Running Whisper, Qwen3-ASR, Nemotron & MOSS completely offline on iPhone [P]

Over the past month, I've been building LiveTranscriber, an open-source iOS app for running modern speech and language models entirely on-device. The goal was to see whether recent open-source models could be turned into a practical mobile product—not just technical demos. Currently supported local models include: - Whisper for offline transcription - Qwen3-ASR for multilingual speech recognition - NVIDIA Nemotron Streaming for low-latency live transcription - MOSS Multi-Speaker for speaker-aware transcription - Qwen3 for local summaries, key points, titles, and transcript analysis Features include: - 100% offline speech recognition - Offline multi-speaker transcription - On-device summaries and key-point extraction - Real-time translation - Apple Watch recording with automatic sync - Downloadable and switchable local models - Searchable transcript history The main engineering challenge was not simply running the models, but making them usable on iPhone: memory management, streaming latency, model loading, context handling, battery usage, and switching between different inference backends. The project is fully open source: GitHub: https://github.com/iamwilliamli/LiveTranscriber App Store: https://apps.apple.com/us/app/live-transcriber-recorder/id6785515364 I'd appreciate feedback from anyone working on ASR, local LLMs, on-device AI, Core ML, or mobile inference. submitted by /u/marshmallow_ki [link] [留言]