Instructions to use ddalcu/Kokoro-82M-MLX-Serve with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ddalcu/Kokoro-82M-MLX-Serve with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Kokoro-82M-MLX-Serve ddalcu/Kokoro-82M-MLX-Serve
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Kokoro-82M for mlx-serve
Kokoro-82M repacked for mlx-serve's native Zig + MLX engine.
Same weights, same precision โ f32, not quantized. The repack folds
weight-norm into .weight (so the engine has no weight_g/weight_v to
resolve), drops the unused pooler tensors, converts .pth to safetensors, and
bundles the 54 voice packs plus the English pronunciation dictionaries the
phonemizer needs.
| File | Contents |
|---|---|
model.safetensors |
457 tensors, 81.1M params, 325 MB f32 |
voices.safetensors |
54 voice packs, [510, 1, 256] each |
g2p/*.json |
misaki us_gold / us_silver / gb_gold |
Verified against the torch reference: per-phoneme durations match exactly, F0 / noise / text-encoder outputs at cosine 1.000000, and the waveform at 0.9968 โ which is inside the reference's own seed-to-seed spread (0.9941โ0.9960), since its vocoder is stochastic.
About 17x realtime on an M-series Mac, ~350 MB resident.
Run it
Download MLX Core.app, open Settings โธ Voice, and pick Kokoro as the voice engine. 54 voices, and naming several separated by commas blends them into a new one.
Over HTTP:
curl -X POST http://localhost:11234/v1/audio/speech \
-H 'content-type: application/json' \
-d '{"model":"kokoro","input":"Hello there.","voice":"af_bella,af_sky"}' \
--output out.wav
Credit
Kokoro-82M and the misaki G2P dictionaries are both by hexgrad, Apache-2.0. This repo only changes the packaging. No espeak-ng anywhere in the pipeline โ the dictionaries make it unnecessary, which keeps the whole path Apache-2.0.
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Quantized