Smart-Translate AI (On-Device Edition)

@jastp

Greetings from Logictrix! My name is Jas, and I am looking forward to discuss about your app in details over the Chat or Call. We have enough app developers available in team so I can assure you to deliver this project at a fairly low cost with great quality and with a commitment for long term support. *** We have now earned 'Expertise' level in AI, ChatGpt and a couple of other AI platforms for App development and other Chatbot work! *** We have developed around 400+ Android and iOS apps using Native and Flutter SDK in the past 15 years, Many apps are Live in Google play and App store. We will share our detailed portfolio over the Chat once we connect. Looking forward to your reply... Thanks and Regards Jas

@AwaisChaudhry

As a team of seasoned, multidisciplinary developers at ZAWN Tech with over a decade of experience, we are confident in our abilities to tackle the Smart-Translate AI (On-Device Edition) project. Our expertise spans key areas in this project such as AI Model Development, Android and iPhone app development, and Machine Learning (ML). This amalgamation ensures that we deliver end-to-end solutions that are robust, efficient, and tailored to meet your unique needs. Our proficiency in Native Development (Swift and Kotlin) for maximum performance aligns seamlessly with your project's requirements. In addition, our extensive skills span computer vision applications including OCR systems which is pertinent to this project's natural language processing aspect. Ensuring a compact yet powerful application within the limited storage space of 1.5GB -2GB is not a novice task for us. We have previously built similar products with specific size constraints. Thus, we can effectively strategize, optimize, and deliver at speed without compromising on quality. Lastly, we appreciate the importance of strong support and clear communication throughout the project lifecycle as well as post-delivery.

@leadconcept

Hi, We are a team of senior full-stack and AI engineers with strong experience building high-performance mobile applications with on-device AI inference. We can develop your fully offline speech-to-speech and text translation app within 10–12 weeks, covering quantized SLM integration, offline Whisper STT, optimized TTS integration, and hardware-accelerated inference for both Android and iOS. For optimal latency under 500ms, would you prefer MediaPipe LLM Inference with aggressive 4-bit quantization, or ONNX Runtime with custom NPU delegates for tighter hardware control on Snapdragon and Apple Neural Engine? Our native Swift and Kotlin developers will ensure efficient memory handling, GPU/NPU acceleration, thermal stability, and model size optimization within the 1.5–2GB footprint. We will deliver clean architecture, documented model pipelines, performance benchmarking results, and deployment-ready builds. We also provide 5 months FREE support and long-term collaboration guarantee. FYI. The current bid amount is a placeholder to submit the proposal. Look forward to hearing from you. Ragards Yasir LEADconcept PS: Let me know, if you want to see our team past work to determine our skills/expertise or past customer's references.

@Julian29

Hello, I have 8+ years of proven experience in AI/ML mobile applications, I confidently understand your goal: to build a high-performance, fully offline speech-to-speech and text translation app optimized for modern mobile devices. **** You can track the project’s progress using the tracker. I’m available to work 40 hours per week **** -->> Offline STT using Whisper Tiny/Base models -->> On-device NMT/LLM translation (Gemma 2, Llama 3.2, Phi-3.5 Mini) -->> TTS output via system libraries (AVFoundation / Google TTS) -->> Hardware-accelerated inference using NPUs and GPUs (Apple A-series, Snapdragon, Dimensity) -->> Native development for Swift (iOS) and Kotlin (Android) ensuring low latency and battery efficiency I follow clean modular architecture, efficient on-device inference pipelines, and rigorous optimization workflows to deliver a lightweight, responsive, and energy-efficient AI application. I would approach your project by first profiling device capabilities and model quantization requirements, then implementing offline STT → translation → TTS pipelines, and I have a few technical queries to clarify in chat to ensure optimal performance. I am confident I can deliver this Smart-Translate AI app from start to finish. Thanks & regards Julian >>>>>>> We'll share our portfolio in Chat. Let's talk further speak over the freelancer call or chat. <<<<<<

@corpmember29

Hello, {{{ I HAVE CREATED SIMILAR APPS BEFORE AND I CAN SHOW YOU }}} I can deliver a fully offline, high-performance speech-to-speech and text translation mobile app built natively in Swift (iOS) and Kotlin (Android), optimized for Apple Neural Engine, Snapdragon, and Dimensity NPUs/GPUs. I have 10+ years of experience in mobile systems, on-device AI, and performance-critical applications, including offline inference pipelines. The solution will use quantized SLMs (4/8-bit) such as Gemma 2 2B, Llama 3.2 (1B/3B), or Phi-3.5 Mini, integrated via MediaPipe LLM, TensorFlow Lite, or ONNX Runtime, combined with offline Whisper (Tiny/Base) for STT and native system TTS (Google TTS / AVFoundation). The full pipeline will meet the <500ms latency target, stay within the 1.5–2GB footprint, and maintain thermal and battery stability under continuous use. I WILL PROVIDE 2 YEARS FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. We will work using Agile methodology, with clear milestones, profiling reports, and continuous optimization. I will assist from architecture and model selection to on-device benchmarking and production-ready builds. I eagerly await your positive response. Thanks.

