55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import torch
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from rvc.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
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def export_onnx(ModelPath, ExportedPath):
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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]
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vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
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test_phone = torch.rand(1, 200, vec_channels) # hidden unit
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test_phone_lengths = torch.tensor(
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[200]
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).long() # hidden unit length (doesn't seem to help)）
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test_pitch = torch.randint(size=(1, 200), low=5, high=255) # Base frequency (in Hz)
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test_pitchf = torch.rand(1, 200) # nsf base frequency
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test_ds = torch.LongTensor([0]) # Speaker ID
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test_rnd = torch.rand(1, 192, 200) # Noise (add random factor)
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device = "cpu" # Device on export (does not affect use of model)
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net_g = SynthesizerTrnMsNSFsidM(
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*cpt["config"], is_half=False, version=cpt.get("version", "v1")
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) # fp32 export (C++ has to manually rearrange memory to support fp16 so no fp16 for now)
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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) Multi-Role Mixed Track Export
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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=13,
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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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return "Finished"
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