BlueV3-onnx

BlueV3 — ONNX Runtime Bundle

ONNX export of BlueV3 TTS for CPU / CUDA / TensorRT inference. Includes the vocoder (codec decoder).

TTS version: v1.7.3 · Sample rate: 44.1 kHz · Exported from PyTorch ckpt_step_767000 + AE ae_541000

Contents

Companion repo

PyTorch weights (no codec): notmax123/BlueV3

Download

hf download notmax123/BlueV3-onnx --local-dir ./onnx_models

Inference contract

1. Style — provide style_ttl [1, 50, 256] and style_dp [1, 8, 16] (from a style JSON / reference encoder).
2. Text — IPA / phoneme string → text_ids, text_mask.
3. Duration — duration_predictor → seconds; divide by speed; convert to latent length with base_chunk_size=512, chunk_compress_factor=6.
4. Encode — text_encoder(text_ids, style_ttl, text_mask) → text_emb.
5. Sample — loop vector_estimator for N steps (e.g. 8). Output is the next latent state (CFG is inside the graph; do not apply CFG again).
6. Decode — unnormalize with stats.npz:

   z = (x / normalizer_scale) * std + mean   # raw 144-d
   

   optionally drop the last compressed frame, then vocoder(latent=z) → wav_tts.

vocoder.onnx expects raw (unnormalized) 144-channel latents (normalizer_scale=1 inside the export).

Example (this codebase)

# ONNX (ORT CUDA / CPU)
uv run python run_onnx_inference.py --onnx_dir ./onnx_models --speaker netsiga --steps 8

# TensorRT (after building engines from these ONNX files)
uv run python create_tensorrt.py --onnx_dir ./onnx_models --engine_dir trt_engines
uv run python benchmark_trt.py --style_json voice_styles/Rotem.json --steps 8 --out out.wav

I/O cheat sheet

text_encoder: text_ids, style_ttl, text_mask → text_emb
vector_estimator: noisy_latent, text_emb, style_ttl, latent_mask, text_mask, current_step, total_step → denoised_latent
duration_predictor: text_ids, style_dp, text_mask → duration
vocoder: latent [B, 144, T] → wav_tts

License

MIT (see frontmatter).