import torch from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM if __name__ == "__main__": MoeVS = True # 模型是否为MoeVoiceStudio(原MoeSS)使用 ModelPath = "Shiroha/shiroha.pth" # 模型路径 ExportedPath = "model.onnx" # 输出路径 hidden_channels = 256 # hidden_channels,为768Vec做准备 cpt = torch.load(ModelPath, map_location="cpu") cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk print(*cpt["config"]) test_phone = torch.rand(1, 200, hidden_channels) # hidden unit test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度(貌似没啥用) test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频(单位赫兹) test_pitchf = torch.rand(1, 200) # nsf基频 test_ds = torch.LongTensor([0]) # 说话人ID test_rnd = torch.rand(1, 192, 200) # 噪声(加入随机因子) device = "cpu" # 导出时设备(不影响使用模型) net_g = SynthesizerTrnMsNSFsidM( *cpt["config"], is_half=False ) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16) net_g.load_state_dict(cpt["weight"], strict=False) input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] output_names = [ "audio", ] # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出 torch.onnx.export( net_g, ( test_phone.to(device), test_phone_lengths.to(device), test_pitch.to(device), test_pitchf.to(device), test_ds.to(device), test_rnd.to(device), ), ExportedPath, dynamic_axes={ "phone": [1], "pitch": [1], "pitchf": [1], "rnd": [2], }, do_constant_folding=False, opset_version=16, verbose=False, input_names=input_names, output_names=output_names, )