---
license: apache-2.0
library_name: mlx
pipeline_tag: text-to-speech
base_model: drbaph/ZONOS2-BF16
tags:
  - mlx
  - apple-silicon
  - text-to-speech
  - tts
  - voice-cloning
  - zonos
  - zonos2
  - moe
language:
  - en
---

# zonos2-mlx — ready-to-run MLX weights

Pre-converted, pre-quantized [MLX](https://github.com/ml-explore/mlx) weights for Zyphra's
[**ZONOS2**](https://github.com/Zyphra/ZONOS2) — an **8B-parameter Mixture-of-Experts**
autoregressive text-to-speech model — running natively on Apple Silicon.

**Download and run. No PyTorch in the inference path, no conversion step.**

- 🧠 **Model:** 16-expert top-1 MoE AR trunk (layer 26 routes top-2) → DAC 44.1 kHz neural codec for the waveform, with an ECAPA-TDNN speaker encoder (+ LDA) for voice cloning from a short reference clip.
- 🍎 **Runtime:** [`sb1992/mlx-zonos2`](https://github.com/sb1992/mlx-zonos2) — a clean-room MLX reimplementation of the inference runtime, gated per-stage against the original PyTorch model.
- 📦 **This repo:** the weights only. Three precision tiers, each a **self-contained folder**.

## Tiers

Each folder (`bf16/`, `int8/`, `int4/`) is **self-contained** — it bundles the quantized trunk
plus the (tier-independent) DAC codec and ECAPA speaker encoder, so you download **one folder**
and it just runs.

| Folder | what's quantized | folder size | peak RAM | target Macs |
|---|---|---|---|---|
| `bf16/` | nothing (reference) | ~14 GB | ~44 GB | 64 GB |
| `int8/` | attention/FFN/lm_head + experts int8; router/embeddings/norms bf16 | ~7.9 GB | ~13 GB | 32 GB |
| `int4/` | attention/FFN/lm_head int8; experts gate/up int4, down int8; router/embeddings/norms bf16 | ~5.7 GB | ~10.6 GB | 16 GB |

<sub>Folder size includes the bundled ~315 MB DAC codec + ECAPA speaker encoder (identical across
tiers — Hugging Face Xet de-dups them, so they cost storage only once).</sub>

The MoE experts (the bulk of the 8B) carry the int4; the **router/gate**, the **`lm_head`**, and
the sensitive expert **`down`** projection stay int8/bf16 — the MoE-quant recipe that keeps the
model intact. All three tiers produce **full, intelligible audio** — they're equal options, pick
by the RAM you have.

## Quick start

```bash
# 1. get the runtime
git clone https://github.com/sb1992/mlx-zonos2.git
cd mlx-zonos2
uv sync --extra oracle        # `oracle` extra = torchaudio, for enrolling a voice from raw audio

# 2. download one tier (self-contained: trunk + DAC + speaker encoder)
hf download shraey/zonos2-mlx --include "int8/*" --local-dir ./zonos2-mlx-weights

# 3. clone a voice + synthesize
python scripts/zonos2_cli.py \
    --model-dir ./zonos2-mlx-weights/int8 \
    --text "The quick brown fox jumps over the lazy dog." \
    --ref ref.wav \
    --out out.wav
```

Swap `int8` → `int4` (16 GB Macs) or `bf16` (64 GB Macs) — same flow, just point `--model-dir`
at the folder you downloaded. To grab every tier at once, drop the `--include` filter.

`--ref` enrolls a reference clip on the fly (needs the `oracle` extra for the mel front-end). You
can also enroll a voice **once** into a small `.zonos` profile and reuse it — then generation is
pure-MLX with no torch. See the [runtime repo](https://github.com/sb1992/mlx-zonos2) for the
Python API, the enroll-once flow, and the full parity report.

## Responsible use

This performs **voice cloning** — it can reproduce a person's voice from a few seconds
of audio. Use it responsibly: no impersonation, fraud, or disinformation; only clone voices you
own or have explicit consent for; disclose AI-generated audio wherever it's published. See the
[runtime repo](https://github.com/sb1992/mlx-zonos2) for the full policy.

## Attribution + license

This is a derivative port. The components it builds on are each independently licensed:

- **ZONOS2** — **Apache-2.0**, © [Zyphra](https://www.zyphra.com/). The 8B-MoE model, the DAC
  44.1 kHz codec, and the speaker encoder are Zyphra's. [Code](https://github.com/Zyphra/ZONOS2)
- **Released checkpoint** — this port converts the [`drbaph/ZONOS2-BF16`](https://huggingface.co/drbaph/ZONOS2-BF16)
  release (its speaker encoder is an ECAPA-TDNN, 2048-d).
- **Porting oracle** — the clean plain-torch [Zonos2_TTS-ComfyUI](https://github.com/Saganaki22/Zonos2_TTS-ComfyUI)
  fork by Saganaki22 (Apache-2.0), used as the op-for-op reference.
- **MLX** — Apple's [ml-explore/mlx](https://github.com/ml-explore/mlx).

The MLX port code is licensed **Apache-2.0**. You must comply with the upstream ZONOS2 license and
usage terms for the model weights. **Full credit to Zyphra** for the model, its training, and the
open release — this repo only re-expresses their runtime in MLX.