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bluev2-onnx-int8
notmax123/bluev2-onnx-int8
bluev2-onnx-int8 is a machine learning model from notmax123. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Weight-only dynamic INT8 (QUInt8) ONNX graphs for Blue / Light Blue TTS inference with ONNX Runtime. Same multilingual pipeline and file layout as the full-precision bundle (notmax123/blue-onnx-v2): Hebrew, English, S…
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Updated Apr 29, 2026
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109 MB
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.onnx109 MB · 100%
At a glance
- Access
- Public
- Created
- Apr 29, 2026
- Updated
- Apr 29, 2026
- SHA
- 5f715c98
Model card
From the Hugging Face model README
Blue ONNX v2 — INT8 (experimental)
Weight-only dynamic INT8 (QUInt8) ONNX graphs for Blue / Light Blue TTS inference with ONNX Runtime. Same multilingual pipeline and file layout as the full-precision bundle (notmax123/blue-onnx-v2): Hebrew, English, Spanish, Italian, and German (including mixed-language text with XML-style tags).
Trade-offs
| FP32 slim (blue-onnx-v2) | This repo (INT8) | |
|---|---|---|
| Quality | Recommended default | Experimental; may degrade prosody / clarity |
| Size | Larger | Smaller disk footprint |
| Graph cleanup | After export: onnxslim | Not slimmed (quantization runs on the unslimmed export) |
Use this bundle when you need a smaller download or want to benchmark INT8 on CPU/GPU; for production quality, prefer the FP32 slim Hub model.
Files in this repository
| File | Role |
|---|---|
text_encoder.onnx | Text encoder; phone IDs + per-voice style (TTL) |
vector_estimator.onnx | Flow / vector field; CFG and unconditional latents baked in |
vocoder.onnx | Decoder to waveform; stats baked in-graph |
duration_predictor.onnx | Style-conditioned duration; style_dp from voice JSON |
codec_encoder.onnx | Mel → latent for reference zero-shot style |
style_encoder.onnx | Reference latent → style_ttl |
duration_style_encoder.onnx | Reference latent → style_dp |
tts.json | Runtime dimensions / version (tts_version) |
vocab.json | Character vocabulary for the text encoder |
Voice style JSON is not included. Use per-line JSON files (e.g. female1.json) from the BlueTTS repo and pass them to BlueTTS(..., style_json=...).
For Hebrew G2P, add renikud model.onnx next to your app (see main Blue README).
Download
hf download notmax123/bluev2-onnx-int8 --repo-type model --local-dir ./onnx_int8
Usage (Python)
Requires pip install blue-onnx (or a clone of BlueTTS with blue_onnx on the path). Point onnx_dir at the folder above:
import soundfile as sf
from blue_onnx import BlueTTS
tts = BlueTTS(
onnx_dir="onnx_int8",
style_json="voices/female1.json",
renikud_path="model.onnx", # optional; Hebrew
)
audio, sr = tts.synthesize("Hello, world.", lang="en")
sf.write("out.wav", audio, sr)
How these graphs were produced
Exported from PyTorch checkpoints with exports/export_onnx.py using --int8 only (no --slim): ONNX Runtime dynamic quantization, per-tensor weight QUInt8, per_channel=False. Some ops remain float where quantization does not apply; you may see warnings during export.
License
Same as the BlueTTS project: MIT.
- Created
- Apr 29, 2026
- Updated
- Apr 29, 2026