Downloads · 30 days
420
30% of all-time downloads
soniqo/Supertonic-3-LiteRT
Supertonic-3-LiteRT is a text-to-speech model from soniqo. 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…
Downloads · 30 days
420
30% of all-time downloads
All-time downloads
1.4K
Public
Repo size
754 MB
Likes
3
Public
Click a slice to open those files.
.tflite754 MB · 100%
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) ⚠️ |
vector_estimator_L128.tflite (L=128 bucket) | 244 MB | 1.7e-02 (relative 2.7e-3) ✓ |
vocoder_L128.tflite (L=128 bucket) | 97 MB | 2.7e-04 ✓ |
Shapes are fixed (T=128 text tokens; latent length L per graph) — litert_torch cannot lower
the relpos attention + ConvNeXt stack with a symbolic L, so the latent axis ships in buckets:
the base pair is L=64 (≈4.5 s of audio), and *_L128.tflite is the same two L-dependent graphs
exported at L=128 (≈9 s). Pick the smallest bucket whose window holds a piece's predicted
duration — short sentences run on the cheap L=64 pair, a long sentence is generated in one pass
on L=128 instead of being split (speech-core's LiteRTSupertonicTts does this automatically and
loads the L=128 pair on first use). 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.
Graph inputs are bound by tensor name (serving_default_args_N, N = signature position): the
converter permutes input slots differently per toolchain version, so do not rely on slot order.
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.