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license: cc-by-nc-4.0
tags:
- audio-to-audio
- demucs
- libtorch
- coreml
- metal
- ios
- macos
pipeline_tag: audio-to-audio
---
# HTDemucs (LibTorch / CoreML Ready)
This repository contains **TorchScript (`.pt`)** exports of the [Hybrid Transformer Demucs (htdemucs)](https://github.com/facebookresearch/demucs) model by Meta Research.
These models are optimized for **C++ Inference** (using LibTorch) on Apple Silicon (Metal/MPS) and CPU.
## ⚠️ License & Attribution
* **Original Model Code:** MIT License (Copyright Meta Platforms, Inc.)
* **Pre-Trained Weights:** [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (Research constraints derived from training data).
**Attribution:**
> Original work by **Meta Research**. Based on the paper "Hybrid Transformers for Music Source Separation" by Alexandre Défossez et al.
> Source Repository: [facebookresearch/demucs](https://github.com/facebookresearch/demucs)
**Disclaimer:**
> This is a **format conversion only**. No fine-tuning was performed. The weights are numerically identical to the original release, but packaged for C++ execution without Python dependencies.
## Model Variants
| Filename | Description | Chunk Size | Target Device |
| :--- | :--- | :--- | :--- |
| `htdemucs_ft.pt` | **Fast Trace** (Recommended) | 8.0s | Metal (MPS) / GPU |
| `htdemucs_6s.pt` | **6-Stem** (Guitar/Piano) | 8.0s | Metal (MPS) / GPU |
| `htdemucs_cpu.pt` | CPU Fallback | 8.0s | CPU |
## Usage (C++)
These models are designed to be loaded directly in C++ using `torch::jit::load()`:
```cpp
#include <torch/script.h>
auto module = torch::jit::load("htdemucs_ft.pt");
module.to(torch::kMPS); // Or kCPU
module.eval();
// Input: [1, 2, Samples]
auto output = module.forward({input_tensor}).toTensor();
```
## Integrity
SHA256 checksums are provided in `SHA256SUMS.txt` to verify file integrity.
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