The 10 preset voices live on a hypersphere of radius ≈ 7.1 in the
12 800-D style-token space (verified empirically: pairwise cosines
0.86-0.97, SVD shows 7 axes cover 99 % of variance). Linear or
spherical interpolation between presets stays in the trained
distribution and produces new intelligible voices.
API:
voice = pipe.create_voice({'F2': 0.7, 'M1': 0.3}) # slerp by default
voice = pipe.create_voice({'F2': 0.5, 'M1': 0.5}, interp='lerp')
wav = pipe.generate('Bonjour', voice=voice, lang='fr')
The voice argument of pipe.generate() now accepts either a preset
name (str) or a custom voice descriptor (dict from create_voice).
Whisper validation on 6 custom blends (FR test phrase):
F2 70 / M1 30 → 100 % (lightly androgyne F voice)
F2 50 / M1 50 → 91 % (true androgyne)
avg of 5 F voices → 100 % (mean feminine timbre)
avg of 5 M voices → 91 % (mean masculine timbre)
warm fem (F4+F5) → 91 %
bright masc (M1+M5) → 100 %
All blends remain intelligible — the trained voice manifold is convex
enough that interpolations don't fall out of the model's distribution.
Example script in examples/custom_voice_demo.py.
Splits the input text at sentence-ending punctuation (with secondary
split on , ; : for sentences over 220 chars), yields one wav chunk
per clause. Callers can start playback as soon as chunk 0 arrives —
TTFB ~ 50 ms on M4 — while the rest synthesise in the background.
API:
for idx, wav in pipe.generate_stream('Phrase 1. Phrase 2.', voice='F1', lang='fr'):
play_audio(wav)
For non-streaming consumers:
chunks = [w for _, w in pipe.generate_stream(text, ...)]
full = pipe.concat_chunks(chunks, gap_ms=80)
Bench on a 23 s French paragraph (M3 Ultra):
chunks: 6
TTFB: 54 ms (first 2.44 s audio chunk ready)
total: 410 ms (RTF x56)
Whisper: 98 % word overlap on concat
The 80 ms inter-chunk silence in concat_chunks roughly matches the
natural breathing pause between sentences and masks the prosody
discontinuity from independent chunk generation. Each chunk uses
seed + idx so chunks don't sound identical even on repeated nouns.
Example script in examples/streaming_demo.py.
MLX-native port of Supertone's Supertonic 3 multilingual TTS. Runs the
full flow-matching + classifier-free-guidance pipeline at ~x100 realtime
on Apple Silicon, with audio cosine 1.0 vs the cached MLX path and
cosine 0.98 vs the upstream ONNX Runtime reference.
Weights are hosted at https://huggingface.co/ambassadia/supertonic-3-mlx
and auto-downloaded on first use; this repository ships the port code,
the model card, audio samples, and a zero-config setup_and_test.sh.
Install:
pip install git+https://gitea.tavportal.com/olivier/supertonic-3-mlx.git
Quick test:
git clone https://gitea.tavportal.com/olivier/supertonic-3-mlx.git
cd supertonic-3-mlx && ./setup_and_test.sh
Licenses (dual): model weights = BigScience Open RAIL-M (Section 4
propagation), port code = Apache-2.0. See LICENSE, LICENSE-CODE, NOTICE.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
