repo_description: vits2 backbone with multilingual-bert
repo_topics: agent bert bert-vits bert-vits2 fish fish-speech llm tts vits vits2 vocoder
code: ﻿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)
  