SupertonicTTS-3 β€” CoreML (.mlpackage, iOS)

First-party CoreML export of Supertonic-3's four non-autoregressive flow-matching graphs, for on-device iOS / Apple Neural Engine. Built by our own pipeline (speech-models/stmodels): weights lifted from the Supertone/supertonic-3 ONNX initializers β†’ PyTorch nn.Module β†’ coremltools (mlprogram, FP32, iOS18+).

Graphs & parity (FP32, vs ONNX Runtime)

Module mlpackage parity max|Ξ”|
Duration predictor DurationPredictor.mlpackage 7.2e-06 βœ“
Vector estimator (ODE denoiser) VectorEstimator.mlpackage 2.5e-03 βœ“
Vocoder Vocoder.mlpackage 3.0e-04 βœ“
Text encoder TextEncoder.mlpackage mean 2.5e-04 (max 2.5e-2 at isolated positions)

Text/duration use fixed T=128 (relpos attention has T-dependent pad widths β€” pad/segment text to 128); vocoder + vector-estimator use a dynamic latent-length RangeDim. The host runs the flow-matching ODE loop (vector_estimator Γ—total_steps) β€” the graphs contain no control flow. Assets to drive them: tts.json, unicode_indexer.json (G2P-free tokenizer table), voice_styles/*.json.

FP32 = parity reference. For ANE residency, use the mixed-precision Supertonic-3-CoreML-FP16 β€” vocoder + duration FP16, text-encoder + vector-estimator FP32; measured transparent at 47–51 dB mag-STFT SNR.

Attribution & license

  • Weights: derivative of Supertone/supertonic-3 (commit 3cadd1ee6394adea1bd021217a0e650ede09a323), Supertone Inc., arXiv:2503.23108 β€” OpenRAIL-M (use-based restrictions carry over: no non-consensual impersonation/deepfakes, etc.).

Other Supertonic-3 formats

Ecosystem

  • soniqo.audio β€” website / use-case explorer (transcription, voice cloning, live ASR, voice agents).
  • speech-core β€” C++ orchestration library; Supertonic plugs in as a TTSInterface CoreML model.
  • speech-swift β€” Apple Silicon MLX + CoreML runtime.
  • speech-android β€” Android SDK consuming on-device LiteRT bundles.

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