For researchers
VoiceForever runs on a single iPad, with no server, a speech synthesis that takes on a person’s own voice, and gaze tracking through the front camera. Here is how it works, and how to measure it.


A fully on-device architecture
No account, no remote API, no analytics. Inference runs on the iPad’s GPU with MLX, from a Swift app.
On the iPad
-
Gaze
- Front camera
- ARKit face tracking
- VoiceForever gaze engine: calibration, target choice
- Phrase or key selected by dwell
-
Voice
- Typed text
- Qwen3-TTS on MLX, conditioned on a reference clip and its transcript
- Audio played while it is generated
-
Transcription
- Microphone
- Parakeet (NVIDIA) on MLX
- Transcript checked by the person
-
Suggestions
- Sentence in progress
- The person’s own vocabulary, the iPadOS spell checker, Apple’s on-device model when the iPad supports it
- Suggested words and phrases
What comes in, once: the models, downloaded from Hugging Face at a pinned revision.
What goes out, only if the person decides: a technical report, a profile export, a gaze trace.
The models
Qwen3-TTS for the voice (Apache-2.0 license) and NVIDIA Parakeet for transcription (CC BY 4.0 license), both converted for MLX. Each one is pinned to a revision: the app downloads exactly these files. The light voice is the default; the high-quality voice is available as an option for iPads with enough memory.
| Model | Role | Size | Repository and revision |
|---|---|---|---|
| Qwen3-TTS 0.6B Base 8-bit | Voice | 2.0 GB | voiceforever/Qwen3-TTS-12Hz-0.6B-Base-8bit @75836808327c |
| Qwen3-TTS 1.7B Base 8-bit | Voice | 3.1 GB | voiceforever/Qwen3-TTS-12Hz-1.7B-Base-8bit @878f2657bbde |
| Qwen3-TTS 1.7B Base BF16 | Voice | 4.5 GB | voiceforever/Qwen3-TTS-12Hz-1.7B-Base-bf16 @3d06c59fd5fc |
| Parakeet TDT 0.6B v3 8-bit | Transcription | 0.9 GB | voiceforever/parakeet-tdt-0.6b-v3-mlx-8bit @c72211d978ee |
Measurements
The app shows its own measurements in Advanced mode, which you turn on in Settings, About. They are explained there in plain language, with their units.
Gaze
- Median error and 90th percentile, in screen points
- Accuracy exercise: 40 targets, then a rest period
- Calibration history
- Export of gaze traces (JSONL) and of the accuracy report (JSON)
Speech synthesis
- Time to first sound
- Real-time factor
- Memory used
- For each recent phrase
- Gaze accuracy on iPad On request
- Protocol, calibration sessions recorded on the device, and traces replayed on a test bench to check every change to the engine.
- Speech synthesis performance on iPad In progress
- Time to first sound, real-time factor and memory, for both voices, several reference lengths and several sentence lengths, with a blind listening test.
We only publish a figure together with the report that establishes it. VoiceForever has not been the subject of any clinical study: these measurements describe how the app behaves technically on a device, not any effect on health.
Get in touch
Evaluating the app, planning a study, or want to compare it with your own system? Ask us for the reports or for a conversation.