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README.md

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+---
+license: mit
+library_name: coreml
+pipeline_tag: audio-to-audio
+tags:
+  - coreml
+  - core-ml
+  - ios
+  - macos
+  - apple
+  - on-device
+  - voice-conversion
+  - voice-cloning
+  - arxiv:2312.01479
+---
+
+# OpenVoice V2 — Core ML
+
+*Voice Cloning*
+
+Zero-shot voice conversion. Clone a speaker from ~10s reference audio.
+
+Core ML conversion of [myshell-ai/OpenVoice](https://github.com/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](https://github.com/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
+
+```bash
+hf download mlboydaisuke/coreml-zoo --include "openvoice/*" --local-dir ./openvoice
+unzip './openvoice/openvoice/*.zip' -d ./openvoice
+```
+
+## 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 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`](https://github.com/john-rocky/CoreML-Models/tree/master/sample_apps/OpenVoiceDemo), 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_openvoice.py`](https://github.com/john-rocky/CoreML-Models/blob/master/conversion_scripts/convert_openvoice.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: **MIT**.
+
+## Credits
+
+- Upstream authors: [myshell-ai/OpenVoice](https://github.com/myshell-ai/OpenVoice), 2023
+- Core ML conversion: john-rocky (Daisuke Majima)