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.safetensors), which only run through a Python inference stack. Exporting to ONNX lets a voice run anywhere an ONNX Runtime is available — including entirely client-side, in a browser or Electron app, via onnxruntime-web, with no server or Python install needed. Each voice here was exported with torch.onnx.export + onnxsim (with a fix for a real onnxsim bug that otherwise silently corrupts audio content while still producing a valid-shaped file).voices/<id>/ has two files you need: model.onnx and style_vectors.npy.ku-nlp/deberta-v2-large-japanese-char-wwm) features — the same preprocessing Style-Bert-VITS2 itself does before inference.style_vectors.npy and select a style row (index 0 is "Neutral").model.onnx in any ONNX Runtime build, passing those as inputs: x_tst, x_tst_lengths, sid (speaker ID, 0 for these single-speaker voices), tones, language, bert, style_vec, plus the generation knobs length_scale, sdp_ratio, noise_scale, noise_scale_w. This produces raw audio samples.packages/inference package (runs in-browser via onnxruntime-web). For the reference PyTorch implementation these exports were traced from, see Style-Bert-VITS2 itself.