This repository contains Core AI .aimodel assets converted for an arm64
iOS application running on physical iPhone hardware. It is the model-asset
companion to the TTSCoreAILab iOS app.
Deployment target: iOS 27.0+ on arm64 iPhone hardware.
Benchmark device: iPhone 17 Pro. This identifies the device used for the
current RTF benchmark; it is not a guarantee that every included profile has
passed end-to-end validation on every iPhone.
Four Core AI .aimodel stages per fixed-shape profile
On-device iOS TTS research and profiling
Kokoro subgraphs are exported through torch.export and converted with
coreai_torch. Supertonic ONNX graphs are recovered at fixed shapes, converted
through onnx2torch and torch.export, then converted with coreai_torch.
The resulting assets use the versioned AICode layout required by the iOS 27
device compiler.
These are deployment assets, not general-purpose PyTorch, ONNX, Core ML, web,
Android, macOS, or iOS Simulator models. Use them only with the matching
TTSCoreAILab app and its Core AI runtime integration.
Device and profile notes
The app targets arm64 iPhone hardware with deployment target iOS 27.0.
The iPhone 17 Pro is the current benchmark device.
Kokoro packages include 64x64, 128x256, and 256x512 routed profiles,
plus legacy/reference profiles.
Supertonic packages include 64x32, 128x64, 128x128, and 128x256
fixed-shape profiles.
Asset presence does not imply physical-device validation. Validate the exact
app revision, profile, prompt, and device before shipping or profiling.
App integration
The iOS project downloads the two directories below into its repository root:
TTSCoreAILab/Models
TTSCoreAILab/SupertonicResources
From the app repository, run:
./scripts/download-assets.sh
Project overview
Local iOS workbench for incrementally porting and profiling the split Kokoro
pipeline in Swift.
The app integrates the official KittenTTS Swift package and Supertonic 3
ONNX pipeline, and provides four modes:
Kokoro: the existing 11-stage split Core AI pipeline.
Supertonic 3: four Core AI models, ten voices, configurable flow steps,
44.1 kHz.
Compare: sequentially generates all three models for the same text, preserves
PCM results for independent playback, and displays wall time, inference
time, RTF, Core AI coverage, and speedup.
KittenTTS downloads its Nano model and phonemizer assets on first use. Kokoro
and Supertonic 3 use Core AI model assets hosted separately on Hugging Face:
https://huggingface.co/augustZheng/TTS-Core-AI.
Model Assets
The Xcode project expects these asset directories:
TTSCoreAILab/Models
TTSCoreAILab/SupertonicResources
They are not committed to GitHub because the full asset bundle is about 4.2 GB
and contains files larger than GitHub's regular 100 MB file limit.
Download them from Hugging Face before building:
./scripts/download-assets.sh
The script uses uv to run the Hugging Face CLI without creating a project
virtual environment.
The current stage runs a fixed Hi. text-to-acoustic-feature chain:
text
1fixed token ids + attention mask
2-> ALBERT
3-> BERT projection
4-> fixed-length duration encoder
5-> duration head
6-> Swift duration/alignment calculation
7-> text encoder CNN + LSTM
8-> Swift ASR alignment
9-> F0/noise shared LSTM + residual blocks
fixed Hi. decoder style and harmonic-source fixtures
Swift PCM-to-WAV playback through AVAudioPlayer
The current UI mirrors the browser Local Lab:
editable text and af_heart voice selection;
selectable bundled Core AI budgets (64 / 64, 128 / 256, and 256 / 512), with automatic profile escalation for longer segments;
optional automatic segmentation;
total time, Core AI inference time, audio duration, and RTF;
per-stage Core AI timing bars;
generated audio playback controls and a detailed run log.
The PyTorch reference and native Kokoro comparison controls are visible but
disabled because those runtimes are not bundled in the iOS app.
The bundled profiles increase the text/frame budget from 64 / 64 to
128 / 256 and 256 / 512; automatic mode chooses the smallest profile that
fits a segment. Supertonic ships its validated 64 / 32 baseline assets under
SupertonicResources/coreai-assets-baseline.
Tap Generate Locally. A successful run should play generated audio and report:
generator input: [1, 512, 128]
spec/phase: [1, 22, 7681]
audio: [1, 1, 38400]
audio mean absolute value: approximately 0.01756
audio min/max: approximately -0.2076 / 0.3555
RTF Result
The current benchmark on iPhone 17 Pro.
RTF (Real-Time Factor) measures how long it takes to generate one second of audio.
RTF < 1.0 → Faster than real time (suitable for interactive TTS)
Lower is better
The benchmark below compares the same prompt on the same device across different runtimes.
