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.