Introduction to bark.cpp
The bark.cpp project aims to bring advanced text-to-speech capabilities to users by providing a real-time and realistic multilingual audio generation system. This project involves inferencing the Bark model from SunoAI using pure C/C++ programming. It stands out by focusing on efficiency, compatibility, and accessibility.
Key Features
- Plain C/C++ Implementation: The bark.cpp project is crafted using plain C/C++ without any external dependencies, enhancing portability and ease of use.
- Architecture Support: It takes advantage of AVX, AVX2, and AVX512 extensions for x86 architectures to optimize performance.
- Compatible Backends: The system supports both CPU and GPU backends, making it versatile to run on various hardware configurations.
- Precision Flexibility: It supports mixed F16/F32 precision, which allows users to balance performance and resource usage.
- Integer Quantization: The project offers 4-bit, 5-bit, and 8-bit integer quantization options to optimize model size and performance.
- Backend Support: Compatible with both Metal and CUDA backends, it can run efficiently on MacOS and NVIDIA GPUs.
Supported and Planned Models
Currently, bark.cpp supports two primary models:
- Bark Small
- Bark Large
Future aspirations include integrating models like AudioCraft, AudioLDM2, and Piper if the community contributes to these adaptations.
Demonstration
A demo of bark.cpp can be experienced live on Google Colab, showcasing its capabilities in generating audio from text prompts.
Usage Overview
Getting Started
To start using bark.cpp, the code can be cloned from GitHub using:
git clone --recursive https://github.com/PABannier/bark.cpp.git
cd bark.cpp
git submodule update --init --recursive
Building the Project
The project requires CMake for building:
mkdir build
cd build
cmake ..
cmake --build . --config Release
Preparing and Running
To prepare and execute the software:
# Install necessary Python libraries
python3 -m pip install -r requirements.txt
# Download model checkpoints
python3 download_weights.py --out-dir ./models --models bark-small bark
# Convert model to necessary format
python3 convert.py --dir-model ./models/bark-small --use-f16
# Run the inference
./build/examples/main/main -m ./models/bark-small/ggml_weights.bin -p "this is an audio generated by bark.cpp" -t 4
Optional Quantization
For users interested in model size reduction while maintaining quality, weights can be quantized with several strategies:
./build/examples/quantize/quantize ./ggml_weights.bin ./ggml_weights_q4.bin q4_0
Note that the codec model is not quantized to preserve audio quality.
Contributions and Community Involvement
Bark.cpp thrives on community input. Contributions can include:
- Bug Reports: Noticing an issue? Report it to help improve the software.
- Feature Requests: Suggest new models or platform support to expand capabilities.
- Pull Requests: Contribute code improvements or fixes.
Coding Principles
- Keep the codebase clean by avoiding unnecessary third-party dependencies.
- Ensure cross-platform compatibility for broader usability.
With bark.cpp, the goal is to democratize cutting-edge text-to-speech technology by making it accessible and functional across various systems and languages, all while relying on community collaboration for ongoing enhancements.