Core ML conversion of 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
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, a standalone SwiftUI project.
Models Zoo — this model is downloadable and runnable inside the Models Zoo app on the App Store, no build required.

Conversion

Script: convert_kokoro.py
Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling): docs/coreml_conversion_notes.md
Model index: CoreML-Models

License

The conversion inherits the upstream license: Apache-2.0.

Credits

Upstream authors: hexgrad/Kokoro-82M, 2024
Core ML conversion: john-rocky (Daisuke Majima)

More models in this format: Core ML Model Zoo — 46 models, each with the recipe that produced it.

Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.

mlboydaisuke/Kokoro-82M-CoreML

Kokoro-82M — Core ML