Model card: use upstream's 'voice cloning' wording

README.md

@@ -24,7 +24,7 @@ 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 zero-shot voice cloning from a short reference clip.
+- 🧠 **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**.
 
@@ -77,7 +77,7 @@ Python API, the enroll-once flow, and the full parity report.
 
 ## Responsible use
 
-This performs **zero-shot voice cloning** — it can reproduce a person's voice from a few seconds
+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.