Model Details
Full Model IDonnx-community/Kokoro-82M-v1.0-ONNX
Pipeline / Tasktext-to-speech
Librarytransformers.js
Downloads (all-time)679.6K
Likes281
Last Modified2/8/2025
Author / Orgonnx-community
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("text-to-speech", model="onnx-community/Kokoro-82M-v1.0-ONNX")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformers.jsonnxstyle_text_to_speech_2text-to-speechenbase_model:hexgrad/Kokoro-82Mbase_model:quantized:hexgrad/Kokoro-82Mlicense:apache-2.0region:us
More text-to-speech Models
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Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗← Browse All Models🤖 Task: text-to-speech
This model is designed for the text-to-speech task. Explore more models for this use case.
All text-to-speech Models →📊 Popularity
⬇ Downloads679.6K
❤️ Community Likes281
🛠️ Requirements
- →Install: pip install transformers.js
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.