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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edu_note.mp4 |
fun_fact.mp4 |
- ONNX Runtime Inference: Kokoro-82M (v0_19) Minimal ONNX Runtime Inference code. It supports
en-usanden-gb. - Optimized ONNX Inference: Mixed precision applied ONNX weights, faster inference and twice smaller in terms of size.
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Clone the repository:
git clone https://github.com/yakhyo/kokoro-82m.git cd kokoro-82m -
Install dependencies:
pip install -r requirements.txt
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Install
espeakfor text-to-speech functionality: Linux:apt-get install espeak -y
docker build -t kokoro-docker . && docker run --rm -p 7860:7860 kokoro-dockerWhat this does:
- Builds the Docker image and tags it as
kokoro-docker. - Runs the container and maps port
7860(container) to port7860(host). - Automatically removes the container when it stops (
--rm).
Access your app at http://localhost:7860 once it's running.
| Filename | Description | Size |
|---|---|---|
kokoro-quant.onnx |
Mixed precision model (faster) | 169MB |
kokoro-v0_19.onnx |
Original model | 330MB |
Run inference using the jupyter notebook:
Specify input text and model weights in inference.py then run:
python inference.pyRun below start Gradio App
python app.pyThis project is licensed under the MIT License.
Model weights licensed under the Apache 2.0