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74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
import argparse
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import time
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import numpy as np
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import onnx
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from onnxsim import simplify
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import onnxruntime as ort
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import onnxoptimizer
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import torch
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from model_onnx_48k import SynthesizerTrn
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import utils
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from hubert import hubert_model_onnx
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def main(HubertExport,NetExport):
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path = "NyaruTaffy"
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if(HubertExport):
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device = torch.device("cuda")
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hubert_soft = hubert_model_onnx.hubert_soft("hubert/model.pt")
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test_input = torch.rand(1, 1, 16000)
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input_names = ["source"]
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output_names = ["embed"]
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torch.onnx.export(hubert_soft.to(device),
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test_input.to(device),
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"hubert3.0.onnx",
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dynamic_axes={
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"source": {
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2: "sample_length"
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}
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},
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verbose=False,
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opset_version=13,
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input_names=input_names,
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output_names=output_names)
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if(NetExport):
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device = torch.device("cuda")
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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, 50, 256)
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test_lengths = torch.LongTensor([50])
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test_pitch = torch.rand(1, 50)
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test_sid = torch.LongTensor([0])
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input_names = ["hidden_unit", "lengths", "pitch", "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_lengths.to(device),
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test_pitch.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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"hidden_unit": [0, 1],
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"pitch": [1]
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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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