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Vocabook/Supertonic-3-LiteRT
Supertonic-3-LiteRT is a text-to-speech model from Vocabook. Use it when you need text read aloud. It is set up for litert. The card lists the license as openrail.
First-party LiteRT export of Supertonic-3's four non-autoregressive flow-matching graphs. Built by our own pipeline (speech-models/stmodels): weights lifted from the Supertone/supertonic-3 ONNX initializers → PyTorch…
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From the Hugging Face model README
First-party LiteRT export of Supertonic-3's four non-autoregressive flow-matching graphs. Built by
our own pipeline (speech-models/stmodels): weights lifted from the
Supertone/supertonic-3 ONNX initializers → PyTorch
nn.Module → litert_torch.convert (torch.export → StableHLO → TFLite). This avoids the onnx2tf
NCHW/ConvNeXt layout failures that block direct ONNX→TFLite for this model.
| Module | tflite | parity max|Δ| |
|---|---|---|
duration_predictor.tflite | 3.4 MB | 4.1e-05 ✓ |
vector_estimator.tflite (ODE denoiser) | 244 MB | 5.6e-03 ✓ |
vocoder.tflite | 97 MB | 2.6e-04 ✓ |
text_encoder.tflite | 34 MB | 1.1e-01 (localized; mean ~2.5e-4) ⚠️ |
Fixed shapes (T=128, L=64) in this revision — pad/segment text to 128 and bucket latent length;
dynamic axes are a follow-up. The host runs the flow-matching ODE loop (vector_estimator ×total_steps).
Assets to drive them: tts.json, unicode_indexer.json (G2P-free tokenizer), voice_styles/*.json.
ai_edge_litert / TFLite) interpreter with XNNPACK/GPU delegate.qai_hub) from these graphs (static shapes are HTP-friendly). int8/int4 PTQ via
ai-edge-quantizer for full HTP residency is a follow-up.Supertone/supertonic-3
(commit 3cadd1ee6394adea1bd021217a0e650ede09a323), Supertone Inc., arXiv:2503.23108 — OpenRAIL-M
(use-based restrictions carry over).TTSInterface LiteRT model.