68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
import torch
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from onnxexport.model_onnx_speaker_mix import SynthesizerTrn
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import utils
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def main(HubertExport, NetExport):
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path = "SummerPockets"
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if NetExport:
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device = torch.device("cpu")
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hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
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SVCVITS = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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**hps.model)
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_ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None)
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_ = SVCVITS.eval().to(device)
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for i in SVCVITS.parameters():
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i.requires_grad = False
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test_hidden_unit = torch.rand(1, 10, SVCVITS.gin_channels)
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test_pitch = torch.rand(1, 10)
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test_mel2ph = torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).unsqueeze(0)
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test_uv = torch.ones(1, 10, dtype=torch.float32)
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test_noise = torch.randn(1, 192, 10)
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export_mix = True
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test_sid = torch.LongTensor([0])
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spk_mix = []
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if export_mix:
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n_spk = len(hps.spk)
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for i in range(n_spk):
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spk_mix.append(1.0/float(n_spk))
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test_sid = torch.tensor(spk_mix)
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SVCVITS.export_chara_mix(n_spk)
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test_sid = test_sid.unsqueeze(0)
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test_sid = test_sid.repeat(10, 1)
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input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"]
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output_names = ["audio", ]
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SVCVITS.eval()
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torch.onnx.export(SVCVITS,
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(
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test_hidden_unit.to(device),
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test_pitch.to(device),
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test_mel2ph.to(device),
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test_uv.to(device),
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test_noise.to(device),
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test_sid.to(device)
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),
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f"checkpoints/{path}/model.onnx",
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dynamic_axes={
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"c": [0, 1],
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"f0": [1],
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"mel2ph": [1],
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"uv": [1],
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"noise": [2],
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"sid":[0]
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},
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do_constant_folding=False,
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opset_version=16,
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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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if __name__ == '__main__':
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main(False, True)
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