BlueV3 Onnx

--- license: mit library_name: onnx tags: - text-to-speech - tts - hebrew - multilingual - flow-matching - onnx - onnxruntime pipeline_tag: text-to-speech --- # 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 | File | Role | |------|------| | `text_encoder.onnx` | Phoneme IDs + style → text embedding | | `vector_estimator.onnx` | Flow-matching Euler step (CFG baked in) | | `vocoder.onnx` | Latent → 44.1 kHz waveform (**codec**) | | `duration_predictor.onnx` | Text + `style_dp` → duration (seconds) | | `stats.npz` | Latent `mean` / `std` / `normalizer_scale` | | `uncond.npz` | Unconditional tokens (CFG / debugging) | | `tts.json` | Runtime config | ## Companion repo PyTorch weights (no codec): [`notmax123/BlueV3`](https://huggingface.co/notmax123/BlueV3) ## Download ```bash 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`: ```text 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) ```bash # 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_id...

Source context: 0 downloads · 1 likes · Pipeline text-to-speech · Library onnx · Repo notmax123/BlueV3-onnx