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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
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55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
import torch
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from infer.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
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if __name__ == "__main__":
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MoeVS = True # 模型是否为MoeVoiceStudio（原MoeSS）使用
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ModelPath = "Shiroha/shiroha.pth" # 模型路径
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ExportedPath = "model.onnx" # 输出路径
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hidden_channels = 256 # hidden_channels，为768Vec做准备
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cpt = torch.load(ModelPath, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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print(*cpt["config"])
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test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
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test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
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test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹）
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test_pitchf = torch.rand(1, 200) # nsf基频
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test_ds = torch.LongTensor([0]) # 说话人ID
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test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子）
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device = "cpu" # 导出时设备（不影响使用模型）
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net_g = SynthesizerTrnMsNSFsidM(
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*cpt["config"], is_half=False
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) # 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）
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net_g.load_state_dict(cpt["weight"], strict=False)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
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output_names = [
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"audio",
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]
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# net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
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torch.onnx.export(
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net_g,
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(
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test_phone.to(device),
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test_phone_lengths.to(device),
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test_pitch.to(device),
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test_pitchf.to(device),
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test_ds.to(device),
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test_rnd.to(device),
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),
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ExportedPath,
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dynamic_axes={
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"phone": [1],
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"pitch": [1],
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"pitchf": [1],
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"rnd": [2],
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},
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do_constant_folding=False,
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opset_version=16,
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verbose=False,
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input_names=input_names,
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output_names=output_names,
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)
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