from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
import torch

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）

    if MoeVS:
        net_g = SynthesizerTrnMs256NSFsidM(
            *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",
        ]
        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,
        )
    else:
        net_g = SynthesizerTrnMs256NSFsidO(
            *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"]
        output_names = [
            "audio",
        ]
        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),
            ),
            ExportedPath,
            dynamic_axes={
                "phone": [1],
                "pitch": [1],
                "pitchf": [1],
            },
            do_constant_folding=False,
            opset_version=16,
            verbose=False,
            input_names=input_names,
            output_names=output_names,
        )