@JijoThomas2020

I have hands on experience in developing a mobile translator app, to say more details, base model is transformer, and train by parallel corpus, and launch to light version to be embedded offline mobile apps. This experience is a great fit for your project. Please contact me so that we can discuss the details more. Thank you, Jijo

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Mobile App Development, iPhone, Android, Machine Learning (ML), Swift, Bluetooth Low Energy (BLE), Kotlin, Deep Learning, Natural Language Processing, AI Model Development and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.   Best regards, Sales department Tangram Canada Inc.     

@mrresearcher99

This is an exciting and technically solid brief — and it fits my skill set well. I’m a senior mobile/full-stack developer with 5+ years of experience in Android (Kotlin/Java), iOS integration, and performance-focused mobile apps. I’ve worked on on-device AI features, media processing, and hardware-optimized mobile workflows, so I’m comfortable building low-latency offline pipelines. How I can help: Native Android (Kotlin) and iOS-ready architecture Integration of offline STT (Whisper Tiny/Base) Quantized SLM deployment via TFLite / ONNX Runtime / MediaPipe NPU/GPU optimization for Snapdragon, Dimensity, and Apple A-series Efficient audio pipeline (Voice → STT → SLM → TTS) Memory, thermal, and battery profiling for sustained usage Model size optimization to stay within the 1.5–2GB footprint I focus heavily on real-device performance tuning and can work toward your <500ms latency target with proper model selection and quantization strategy. Can start immediately Clean, well-documented native code Comfortable with long-term iteration and optimization If you share target languages and priority device list, I can propose a concrete architecture and performance plan right away. Best, Bhargav

@OutsourceMan

⭐⭐⭐⭐⭐ We at CnELIndia, led by Raman Ladhani, can deliver this project by leveraging our deep expertise in high-performance mobile and AI development. Our approach will involve optimizing Gemma 2 2B, Llama 3.2, or Phi-3.5 Mini models for offline inference using TensorFlow Lite and ONNX Runtime, with dedicated NPU/GPU acceleration on Apple A-series, Snapdragon, and Dimensity chips. We will integrate Whisper Tiny/Base for offline STT and native TTS libraries (Google TTS/AVFoundation) to ensure seamless speech translation. Using Swift and Kotlin, we will build a native mobile application optimized for low latency (<500ms), minimal footprint (≤2GB), and energy-efficient performance. CnELIndia will handle model quantization, thermal and battery optimization, and rigorous testing across Android 10+/iOS 12+ devices to ensure a robust, fully offline experience.

@n2rtechnologies

Hello, I am excited about the opportunity to develop your Smart-Translate AI mobile application. My extensive experience in mobile app development positions me well to create a high-performance solution that seamlessly integrates speech-to-speech translation capabilities. I understand the importance of delivering a user-friendly interface that enhances communication across languages. In addition to ensuring a smooth user experience, I will focus on optimizing the app for performance and reliability, allowing it to function effortlessly on various devices. My approach includes thorough testing and iterative feedback to ensure that the final product meets your expectations and requirements. I look forward to discussing how I can contribute to your project and bring your vision to life. Regards, Nurul Hasan

@einnovention

As an experienced and adaptable software development team, specializing in native app development for both Android and iOS platforms, we at Einnovention are uniquely qualified to take on this Smart-Translate AI project. Our expertise lies in utilizing native frameworks like Swift for iOS, Kotlin for Android, ensuring maximum performance leveraging system libraries such as Google TTS for Android and AVFoundation for iOS. Thus, guaranteeing seamlessness and high-performance even without internet connectivity. We possess a deep understanding of Artificial Intelligence (AI) models required, having worked on various projects involving data analysis and AI-powered features. We are proficient with pre-trained models such as Gemma 2 2B, Llama 3.2 (1B/3B), or Phi-3.5 Mini, TensorFlow Lite, or ONNX Runtime that align with the project's scope. And to meet the application's stringent size requirements, we'll optimize these models using quantization techniques. Your project's end-to-end text translation timeline set at under 500ms isn't just a stretch goal; it's a promise we can deliver on. Our catalog comprises similar projects where real-time processing is key need like Live Captioning feature in one of our Social Networking App. We've also ensured small total application size and optimized energy efficiency on all mobile devices.

@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 NPUs and GPUs on iOS and Android, with minimal latency and efficient energy usage. Services mentioned here based on project details ⚜ Develop native iOS (Swift) and Android (Kotlin) apps for offline translation ⚜ Integrate offline STT using Whisper models (Tiny/Base) ⚜ Implement local LLM/SLM processing with Gemma 2, Llama 3.2, or Phi-3.5 Mini ⚜ Integrate TTS via AVFoundation (iOS) and Google TTS (Android) ⚜ Optimize inference using MediaPipe, TensorFlow Lite, or ONNX Runtime ⚜ Ensure NPU/GPU acceleration, low latency (<500ms), and energy efficiency ⚜ Maintain app footprint under 2GB with stable thermal and battery performance I have 9+ years experience in mobile app development, ML, and NLP, and have built offline AI-powered apps before. I plan to use optimized quantized models with TFLite/ONNX, hardware acceleration, and native code to deliver smooth, fast, offline translations. Excited to discuss and bring this advanced translation app to life! Warm Regards, Farhin B.

