VoiceSynth Neural Engine (Mobile / Edge ONNX)
High-performance, 100% offline neural voice cloning and text-to-speech engine optimized specifically for on-device mobile inference on modern Android flagships (Qualcomm Snapdragon 8 Elite / 8 Gen 3 and ARM64 NEON).
Published by latentdivergence.
Model Components
| File | Purpose | Footprint | Target Backend |
|---|---|---|---|
tone_extract.onnx |
Extracts 256-d speaker timbre embedding from 3–5s audio | 3.21 MB | Qualcomm NNAPI / NPU (13 ms) |
tone_color.onnx |
Normalizing-flow timbre adaptation network | 149.91 MB | Qualcomm NNAPI / NPU |
tone_config.json |
Periodic Hann STFT audio DSP configuration (22,050 Hz) | 838 B | Engine Configuration |
en_US-lessac-medium.onnx |
VITS neural acoustic base speech synthesizer | 60.27 MB | Multi-threaded CPU XNNPACK |
en_US-lessac-medium.onnx.json |
Phoneme dictionary, IPA mapping, length scales | 4.88 KB | Engine Configuration |
model_manifest.json |
Production delivery manifest with SHA-256 signatures | 1.8 KB | In-App Downloader & Verifier |
On-Device Physical Benchmarks
Measured on physical hardware running Android 16 (API 36):
1. OnePlus 13 (Snapdragon 8 Elite / SM8750 "sun")
- Speaker Embedding Extraction: 13 ms
- Model Cold Start: 1,172 ms
- Model Warm Start: 633 ms
- Real-Time Factor (RTF): 0.550x (1.8x faster than real-time)
- Active Synthesis RAM: 91.7 MB
- Thermal Status: Nominal (Cool)
2. OnePlus 12 (Snapdragon 8 Gen 3 / SM8650 "pineapple")
- Speaker Embedding Extraction: 25 ms
- Model Cold Start: 1,549 ms
- Model Warm Start: 588 ms
- Real-Time Factor (RTF): 0.499x (2.01x faster than real-time)
- Active Synthesis RAM: 71.1 MB
- Thermal Status: Nominal (Cool)
In-App Model Distribution
Production Android applications can keep their base APK lightweight (~15 MB) by fetching this model bundle on first launch using the included model_manifest.json:
https://huggingface.co/latentdivergence/voicesynth-neural-engine/resolve/main/model_manifest.json
All models are served via high-speed global CDN with SHA-256 cryptographic verification.
License
This model suite is licensed under the MIT License. Commercially unencumbered for embedded, on-device, and edge applications.