README.md

metadata

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.

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

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

Use in 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)