98f5917ac9
* Fix: support infer 2.2 models * Fix imports * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
61 lines
1.3 KiB
Python
61 lines
1.3 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.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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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[0], 1024)
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ja_bert = np.random.randn(x.shape[0], 1024)
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en_bert = np.random.randn(x.shape[0], 1024)
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emo = np.random.randn(512, 1)
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audio = Session(x, tone, language, bert, ja_bert, en_bert, emo, sid)
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print(audio)
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