indextts-onnx 0.3.0

IndexTTS2 voice cloning via ONNX Runtime — no PyTorch at inference time

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