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import torch
import utils
from onnxexport.model_onnx import SynthesizerTrn
def main(NetExport):
path = "SoVits4.0"
if NetExport:
device = torch.device("cpu")
hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
SVCVITS = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model)
_ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None)
_ = SVCVITS.eval().to(device)
for i in SVCVITS.parameters():
i.requires_grad = False
n_frame = 10
test_hidden_unit = torch.rand(1, n_frame, 256)
test_pitch = torch.rand(1, n_frame)
test_mel2ph = torch.arange(0, n_frame, dtype=torch.int64)[None] # torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).unsqueeze(0)
test_uv = torch.ones(1, n_frame, dtype=torch.float32)
test_noise = torch.randn(1, 192, n_frame)
test_sid = torch.LongTensor([0])
input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"]
output_names = ["audio", ]
torch.onnx.export(SVCVITS,
(
test_hidden_unit.to(device),
test_pitch.to(device),
test_mel2ph.to(device),
test_uv.to(device),
test_noise.to(device),
test_sid.to(device)
),
f"checkpoints/{path}/model.onnx",
dynamic_axes={
"c": [0, 1],
"f0": [1],
"mel2ph": [1],
"uv": [1],
"noise": [2],
},
do_constant_folding=False,
opset_version=16,
verbose=False,
input_names=input_names,
output_names=output_names)
if __name__ == '__main__':
main(True)
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