Fix tools/export_onnx.py file
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@ -1,54 +1,14 @@
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import torch
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from infer.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
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import argparse
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from infer.modules.onnx.export import export_onnx as eo
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# Usage Example:
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# python tools/export_onnx.py --input_model ~/models/my-model.pth --output_model ~/models/my-model.onnx
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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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parser = argparse.ArgumentParser()
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parser.add_argument("--input_model", type=str, help="input model path", default="Shiroha/shiroha.pth")
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parser.add_argument("--output_model", type=str, help="output Onnx model path", default="model.onnx")
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args = parser.parse_args()
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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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ModelPath = args.input_model
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ExportedPath = args.output_model
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eo(ModelPath, ExportedPath)
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