---
license: apache-2.0
library_name: coreml
pipeline_tag: text-to-speech
base_model: hexgrad/Kokoro-82M
base_model_relation: quantized
tags:
  - coreml
  - core-ml
  - ios
  - macos
  - apple
  - on-device
  - tts
  - styletts2
  - istftnet
  - arxiv:2306.07691
---

# Kokoro-82M — Core ML

*Multilingual TTS*

English + Japanese text-to-speech. 24 kHz. StyleTTS2 + iSTFTNet vocoder. Multiple voices.

<p><img src="https://huggingface.co/mlboydaisuke/Kokoro-82M-CoreML/resolve/main/media/2ff88dfdc5.gif" alt="Kokoro-82M demo"></p>

Core ML conversion of [hexgrad/Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) for on-device inference on iPhone, iPad and Mac. Converted with `coremltools`; the packages are stateless, so all sequencing and buffering lives in your Swift code.

| | |
|---|---|
| Task | text to speech |
| Upstream | [hexgrad/Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) |
| Packages | 4 |
| Download size | 724 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~1000 MB |

## Files

| File | Size | Compute units | SHA-256 |
|---|---:|---|---|
| `Kokoro_Predictor.mlpackage.zip` | 69 MB | `cpuAndGPU` | `af1d55dc842980c3…` |
| `Kokoro_Decoder_128.mlpackage.zip` | 219 MB | `cpuAndGPU` | `cece0d072f5ba6aa…` |
| `Kokoro_Decoder_256.mlpackage.zip` | 219 MB | `cpuAndGPU` | `36d5e16d5c5ccb50…` |
| `Kokoro_Decoder_512.mlpackage.zip` | 219 MB | `cpuAndGPU` | `0a44484c327e4fe8…` |
| `kokoro_vocab.json` | 1 KB | `-` | `70abefbe8a1c8865…` |
| **Total** | **724 MB** | | |

`compute_units` is not a suggestion -- it is the configuration the conversion was verified against. Moving a package to a different compute unit can silently change the numerics (FP16 attention overflow) or crash on the GPU.

## Download

```bash
hf download mlboydaisuke/coreml-zoo --include "kokoro/*" --local-dir ./kokoro
unzip './kokoro/kokoro/*.zip' -d ./kokoro
```

## Use in Swift

```swift
import CoreML

let config = MLModelConfiguration()
config.computeUnits = .cpuAndGPU   // as converted — see the table above

// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
// at build time:
let model = try Kokoro_Predictor(configuration: config)

// ...or compile a downloaded .mlpackage at runtime:
let compiled = try await MLModel.compileModel(at: mlpackageURL)
let model = try MLModel(contentsOf: compiled, configuration: config)
```

> This model is split into 4 Core ML packages that are driven in sequence from Swift. Load them one at a time, copy the outputs out of the `MLMultiArray` buffers and release each model before loading the next — two large Core ML models resident at once will OOM on an iPhone.

## Demo

- **Sample app** — [`sample_apps/KokoroDemo`](https://github.com/john-rocky/CoreML-Models/tree/master/sample_apps/KokoroDemo), a standalone SwiftUI project.
- **Models Zoo** — this model is downloadable and runnable inside the [Models Zoo app](https://apps.apple.com/app/id6762083207) on the App Store, no build required.

## Conversion

- Script: [`convert_kokoro.py`](https://github.com/john-rocky/CoreML-Models/blob/master/conversion_scripts/convert_kokoro.py)
- Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling): [`docs/coreml_conversion_notes.md`](https://github.com/john-rocky/CoreML-Models/blob/master/docs/coreml_conversion_notes.md)
- Model index: [CoreML-Models](https://github.com/john-rocky/CoreML-Models)

## License

The conversion inherits the upstream license: **Apache-2.0**.

## Credits

- Upstream authors: [hexgrad/Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M), 2024
- Core ML conversion: john-rocky (Daisuke Majima)