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notmax123/
blue-onnx

Name: blue-onnx
Availability: InStock

notmax123/SententicDataTTS

mit

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Blue ONNX — Text-to-speech inference

This repository is the ONNX model bundle for BlueTTS: fast Hebrew-first multilingual speech synthesis with ONNX Runtime and optional NVIDIA TensorRT engines (see the GitHub repo).

Project home (install, usage, examples, TensorRT): https://github.com/maxmelichov/BlueTTS

Try in the browser: Hugging Face Space — notmax123/Blue · Website: lightbluetts.com

Model description

BlueTTS targets native Hebrew quality (including nikud and disambiguation of common homographs) while staying fast enough for real-time and batch use. The reference codebase also supports English, Spanish, Italian, German, and mixed-language segments in one utterance.

Hebrew G2P at inference uses the renikud ONNX model (model.onnx); download it separately (see below).

Related weights

Repo	Purpose
This repo (`notmax123/blue-onnx`)	ONNX checkpoints for `BlueTTS` inference
`notmax123/blue`	PyTorch / Safetensors weights and stats for training, finetuning, and exporting new voice JSON

Download

Repo id is case-sensitive: notmax123/blue-onnx.

bash

hf download notmax123/blue-onnx --repo-type model --local-dir ./onnx_models
wget -O model.onnx https://huggingface.co/thewh1teagle/renikud/resolve/main/model.onnx

Classic CLI equivalent:

huggingface-cli download notmax123/blue-onnx --repo-type model --local-dir ./onnx_models

How to get started

Clone BlueTTS, run uv sync, place the ONNX bundle and model.onnx as in the project README, then use src.blue_onnx.BlueTTS or the examples/ scripts.

Minimal pattern:

python

import soundfile as sf
from src.blue_onnx import BlueTTS

tts = BlueTTS(
    onnx_dir="onnx_models",
    style_json="voices/female1.json",
    renikud_path="model.onnx",
)
samples, sr = tts.synthesize("Hello, this is a short English sample from BlueTTS.", lang="en")
sf.write("output.wav", samples, sr)

(Adjust PYTHONPATH / imports if you install the package instead of running from a clone.)

Speed (indicative)

Reported reference throughput on tuned setups (see project materials and lightbluetts.com); your hardware, batching, and TensorRT vs ONNX Runtime settings will change these numbers.

Hardware	Approx. speed	~1 h audio
NVIDIA RTX 3090 (GPU)	very high RTF	on the order of seconds
Typical CPU	strong RTF	on the order of minutes
Apple M1 class	strong RTF	on the order of a few minutes

Uses

Hebrew and multilingual TTS from text
Real-time or offline apps on CPU or GPU
Audiobooks, accessibility, assistants, and broadcasting pipelines

Citations

BibTeX and paper links are maintained in the BlueTTS README.

License

MIT — see the BlueTTS repository.