indextts-onnx
IndexTTS2 voice cloning via ONNX Runtime. No PyTorch at inference time.
Models are automatically downloaded from HuggingFace on first use.
One-liner
uvx indextts-onnx --ref-audio reference.wav --text "你好，欢迎使用语音克隆系统。" --output output.wav
Python API
from indextts_onnx import IndexTTSInfer
tts = IndexTTSInfer("~/.cache/indextts-onnx/models")
tts.infer("reference.wav", "你好，欢迎使用语音克隆系统。", "output.wav")
Install
pip install indextts-onnx
CLI
indextts-onnx --ref-audio ref.wav --text "Your text" --output out.wav
Options:
--model-dir: Custom model directory (default: auto-download to~/.cache/indextts-onnx/models)--threads: CPU threads (default: auto)
Architecture
IndexTTS2 inference involves 10 ONNX models. This package runs them with ONNX Runtime + numpy, eliminating the PyTorch runtime dependency (~3.3GB total):
| Model | Format | Size | Purpose |
|---|---|---|---|
| wav2vec2bert | int8 | 396M | Semantic feature extraction |
| semantic_codec | int8 | 22M | Quantize semantic embeddings |
| campplus | int8 | 8M | Speaker style embedding |
| gpt2_init | int8 | 837M | First autoregressive step |
| gpt2_step | int8 | 476M | Per-token generation step |
| gpt2_forward | int8 | 826M | Forward pass for latent |
| s2mel_ref | int8 | 4M | Reference length regulation |
| s2mel_gen | int8 | 4M | Generation preprocessing |
| dit_step | fp32 | 363M | DiT flow matching step |
| bigvgan | fp32 | 429M | Neural vocoder |
GPT2 models use int8 quantization (3x faster). DiT and BigVGAN stay fp32 (int8 degrades quality for flow matching).
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for indextts-onnx 0.3.0
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