Zyphra's ZONOS3-Mini Clones Voices in 43 Languages Directly on Your Phone
Zyphra released ZONOS3-Mini , an Apache 2.0 TTS with 100M active and 800M total MoE parameters. Runs in real time on-device: under 800 ms time-to-first-audio on a Pixel 9, under 450 ms in M3 MacBook browser. Supports 43 languages with zero-shot voice cloning from a 5-10 second reference clip.
Takeaways
- Zyphra released ZONOS3-Mini , an Apache 2.0 TTS with 100M active and 800M total MoE parameters.
- Runs in real time on-device: under 800 ms time-to-first-audio on a Pixel 9, under 450 ms in M3 MacBook browser.
- Supports 43 languages with zero-shot voice cloning from a 5-10 second reference clip.
- Word-level control over emotion, pace, projection, whispering, and delivery for games and narration.
- New ZAC-1 codec drops speech tokens from 774 to 150 per second; trained on 112 H100s in 2.5 days.
- Open runtimes for WebGPU, CUDA, MLX, and ggml; SGLang-Omni serves an OpenAI-compatible endpoint.
Zyphra has released ZONOS3-Mini , a text-to-speech model that clones voices from short recordings, supports 43 languages, and applies emotion and delivery controls to individual text spans. Its weights use the Apache 2.0 license, its runtimes are open source, and local inference works on phones, laptops, and browser-based applications.
Local execution removes the network latency, usage fees, and audio transfer associated with a hosted speech API. Zyphra reports time to first audio below 800 milliseconds on a Pixel 9 and below 450 milliseconds in a browser on an M3 MacBook Air, with both devices generating audio at least as fast as playback.
How 800 million parameters fit local inference
ZONOS3-Mini is an 11-layer language model built from Gated DeltaNet sequence mixers and two interleaved global-attention layers. Gated DeltaNet handles long sequences with lower computational cost than full attention, while the global layers can draw information from the entire sequence. Full AttnRes connections let each layer combine outputs from earlier layers instead of relying only on the immediately preceding state.
The latent mixture-of-experts backbone contains 64 experts and uses top-2 routing from the first layer onward. Each token activates about 100 million of the model’s 800 million parameters, reducing the arithmetic required for generation. All expert weights still need to remain available through memory, storage, quantization, or runtime offloading, so active parameter count does not represent the complete deployment footprint.
The model tokenizes text with SuperBPE and predicts ten streams of 4,096-way audio codes at 15 frames per second. A causal, multi-resolution inverse short-time Fourier transform decoder reconstructs those codes incrementally as 44.1 kHz audio.
Zyphra also replaced the Descript Audio Codec with Zyphra Audio Codec 1, or ZAC-1. The new codec reduces the representation from 774 to 150 speech tokens per second, an approximately fivefold reduction in the sequence the language model must process. Combined with sparse expert routing, the shorter sequence makes interactive local generation practical.
Reporting is indexed from AlphaSignal. Rights remain with the original publisher and cited sources.
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