traxlate-supertonic-3-q8ve

traxlate-supertonic-3-q8ve

ONNX export of Supertonic-3 for on-device, browser-wasm text-to-speech. One 198 MB pack serves every supported language.

Used by Traxlate for offline dubbing and read-aloud. Weights and architecture are Supertone's; this repo is a quantisation + packaging of their release.

⚠ q8ve means "quantise the vector estimator, NOT the vocoder"

This is the whole point of the export, and it is easy to get wrong in a way that every cheap check misses.

Sample counts were byte-identical (1,422,336) across all three arms and every graph loaded in all three. Output length, file size and a successful InferenceSession.create tell you nothing about whether a TTS export still speaks. Only a round-trip (synthesise → ASR → compare) separates them.

Contents

onnx/tts.json                    8 KB     config (sample rate, latent dims, chunking)
onnx/unicode_indexer.json      278 KB     65,536-entry codepoint table (8,321 mapped)
onnx/duration_predictor.onnx   3.7 MB
onnx/text_encoder.onnx        36.4 MB
onnx/vector_estimator.onnx    65.4 MB     int8 — run once per denoising step
onnx/vocoder.onnx            101.4 MB     float — DO NOT QUANTISE
voice_styles/F1.json           292 KB     the single speaker

One speaker, many languages

The pack ships one voice style (F1, extracted from a 44.1 kHz source). Selecting a language does not select a speaker — every language speaks in the same voice.

Language is carried in the text, not by an embedding: tts.json reports n_langs: 0, and the model expects the utterance wrapped as <en>Hello there.</en>. Dropping the tag does not raise an error; it just synthesises with the wrong accent.

Coverage is a property of the codepoint table

unicode_indexer.json maps 8,321 codepoints. Anything outside it becomes -1 — again, silently, with no exception. Bengali (U+0980–U+09FF) has 0 of 128 codepoints mapped, so this pack can never speak it. Persian and Urdu lose ZWNJ, U+0691 and U+06BA.

Check the table before adding a language, and gate on a round-trip measurement per language rather than on one English clip.

Usage (onnxruntime-web)

Four graphs, driven by an iterative denoising loop:

1. duration_predictor → per-token durations (divide by speed; higher = faster speech)
2. text_encoder → text embedding
3. vector_estimator → run once per step, feeding its output back as noisy_latent
4. vocoder → waveform

Four steps is what the WER figure above was measured at. Fewer steps degrades audibly before it saves meaningful time.

WASM only. Supertonic's ConvNeXt depthwise-conv is fragile on GPU execution providers (it breaks ORT-GPU on Pascal and onnxruntime-web WebGPU on some devices). CPU-wasm measured RTF 0.296 — comfortably faster than realtime — so there is nothing to gain and a crash to lose.

Measurements

RTF = wall / audio, lower is faster. Chrome 150, 4 threads, 4 steps, cross-origin isolated, Ryzen 5 8500G:

Per-language round-trip CER (CER, not WER — word segmentation is meaningless for ja/ko) was measured over 27 languages × 2 clips before any language was shipped.

License

MIT, following the upstream Supertonic release. See supertone-inc/supertonic.