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 → PyTorch
nn.Module
→
coremltools
(
mlprogram
, FP32, iOS18+).
Graphs & parity (FP32, vs ONNX Runtime)
Module
mlpackage
parity max|Δ|
Duration predictor
DurationPredictor.mlpackage
7.2e-06 ✓
Vector estimator (ODE denoiser)
VectorEstimator.mlpackage
2.5e-03 ✓
Vocoder
Vocoder.mlpackage
3.0e-04 ✓
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 — pad/segment text to
128); vocoder + vector-estimator use a dynamic latent-length
RangeDim
. The host runs the flow-matching
ODE loop (
vector_estimator
×
total_steps
) — 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
— vocoder + duration FP16,
text-encoder + vector-estimator FP32; measured transparent at
47–51 dB
mag-STFT SNR.
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aufklarer Supertonic-3-CoreML online free url in huggingface.co:
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Supertonic-3-CoreML install url in huggingface.co: