Kokoro in a Container

Kokoro is a solid, lightweight text-to-speech (TTS) model. But here’s the problem: Setting up AI models is annoying.

• Dependencies break.
• GPU drivers don’t cooperate.
• Python environments become a tangled mess.

So, instead of dealing with dependency hell, I built a containerized Kokoro-82M setup - a plug-and-play Docker image that lets you start generating speech in seconds.

No setup. No headaches. Just run a container, pass some text, and get a WAV file.

Why Containerize Kokoro?

Most AI models require specific Python versions, obscure libraries, and GPU dependencies that turn a simple project into a setup nightmare. My containerized solution solves all that:

• No Install Hassles – No need to manually install Python, Torch, or anything else.
• 100% Repeatable – Works the same on your laptop, a cloud VM, or inside Kubernetes.
• GPU or CPU Support – Automatically detects and uses GPU if available.
• Runs Anywhere – Docker ensures compatibility across Windows, macOS, and Linux.

How to Use This

Step 1: Pull the Docker Image

If you just want to run it, grab the pre-built image:

docker pull ghcr.io/codysnider/kokoro:latest

Or build it yourself:

git clone https://github.com/codysnider/kokoro.git
cd kokoro
docker build -t ghcr.io/codysnider/kokoro .

Step 2: Run the Container

To generate speech from text:

mkdir -p "$(pwd)/output"
docker run --rm -v $(pwd)/output:/output ghcr.io/codysnider/kokoro:latest "af_bella" "Hello, world!" "/output/output.wav"

• Creates and output folder
• Mounts the output folder
• Runs the container
• Outputs a WAV file

From there just open the output.wav file.

Final Thoughts

With this containerized Kokoro setup, you don’t have to waste time installing dependencies or fixing broken Python environments.

It’s fast, repeatable, and simple.

GitHub: codysnider/kokoro-container