OpenVoice V2 — Core ML

Voice Cloning

Zero-shot voice conversion. Clone a speaker from ~10s reference audio.

OpenVoice V2 demo

Core ML conversion of myshell-ai/OpenVoice 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 audio to audio
Upstream myshell-ai/OpenVoice
Packages 2
Download size 58 MB
Minimum iOS 17.0
Peak RAM ~500 MB

Files

File Size Compute units SHA-256
OpenVoice_SpeakerEncoder.mlpackage.zip 1 MB cpuAndGPU c3f2a96aaf5ecb5c…
OpenVoice_VoiceConverter.mlpackage.zip 57 MB cpuAndGPU ef3ce8a2d1564aef…
Total 58 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 "openvoice/*" --local-dir ./openvoice
unzip './openvoice/openvoice/*.zip' -d ./openvoice

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 OpenVoice_SpeakerEncoder(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 2 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/OpenVoiceDemo, 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

License

The conversion inherits the upstream license: MIT.

Credits

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