Adds the Newton-sentence benchmark numbers measured on two real Macs +
the upstream CoreML and ONNX baselines. Highlights:
- Mac Studio M3 Ultra: 45.8 ms wall median (best 39 ms), RTF x88
- MacBook Air M4: 86.7 ms wall median, RTF x47
- M4 + CoreML: 303.5 ms wall median, RTF x27
- M4 + ONNX SDK: ~1200 ms wall median, RTF ~x3
Same FR utterance, same warmup protocol, 5 warm runs each. The
ms-per-second-of-audio column is the honest backend comparison since the
two paths produce slightly different audio durations (DurationPredictor
+ CoreML's speed=1.05 give different timing). MLX wins 1.78× over the
CoreML build on identical M4 hardware, and ~35-40× over the upstream
ONNX SDK.
GPU memory footprint on the Ultra: 750 MB active, 844 MB peak.
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>
