blue-onnx 0.2.2

Hebrew Text-to-Speech inference using ONNX and TensorRT

Blue

Text-to-speech inference with ONNX Runtime; optional TensorRT acceleration on NVIDIA GPUs.

   

Hebrew, English, Spanish, Italian, and German — samples and a live demo on the site and Space above.

Install

Users (PyPI)

pip install blue-onnx

Optional accelerators (PyPI): install the extra, then drop the stock CPU wheel so a single build owns onnxruntime:

• Intel OpenVINO: pip install "blue-onnx[openvino]" then pip uninstall onnxruntime
• NVIDIA CUDA: pip install "blue-onnx[gpu]" then pip uninstall onnxruntime

This repository

git clone https://github.com/maxmelichov/BlueTTS.git
cd BlueTTS
uv sync

Optional extras:

uv sync --extra openvino   # Intel OpenVINO EP (then: uv pip uninstall onnxruntime)
uv sync --extra gpu        # NVIDIA CUDA ORT (then: uv pip uninstall onnxruntime)
uv sync --extra tensorrt   # TensorRT path (see below)
uv sync --extra export     # PyTorch / export tooling

The default environment uses the stock onnxruntime CPU wheel. OpenVINO and CUDA (gpu) are optional; each adds a second ONNX Runtime distribution until you remove the stock CPU wheel so the accelerator build owns the onnxruntime import (uv pip uninstall onnxruntime, or the same with pip after a PyPI install with [openvino] or [gpu]). Do not combine the openvino and gpu extras.

Models

ONNX bundle (default voices, excludes the large all_voices set):

uv run hf download notmax123/blue-onnx --repo-type model --local-dir ./onnx_models \
  --exclude "voices/all_voices/**"

Optional

• Hebrew G2P:

  wget -O model.onnx https://huggingface.co/thewh1teagle/renikud/resolve/main/model.onnx
  

• 2000+ voice JSONs:

  uv run hf download notmax123/blue-onnx voices/all_voices/ --repo-type model --local-dir ./onnx_models
  

• PyTorch weights (export new voices): uv sync --extra export then

  uv run hf download notmax123/blue --repo-type model --local-dir ./pt_models
  

Usage

Examples below use voices/female1.json from this repo. If you downloaded all_voices, pick paths under onnx_models/voices/all_voices/ (see manifest.tsv).

Quick start

import soundfile as sf
from blue_onnx import BlueTTS

tts = BlueTTS(
    onnx_dir="onnx_models",
    style_json="voices/female1.json",
    renikud_path="model.onnx",
)

samples, sr = tts.synthesize("שלום, זהו מודל דיבור בעברית.", lang="he")
sf.write("output.wav", samples, sr)

mixed = "שלום לכולם, <en>welcome to the presentation</en>, <es>espero que lo disfruten</es>."
samples, sr = tts.synthesize(mixed, lang="he")
sf.write("mixed_output.wav", samples, sr)

If you are editing this repo without installing the package, use from src.blue_onnx import BlueTTS (as in examples/) or put src on PYTHONPATH.

Examples

Outputs go to examples/out/ when run from the repo root.

uv run python examples/hebrew.py
uv run python examples/english.py
uv run python examples/spanish.py
uv run python examples/italian.py
uv run python examples/german.py
uv run python examples/mixed.py
uv run python examples/app.py --lang en --text "Hello world."

TensorRT (NVIDIA only)

1. Dependencies:

uv sync --extra tensorrt
uv pip install tensorrt-cu12   # separate install; see astral-sh/uv#14313

2. Build engines (details in scripts/README.md):

uv run python scripts/create_tensorrt.py \
  --onnx_dir onnx_models --engine_dir trt_engines --precision fp32 --config config/tts.json

Note: examples/all_langs_and_mix.py --tensorrt

Citations

@ARTICLE{2025arXiv250323108K,
       author = {{Kim}, Hyeongju and {Yang}, Jinhyeok and {Yu}, Yechan and {Ji}, Seunghun and {Morton}, Jacob and {Bous}, Frederik and {Byun}, Joon and {Lee}, Juheon},
        title = "{SupertonicTTS: Towards Highly Efficient and Streamlined Text-to-Speech System}",
      journal = {arXiv e-prints},
     keywords = {Audio and Speech Processing, Machine Learning, Sound},
        pages = {arXiv:2503.23108},
}
@article{kim2025training,
  title={Training Flow Matching Models with Reliable Labels via Self-Purification},
  author={Kim, Hyeongju and Yu, Yechan and Yi, June Young and Lee, Juheon},
  journal={arXiv preprint arXiv:2509.19091},
  year={2025}
}
@misc{yi2025robustttstrainingselfpurifying,
      title={Robust TTS Training via Self-Purifying Flow Matching for the WildSpoof 2026 TTS Track},
      author={June Young Yi and Hyeongju Kim and Juheon Lee},
      year={2025},
      eprint={2512.17293},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2512.17293},
}

Acknowledgments

Hebrew G2P uses renikud. Thanks to thewh1teagle.

License

MIT