Awesome
encodec.cpp
High-performance inference of Meta's Encodec deep learning based audio codec model:
- Plain C/C++ implementation without dependencies using 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
- Support of 24Khz model
- Mixed F16 / F32 precision
- 4-bit and 8-bit quantization
- Metal support
- cuBLAS support
Implementation details
- The core tensor operations are implemented in C (ggml.h / ggml.c)
- The encoder-decoder architecture and the high-level C-style API are implemented in C++ (encodec.h / encodec.cpp)
- Basic usage is demonstrated in main.cpp.
Usage
Here are the steps for the encodec model.
Get the code
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
:
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.
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.
cmake -DGGML_CUBLAS=ON -DBUILD_SHARED_LIBS=Off ..
cmake --build . --config Release