| | import torch |
| | from onnxexport.model_onnx import SynthesizerTrn |
| | import utils |
| |
|
| | 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] |
| | 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) |
| |
|
