https://github.com/pabannier/encodec.cpp

Port of Meta's Encodec in C/C++
https://github.com/pabannier/encodec.cpp

Last synced: 1 day ago
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Port of Meta's Encodec in C/C++

• Host: GitHub
• URL: https://github.com/pabannier/encodec.cpp
• Owner: PABannier
• Created: 2023-06-19T22:48:29.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-29T10:08:29.000Z (4 months ago)
• Last Synced: 2024-07-29T13:21:41.614Z (4 months ago)
• Language: C++
• Size: 3.86 MB
• Stars: 185
• Watchers: 10
• Forks: 14
• Open Issues: 7
• Metadata Files:
  • Readme: README.md

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README

        
# encodec.cpp

![encodec.cpp](./assets/banner.png)

[![Actions Status](https://github.com/PABannier/encodec.cpp/actions/workflows/build.yml/badge.svg)](https://github.com/PABannier/encodec.cpp/actions)

[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

High-performance inference of [Meta's Encodec](https://github.com/facebookresearch/encodec) deep learning based audio codec model:

- Plain C/C++ implementation without dependencies using [ggml](https://github.com/ggerganov/ggml)

## Demo

Here is a demo of running Encodec on a single M1 MacBook Pro:

https://github.com/PABannier/encodec.cpp/assets/12958149/d11561be-98e9-4504-bba7-86bcc233a499

## Roadmap

- [x] Support of 24Khz model

- [x] Mixed F16 / F32 precision

- [ ] 4-bit and 8-bit quantization

- [ ] Metal support

- [ ] CoreML support

## Implementation details

- The core tensor operations are implemented in C ([ggml.h](ggml.h) / [ggml.c](ggml.c))

- The encoder-decoder architecture and the high-level C-style API are implemented in C++ ([encodec.h](encodec.h) / [encodec.cpp](encodec.cpp))

- Basic usage is demonstrated in [main.cpp](examples/main).

## Usage

Here are the steps for the encodec model.

### Get the code

```bash

git clone --recurse-submodules https://github.com/PABannier/encodec.cpp.git

cd encodec.cpp

```

### Build

In order to build encodec.cpp you must use `CMake`:

```bash

mkdir build

cd build

cmake ..

cmake --build . --config Release

```

### Using Metal

Offloading to GPU is possible with the Metal backend for MacOS. Performance are not improved but

the power consumption and CPU activity is reduced.

```bash

cmake -DGGML_METAL=ON -DBUILD_SHARED_LIBS=Off ..

cmake --build . --config Release

```

### Using cuBLAS

The inference can be offloaded on a CUDA backend with cuBLAS.

```bash

cmake -DGGML_CUBLAS=ON -DBUILD_SHARED_LIBS=Off ..

cmake --build . --config Release

```