Sidon — Core ML (speech restoration / dereverberation)
On-device
speech restoration
(denoise + dereverberation + bandwidth restoration)
for Apple Silicon, exported to Core ML (runs on the Neural Engine). Turns a
noisy/reverberant clip into studio-quality
48 kHz
speech — ideal for cleaning a
voice-cloning reference
before TTS, since it preserves speaker identity.
Total 246 M params (predictor 193.6 M + DAC vocoder 52.4 M). Output sample rate 48 kHz.
int8 keeps the vocoder at FP16 (audio quality); only the predictor is palettized.
Files
path
description
fp16/Sidon-Predictor.mlpackage
w2v-BERT 2.0 (8L) + merged LoRA → features (FP16)
fp16/Sidon-Vocoder.mlpackage
DAC decoder → 48 kHz audio (FP16)
int8/Sidon-Predictor.mlpackage
predictor, 8-bit palettized
int8/Sidon-Vocoder.mlpackage
DAC decoder (FP16)
Quality (no-reference MOS, 10 s clip)
DNSMOS P.835 (SIG/BAK/OVRL, higher = better) and UTMOS (naturalness, 1–5):
audio
SIG
BAK
OVRL
UTMOS
speaker cos
input (reverberant)
3.46
3.40
2.90
2.99
—
fp16
3.53
4.09
3.28
3.32
0.797
int8
3.54
4.11
3.29
3.23
0.796
Restoration lifts OVRL 2.90 → 3.29 (driven by
BAK 3.40 → 4.11
— reverb removed).
Quantization is near-lossless on DNSMOS and speaker similarity; UTMOS shows a small
naturalness cost (fp16 −0.09, int8 −0.17). Numbers are a single clip — average over a
set for a definitive figure.
Front-end
The graphs take
input_features [1, T, 160]
from the
w2v-BERT 2.0 SeamlessM4T
feature extractor
(16 kHz input). The sequence length is fixed (T = 499 ≈ 10 s) —
chunk longer audio in the runtime. The front-end and chunking are handled by
speech-swift
.
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