metadata
license: mit
library_name: coreml
pipeline_tag: audio-to-audio
tags:
- coreml
- core-ml
- ios
- macos
- apple
- on-device
- source-separation
- stem-separation
- music
- arxiv:2211.08553
HTDemucs — Core ML
Audio Source Separation
Split music into 4 stems: drums, bass, vocals, other. 44.1 kHz stereo, FP32.
Core ML conversion of adefossez/demucs 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 | adefossez/demucs |
| Packages | 1 |
| Download size | 75 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~1000 MB |
Files
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
HTDemucs_SourceSeparation_F32.mlpackage.zip |
75 MB | cpuOnly |
0fbb941e15a5b2fa… |
| Total | 75 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 "demucs/*" --local-dir ./demucs
unzip './demucs/demucs/*.zip' -d ./demucs
Use in Swift
import CoreML
let config = MLModelConfiguration()
config.computeUnits = .cpuOnly // 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 HTDemucs_SourceSeparation_F32(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)
Demo
- Sample app —
sample_apps/DemucsDemo, 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_htdemucs.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: MIT.
Credits
- Upstream authors: adefossez/demucs, 2021
- Core ML conversion: john-rocky (Daisuke Majima)