OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
Audio & Voice AI

Bark.cpp

Bark.cpp is a C/C++ implementation for running inference with Suno’s Bark text-to-speech model. It includes a command-line example that generates speech from a text prompt and documents Bark Small and Bark Large support.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bark.cpp — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Bark.cpp when you need a C/C++ implementation of Bark inference and are prepared to build the project and prepare model files locally.

Who it is for

  • C and C++ developers adding Bark speech generation to software
  • Developers prototyping local text-to-speech utilities or speech-generation features
  • Users who want to build the documented command-line inference example from source

Documented uses

  • Generate speech from a text prompt with the Bark model.
  • Build a local command-line text-to-speech example.
  • Prepare Bark checkpoints and convert them to the ggml format used by the example.
  • Optionally quantize eligible model weights using the documented formats.
Before you choose

Requirements & limits

The project is published as a public GitHub repository under the MIT license.

  • The documented setup requires CMake, Python dependencies, downloaded model checkpoints and vocabulary, and model conversion.
  • The README describes CPU and GPU backends, CUDA and Metal, mixed F16/F32 precision, and AVX-family support for x86 architectures.
  • Optional quantization includes 4-bit, 5-bit, and 8-bit formats; the project notes that it leaves the codec model unquantized to preserve audio quality.
  • AudioCraft, AudioLDM2, and Piper are listed as models the maintainers want to implement, not as supported models.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Build Bark.cpp and prepare a Bark Small model

Compile the project, download Bark checkpoints and vocabulary, convert Bark Small weights, and run the documented text-to-speech example.

Before you start

  • The Bark.cpp source repository and its recursive submodules
  • CMake
  • Python 3 and the dependencies listed in requirements.txt

Steps

  1. Clone the repository with submodules, enter the project directory, and initialize submodules recursively.

  2. Create a build directory, run CMake configuration, and build the project in Release configuration.

  3. Install the listed Python dependencies, then download Bark checkpoints and vocabulary with download_weights.py.

  4. Convert the Bark Small model to ggml format using convert.py with the documented F16 option.

  5. Run the built main example with the converted model path, a text prompt, and the documented thread option.

Expected outcome

The README’s example workflow invokes Bark Small inference from a text prompt using the built command-line program.

Guide limitations

  • The optional NVIDIA GPU configuration is shown as a CMake option; the README does not establish that a particular GPU or machine will work.
  • Optional weight quantization supports q4_0, q4_1, q5_0, q5_1, and q8_0; the codec model is not quantized in the documented workflow.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
22 October 2026
Original website title
PABannier/bark.cpp · Official homepage title · retained in its source language
How we review sourcesSuggest a correction