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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)
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