@Eliteinno

Hi, Our devs looked at your project, and we noticed the potential for a bottleneck in your system's architecture, particularly in how it currently scales with increased user load. Our backend lead can optimize this by implementing robust solutions to ensure smooth scaling and performance. We recently completed a large-scale e-commerce platform using React and Node.js, handling a 50% increase in traffic without a hitch. The project was delivered ahead of schedule, showcasing our ability to manage complex systems efficiently. I'll be your direct technical point of contact, ensuring clear communication. We'll set up a staging environment early on to test and refine features collaboratively. How do you envision your platform evolving over the next two years? Let's explore how we can make that vision a reality.

@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.

@Shaiwan

❗❕‼️⁉️ Hello ❗❕‼️⁉️ You want a fully offline speech-to-speech translation app with quantized SLMs, Whisper STT, and optimized NPU/GPU inference under strict latency and size limits. I HAVE SOME QUESTIONS REGARDING THE PROJECT SEND ME A MESSAGE FOR MORE DISCUSSION ❗❕❗❕❗❕ What I offer: ⇆ ⇆ ⇆ ★ Model selection & 4/8-bit quantization (Gemma/Llama/Phi) optimized for ONNX/TFLite ★ Offline Whisper Tiny/Base integration with streaming pipeline ★ Native Swift (iOS) & Kotlin (Android) development for max performance ★ NPU/GPU acceleration (A-series, Snapdragon, Dimensity) with thermal profiling ★ Footprint optimization under 2GB & <500ms latency tuning ★ End-to-end pipeline: STT → SLM → TTS with battery efficiency testing ⇆ ⇆ ⇆ ➷➷➷ With 7+ years in ML and mobile AI deployment, I’ve built on-device inference apps using TensorFlow Lite, ONNX, and hardware acceleration. Strong expertise in performance tuning ensures low latency, thermal stability, and scalable architecture. First, benchmark target devices and select optimal quantized models. Second, implement native inference pipeline with hardware acceleration. Third, optimize latency, footprint, and battery through profiling. Let’s discuss target languages and device priorities in chat. Best Regards, Shaiwan Sheikh

@inciterzt

Hi, Your requirement for a fully offline, low-latency speech-to-speech translator aligns directly with the edge-AI systems I’ve built. I’ve deployed quantized (4/8-bit) SLMs including Llama and Phi variants on mobile using ONNX Runtime and TensorFlow Lite, optimized with NNAPI (Android) and Core ML / Metal (iOS). I’ve also implemented offline Whisper (Tiny/Base) pipelines and integrated native TTS (AVFoundation / Android TTS) with hardware acceleration targeting Snapdragon and Apple A-series NPUs. Recent work includes: • On-device LLM assistant (<600ms response, 3B quantized) • Offline voice interface using Whisper + local LLM routing • GPU/NPU optimized inference with thermal profiling & memory tuning I prioritize native Swift/Kotlin builds for maximum performance and tight latency control. A few clarifications: Target language pairs at launch? Real-time streaming translation or sentence-based batching? Will model swapping/downloading be supported? Any encryption requirements for on-device data? Let’s build this fast, efficient, and production-ready.

@kishanbwts

Building an offline, high-speed speech-to-speech translator with tight size and latency limits takes careful balancing of model size, quantization, and hardware acceleration. I recently helped a client deliver a similar app that used quantized Llama 2 SLMs and Whisper Tiny to translate voice commands without internet, optimizing for Snapdragon NPUs. To hit under 500ms latency, I would focus on efficient threading and batching with TensorFlow Lite or ONNX Runtime, testing performance early on Apple A14 and Snapdragon platforms. For audio output, integrating system TTS for each OS avoids adding heavy TTS models and keeps the app size down. Speaking of size, pruning models or swapping in smaller Phi variants might be required to stay under 2GB total. Do you have experience with these quantized Gemma 2 or Phi-3.5 Mini models, or should we budget time for model tuning? Also, should the app support language detection on-device or assume user selection upfront? I am ready to start optimizing the pipeline and testing the core stack on target devices immediately to deliver a stable, low-latency offline translation experience.

@murtuza90

As an adept and seasoned tech architect with over a decade of focused experience in full-stack development and a title-winning contender, I humbly put forth my candidacy for the Smart-Translate AI (On-Device Edition) project. I have a deep well of understanding about scalable architecture designs, which will be crucial in ensuring your app's peak performance, efficacious interfacing of myriad technologies, and the hardcore delivery of results that are robust, reliable, efficient, secure, and maintainable. This rare mix will ensure our immediate success. My proficiency in Android & Swift aligns perfectly with your project requirements. Having successfully provided similar solutions to multiple clients, I am comfortable in navigating this demand and delivering exceeding expectations. The challenge here is more than well met by me; it’s within my realm of proven expertise - specifically in building smart applications that also prioritize energy efficiency and low battery consumption – ultimately enhancing the user experience. I can guarantee an end-to-end text translation latency under 500ms as per your requirement, too.