kokoro-onnx

Kokoro-82M ONNX Runtime Inference | Gradio Demo

This repository contains minimal code and resources for inference using the Kokoro-82M model. The repository supports inference using ONNX Runtime and uses optimized ONNX weights for inference.

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Features

• ONNX Runtime Inference: Kokoro-82M (v0_19) Minimal ONNX Runtime Inference code. It supports en-us and en-gb.
• Optimized ONNX Inference: Mixed precision applied ONNX weights, faster inference and twice smaller in terms of size.

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Installation

1. Clone the repository:

   git clone https://github.com/yakhyo/kokoro-82m.git
   cd kokoro-82m
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Install espeak for text-to-speech functionality: Linux:

   apt-get install espeak -y
   

Docker

docker build -t kokoro-docker . && docker run --rm -p 7860:7860 kokoro-docker

What this does:

1. Builds the Docker image and tags it as kokoro-docker.
2. Runs the container and maps port 7860 (container) to port 7860 (host).
3. Automatically removes the container when it stops (--rm).

Access your app at http://localhost:7860 once it’s running.

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Usage

Download ONNX Model

• kokoro-quant.onnx by @taylorchu
• kokoro-v0_19.onnx by @hexgrad

Filename	Description	Size
kokoro-quant.onnx	Mixed precision model (faster)	169MB
kokoro-v0_19.onnx	Original model	330MB

Jupyter Notebook Inference Example

Run inference using the jupyter notebook:

example.ipynb

CLI Inference

Specify input text and model weights in inference.py then run:

python inference.py

Gradio App

Run below start Gradio App

python app.py

License

This project is licensed under the MIT License.

Model weights licensed under the Apache 2.0

Acknowledgments

• https://huggingface.co/hexgrad/Kokoro-82M

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