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XJP_Voice / onnx_infer.py

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	from onnx_modules.V220_OnnxInference import OnnxInferenceSession
	import numpy as np
	Session = OnnxInferenceSession(
	{
	"enc" : "onnx/BertVits2.2PT/BertVits2.2PT_enc_p.onnx",
	"emb_g" : "onnx/BertVits2.2PT/BertVits2.2PT_emb.onnx",
	"dp" : "onnx/BertVits2.2PT/BertVits2.2PT_dp.onnx",
	"sdp" : "onnx/BertVits2.2PT/BertVits2.2PT_sdp.onnx",
	"flow" : "onnx/BertVits2.2PT/BertVits2.2PT_flow.onnx",
	"dec" : "onnx/BertVits2.2PT/BertVits2.2PT_dec.onnx"
	},
	Providers = ["CPUExecutionProvider"]
	)

	#这里的输入和原版是一样的，只需要在原版预处理结果出来之后加上.numpy()即可
	x = np.array(
	[
	0,
	97,
	0,
	8,
	0,
	78,
	0,
	8,
	0,
	76,
	0,
	37,
	0,
	40,
	0,
	97,
	0,
	8,
	0,
	23,
	0,
	8,
	0,
	74,
	0,
	26,
	0,
	104,
	0,
	]
	)
	tone = np.zeros_like(x)
	language = np.zeros_like(x)
	sid = np.array([0])
	bert = np.random.randn(x.shape[0], 1024)
	ja_bert = np.random.randn(x.shape[0], 1024)
	en_bert = np.random.randn(x.shape[0], 1024)
	emo = np.random.randn(512, 1)

	audio = Session(
	x,
	tone,
	language,
	bert,
	ja_bert,
	en_bert,
	emo,
	sid
	)

	print(audio)