Empirical seed lottery on the (voice × text) matrix showed that some
seeds are unlucky: at seed=42 the worst case was M3 + the long FR
'Supertonic / MLX' utterance at 75 % Whisper word overlap (user
reported audio as 'inaudible' on a second machine). The FP32 noise in
the Euler trajectory is sensitive to the initial draw on long
sequences; some seeds happen to land in a region that confuses the
acoustic model on rare phonemes (Whisper hallucinations on 'MLX' /
'Supertonic' specifically).
Bench across 5 seeds × 6 voices × 4 utterances (debug/seed_sweep
methodology, full results in commit message of the sync):
seed=42 avg ~93 % min 75 % σ ~7 %
seed=99 avg 98 % min 87.5 % σ 3.4 % ← new default
seed=1000 avg 97 % min 81 % σ 5.7 %
seed=7 avg ~95 % min 81 % σ ~5 %
seed=12345 avg 97 % min 81 % σ 5.4 %
Seed=99 dominates on min-overlap (max-min strategy) and has the lowest
variance. Audio samples in samples/*.wav have been regenerated with the
new default.
Users who want to A/B different draws can still pass seed=N explicitly;
the docstring now documents that retrying with another seed is the
right escape hatch if a specific utterance comes out muddled.
The root-cause of the audio gibberish. The ONNX graph has a residual ADD
between attn_encoder output and convnext output before the slot-0
extraction that feeds proj_out:
/sentence_encoder/Add = attn_encoder/Mul_2_output + convnext/convnext.5/Mul_3_output
/sentence_encoder/Slice_1 = Add[:, :, 0:1]
/sentence_encoder/proj_out/Conv = Conv1d(Slice_1, ...)
The MLX port was skipping this residual:
x = self.convnext(x, mask_ntc)
x = self.attn_encoder(x, mask_ntc)
sentence_out = x[:, :1, :] # ← missing + convnext residual
Effect: the sentence vector fed into the predictor MLP was wrong → log
duration was systematically 0.95 nats lower than ONNX → predicted
duration was 35 % of correct length → T_lat 3 × too short → VE had to
compress speech into 1/3 of the proper frames → audio unintelligible.
Fix (one line): explicitly hold both x_conv and x_attn outputs and add
them before the slot-0 slice.
Measured impact on the FR test phrase
'Bonjour, je suis une voix générée par le modèle Supertonic trois en MLX
sur Apple Silicon.' (Whisper-large-v3 word overlap, MLX FP32):
voice before-fix after-fix
F1 25 % 88 %
F2 25 % 88 %
F3 19 % 88 %
F4 0 % 88 %
F5 12 % 81 %
M1 12 % 88 %
M2 56 % 88 %
M3 0 % 75 %
M4 6 % 81 %
M5 0 % 94 %
avg 16 % 86 %
The ONNX SDK reference ceiling on the same phrase is 81-88 %, so MLX is
now AT parity with the upstream ONNX SDK.
Bisection trail: DurationPredictor MLX output was 35 % of ONNX on a
side-by-side check; sentence_encoder per-stage compare showed cosine 1.0
through text_embedder + convnext + attn_encoder, then a drop to 0.149 at
proj_out — caught by tracing the ONNX Slice_1 producer to a missing Add
node. Both the timestep schedule fix (step+1 → step) and the
<lang>-token tokenization fix from the previous commit are still needed;
this third fix closes the gap to ONNX SDK quality.
Repos can be re-published after this commit.
Two coupled bugs producing structureless ('Whisper hallucinates Société
Radio-Canada') audio on the v0.1.0 release.
Fix#1 — Euler timestep schedule (PRIMARY, smoking gun)
ONNX SDK passes current_step = 0..N-1 → t_norm = [0.0, 0.2, 0.4, 0.6, 0.8].
We were passing step + 1 → [0.2, 0.4, 0.6, 0.8, 1.0].
Flow-matching is trained on the SDK schedule; the off-by-one collapses
the trajectory to noise (ONNX-only ablation: wav cosine 0.0037 vs ref).
Fix#2 — text preprocessing (SECONDARY)
Supertonic 3 wraps utterances in <lang>text</lang> via the SDK's
UnicodeProcessor; we were emitting raw character IDs and ignoring lang.
Min-viable port: NFKD normalisation + whitespace collapse + trailing
period + language token wrap. Bit-identical Whisper output vs the full
SDK preprocessor (verified inline).
Measured impact (FR test phrase, Whisper-large-v3):
before: 10/10 voices → 0% word overlap (Whisper hallucinations only)
after: M2 56%, F1/F2 25%, F3 19%, F5/M1 12%, F4/M3/M5 0%, M4 6%
Audio is now structurally voiced French with target words appearing in
the best voices, but still falls short of the ONNX SDK 81-88% ceiling.
Per-step Euler bisect (same conditioning, ONNX vs MLX VE side-by-side)
shows the residual bug is in the VE velocity prediction; cosine drops
1.000 → 0.9995 → 0.965 → 0.889 → 0.673 → 0.453 across steps 0..5,
exponential compounding from ~0.05 % per-step drift. Continues in a
follow-up commit.
Repos remain PRIVATE on HF + GitHub until full fix lands.
T.6 post-publish audit caught two leaks in the published artefacts:
1. `conversion_report.json` (4 hits on both HF and GitHub) exposed
absolute paths from the build machine:
"safetensors": "/Users/transcrilive/MLX_CONVERTOR/sub-projects/supertonic3-mlx/hf_release/weights/X.safetensors"
"onnx": "/tmp/supertonic3/model/onnx/X.onnx"
This revealed the dev Mac's username (transcrilive) + the private
monorepo name (MLX_CONVERTOR) + the internal sub-projects layout.
2. `src/supertonic_3_mlx/pipeline.py` docstring (1 hit) had a
from_pretrained example pointing at /tmp/supertonic3/model.
Fixes:
- conversion_report.json regenerated with basenames only
("vector_estimator.onnx" / "weights/vector_estimator.safetensors")
- pipeline.py docstring example updated to use the canonical Hub repo id
- the upstream converter tool (in the dev monorepo) patched so future
regenerations of the report don't reintroduce the leak
No tokens, credentials, or keys were ever exposed; tokens are kept only
in env vars / keyrings and never enter the published artefacts.
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>
