AudioCraft vs Bark
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A PyTorch library for researching and running AI audio and music generation models.
Open source · MIT
Text-to-audio model that generates speech, music, and sound effects from text prompts.
The short answer
Choose AudioCraft if you need…
- Researchers who want working code for text-to-music or text-to-sound models
- Developers who are comfortable with Python and PyTorch and want to build audio generation into a project
- Students learning how neural audio codecs and generative audio models work
- Anyone experimenting with generating short music or sound clips from text descriptions
Choose Bark if you need…
- Developers who want to add text-to-speech or sound generation to a Python project
- Researchers studying generative audio models
- Creators who want expressive, non-robotic voice clips and are fine with unpredictable results
- People experimenting with multilingual speech output
Side-by-side
| AudioCraft | Bark | |
|---|---|---|
| Type | Library | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 23.8k | 39.3k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Jupyter Notebook | Jupyter Notebook |
| Last code update | 8 months ago | 2 years ago |
What each one is
AudioCraft
AudioCraft is a PyTorch library from Meta's research team for generating audio with deep learning. It bundles inference and training code for models such as MusicGen (text-to-music), AudioGen (text-to-sound), and the EnCodec audio codec. People use it to experiment with generating music or sound from text prompts, or to train their own audio models.
Bark
Bark is a transformer-based model from Suno that turns written text into audio. It can produce multilingual speech, music, background noise, simple sound effects, and nonverbal sounds like laughing or sighing. The project provides pretrained checkpoints that the README says are available for commercial use under the MIT License.
Best for
AudioCraft
- Researchers who want working code for text-to-music or text-to-sound models
- Developers who are comfortable with Python and PyTorch and want to build audio generation into a project
- Students learning how neural audio codecs and generative audio models work
- Anyone experimenting with generating short music or sound clips from text descriptions
Bark
- Developers who want to add text-to-speech or sound generation to a Python project
- Researchers studying generative audio models
- Creators who want expressive, non-robotic voice clips and are fine with unpredictable results
- People experimenting with multilingual speech output
- Users with a GPU who want to try generated audio locally
Strengths
AudioCraft
- Bundles several models (MusicGen, AudioGen, EnCodec, MAGNeT, AudioSeal and others) under one library
- Code is released under the MIT license and the project is actively maintained by Meta's research team
- Includes training code and documentation for several of its models, not only inference
Bark
- Produces expressive speech with nonverbal sounds like laughing and sighing, according to the README
- Pretrained checkpoints are MIT-licensed and documented as available for commercial use
- Supports 100+ speaker presets and multiple languages, with Hugging Face Transformers integration
Not ideal for
AudioCraft
- People who want a finished point-and-click music app: the README describes a library, not a consumer product
- Commercial projects needing to use the pretrained model weights: they are released under CC-BY-NC 4.0, which restricts non-commercial use, while only the code is MIT-licensed
- Users without Python or GPU experience, since installation requires PyTorch and specific versions
Bark
- Anyone needing precise, predictable narration: the README says it is a fully generative model that can deviate from prompts in unexpected ways
- Users who need custom voice cloning: the README states it does not currently support it
- Users needing top quality in every language: the README says English quality is best for now
- People who want a simple no-code app: the README describes a Python library and command line usage
Trade-offs
AudioCraft
- Pretrained model weights use a non-commercial license (CC-BY-NC 4.0), which limits commercial use
- Requires specific versions (Python 3.9, PyTorch 2.1.0 per the README), which can complicate setup
Bark
- Outputs are not deterministic and can drift from the prompt, as the README's disclaimer notes
- Default generation works best with about 13 seconds of spoken text, so longer audio needs extra steps
- The README says it was developed for research purposes and Suno takes no responsibility for outputs
Getting started
AudioCraft
Install PyTorch 2.1.0
python -m pip install 'torch==2.1.0'Install setuptools and wheel
python -m pip install setuptools wheelBark
Install from GitHub (recommended; do not use plain pip install bark)
pip install git+https://github.com/suno-ai/bark.gitAlternative: clone and install locally
git clone https://github.com/suno-ai/barkFrequently asked questions
What is the difference between AudioCraft and Bark?
AudioCraft: A PyTorch library for researching and running AI audio and music generation models. Bark: Text-to-audio model that generates speech, music, and sound effects from text prompts. AudioCraft suits researchers who want working code for text-to-music or text-to-sound models, while Bark suits developers who want to add text-to-speech or sound generation to a python project.
Are AudioCraft and Bark free?
AudioCraft: Free and open source. Bark: Free and open source.
Is AudioCraft or Bark more popular?
Bark has more GitHub stars (39.3k) than AudioCraft (23.8k). Popularity isn't the same as fit — check which one matches your use case above.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.