits and ensure smooth system TTS integration. I’m ready to help architect and prototype this. Let’s start by defining a minimal viable pipeline and push on latency testing in real device conditions.

@Cmonosoft

Hi there, I am a senior mobile and AI application developer with over 8 years of experience building high performance native applications for iOS and Android. My expertise includes on device machine learning, TensorFlow Lite, ONNX Runtime, MediaPipe, and hardware accelerated inference using Apple Neural Engine and Qualcomm Snapdragon NPUs. I will build a fully offline speech to speech and text translation application using quantized 4 bit or 8 bit small language models such as Gemma 2 2B, Llama 3.2, or Phi 3.5 Mini. Whisper Tiny or Base will be optimized for offline speech to text, and native TTS engines will be integrated through AVFoundation on iOS and system TTS on Android. The architecture will use Swift and Kotlin for maximum performance, with inference powered by TensorFlow Lite or ONNX Runtime and optimized for GPU and NPU acceleration. Model loading, memory mapping, and batching strategies will ensure end to end latency under 500 milliseconds while maintaining stable thermal performance during 15 to 20 minutes of continuous usage. I look forward to discussing implementation! Thanks Saurabh

@Web1devloper

As an accomplished data analyst and machine learning professional, I see extraordinary potential in this project. With over eight years of experience successfully modeling complex datasets into valuable business insights, I am no stranger to advanced AI tasks like the ones you have outlined. I have deployed numerous predictive analytic solutions and built end-to-end data systems informed by SQLAlchemy, PyTorch, Jupyter Notebooks, just to name a few of my tech stack. Additionally, thanks to cutting-edge ETL tools like Talend and Azure Data Factory in my repertoire, I won't find it daunting to optimize your app for energy efficiency on diverse hardware like Apple A-series and Snapdragon Dimensity – a key requirement you've mentioned. My work on BigQuery also satisfies your performance KPI need of low latency translations even without an internet connection. Finally, my significant involvement with different industries from finance to healthcare equips me with an arsenal of experience needed to convert high-level requirements into solution-centric plans. I genuinely believe my genuine passion for language technology merged with my technical skills will offer immense value to make your Smart-Translate AI (On-Device Edition) a groundbreaking reality. The choice is yours; let's make the right one!

@josec298

⭐⭐⭐⭐⭐ DEAR CLIENT ⭐⭐⭐⭐⭐ Hi, —Senior Mobile AI Developer with experience in on-device ML (Whisper TFLite, quantized LLMs). Build plan: STT: Offline Whisper Tiny/Base via TensorFlow Lite (Android)/CoreML (iOS). Translation: Quantized Gemma 2 2B or Llama 3.2 1B via MediaPipe LLM Inference for NPU/GPU accel. TTS: Native AVSpeechSynthesizer (iOS) + Android TTS. Target <500ms latency, <2GB footprint. $22/hr, 80–100 hours. Ready to prototype!