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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) | |