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72 lines
1.5 KiB
Python
72 lines
1.5 KiB
Python
from onnx_modules.V220_OnnxInference import OnnxInferenceSession
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import numpy as np
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Session = OnnxInferenceSession(
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{
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"enc" : "onnx/BertVits2.2PT/BertVits2.2PT_enc_p.onnx",
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"emb_g" : "onnx/BertVits2.2PT/BertVits2.2PT_emb.onnx",
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"dp" : "onnx/BertVits2.2PT/BertVits2.2PT_dp.onnx",
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"sdp" : "onnx/BertVits2.2PT/BertVits2.2PT_sdp.onnx",
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"flow" : "onnx/BertVits2.2PT/BertVits2.2PT_flow.onnx",
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"dec" : "onnx/BertVits2.2PT/BertVits2.2PT_dec.onnx"
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},
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Providers = ["CPUExecutionProvider"]
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)
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#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
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x = np.expand_dims(
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np.array(
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[
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0,
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97,
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0,
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8,
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0,
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78,
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0,
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8,
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0,
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76,
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0,
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37,
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0,
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40,
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0,
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97,
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0,
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8,
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0,
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23,
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0,
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8,
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0,
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74,
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0,
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26,
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0,
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104,
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0,
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]
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),
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0
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)
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tone = np.zeros_like(x)
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language = np.zeros_like(x)
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sid = np.array([0])
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bert = np.random.randn(x.shape[1], 1024)
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ja_bert = np.random.randn(x.shape[1], 1024)
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en_bert = np.random.randn(x.shape[1], 1024)
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emo = np.random.randn(512, 1)
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audio = Session(
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x,
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tone,
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language,
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bert,
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ja_bert,
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en_bert,
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emo,
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sid
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)
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print(audio)
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