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import argparse | |
import time | |
import numpy as np | |
import onnx | |
from onnxsim import simplify | |
import onnxruntime as ort | |
import onnxoptimizer | |
import torch | |
from model_onnx_48k import SynthesizerTrn | |
import utils | |
from hubert import hubert_model_onnx | |
def main(HubertExport,NetExport): | |
path = "NyaruTaffy" | |
if(HubertExport): | |
device = torch.device("cuda") | |
hubert_soft = hubert_model_onnx.hubert_soft("hubert/model.pt") | |
test_input = torch.rand(1, 1, 16000) | |
input_names = ["source"] | |
output_names = ["embed"] | |
torch.onnx.export(hubert_soft.to(device), | |
test_input.to(device), | |
"hubert3.0.onnx", | |
dynamic_axes={ | |
"source": { | |
2: "sample_length" | |
} | |
}, | |
verbose=False, | |
opset_version=13, | |
input_names=input_names, | |
output_names=output_names) | |
if(NetExport): | |
device = torch.device("cuda") | |
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 | |
test_hidden_unit = torch.rand(1, 50, 256) | |
test_lengths = torch.LongTensor([50]) | |
test_pitch = torch.rand(1, 50) | |
test_sid = torch.LongTensor([0]) | |
input_names = ["hidden_unit", "lengths", "pitch", "sid"] | |
output_names = ["audio", ] | |
SVCVITS.eval() | |
torch.onnx.export(SVCVITS, | |
( | |
test_hidden_unit.to(device), | |
test_lengths.to(device), | |
test_pitch.to(device), | |
test_sid.to(device) | |
), | |
f"checkpoints/{path}/model.onnx", | |
dynamic_axes={ | |
"hidden_unit": [0, 1], | |
"pitch": [1] | |
}, | |
do_constant_folding=False, | |
opset_version=16, | |
verbose=False, | |
input_names=input_names, | |
output_names=output_names) | |
if __name__ == '__main__': | |
main(False,True) | |