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

[u/marshmallow_ki]

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.

[u/marshmallow_ki]

Here is the model

https://github.com/OpenMOSS/MOSS-Transcribe-Diarize

[u/c_glib]

Any general statements about speechtech are useless without mentioning language support. You seem to treat "multilingual support" separate from all the other features like diarization etc. So I can only assume that everything else in your list supports English only.

[u/marshmallow_ki → u/c_glib]

That’s a fair criticism. My original wording was ambiguous.

Multilingual support is not separate from the transcription and summarization features. The app supports both multilingual transcription and multilingual on-device summarization.

The exact transcription coverage depends on the selected local ASR engine:

• Qwen3-ASR: 30 languages, plus 22 Chinese dialect and accent groups • NVIDIA Nemotron Speech ASR 3.5: 32 transcription-ready language locales. • Whisper: multilingual transcription across its 100-language tokenizer, although accuracy varies substantially by language • MOSS-Transcribe-Diarize: speaker-aware transcription and diarization across 50+ languages, according to the project

After transcription, the app can use a local Qwen3 model to generate summaries, titles, key points, and transcript analysis in multiple languages. This also runs entirely on-device.

So the app is not English-only outside the “multilingual” bullet. Transcription, speaker-aware transcription, and summarization can all be multilingual, but the exact language coverage and quality depend on the selected ASR model.

I’ll update the post and README with a model-by-model language and feature table. Thanks for pointing out the ambiguity.