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import argparse
import json
import torch
import utils
from onnxexport.model_onnx_speaker_mix import SynthesizerTrn
parser = argparse.ArgumentParser(description='SoVitsSvc OnnxExport')
def OnnxExport(path=None):
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
num_frames = 200
test_hidden_unit = torch.rand(1, num_frames, SVCVITS.gin_channels)
test_pitch = torch.rand(1, num_frames)
test_vol = torch.rand(1, num_frames)
test_mel2ph = torch.LongTensor(torch.arange(0, num_frames)).unsqueeze(0)
test_uv = torch.ones(1, num_frames, dtype=torch.float32)
test_noise = torch.randn(1, 192, num_frames)
test_sid = torch.LongTensor([0])
export_mix = True
if len(hps.spk) < 2:
export_mix = False
if export_mix:
spk_mix = []
n_spk = len(hps.spk)
for i in range(n_spk):
spk_mix.append(1.0/float(n_spk))
test_sid = torch.tensor(spk_mix)
SVCVITS.export_chara_mix(hps.spk)
test_sid = test_sid.unsqueeze(0)
test_sid = test_sid.repeat(num_frames, 1)
SVCVITS.eval()
if export_mix:
daxes = {
"c": [0, 1],
"f0": [1],
"mel2ph": [1],
"uv": [1],
"noise": [2],
"sid":[0]
}
else:
daxes = {
"c": [0, 1],
"f0": [1],
"mel2ph": [1],
"uv": [1],
"noise": [2]
}
input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"]
output_names = ["audio", ]
if SVCVITS.vol_embedding:
input_names.append("vol")
vol_dadict = {"vol" : [1]}
daxes.update(vol_dadict)
test_inputs = (
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),
test_vol.to(device)
)
else:
test_inputs = (
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)
)
# SVCVITS = torch.jit.script(SVCVITS)
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),
test_vol.to(device))
SVCVITS.dec.OnnxExport()
torch.onnx.export(
SVCVITS,
test_inputs,
f"checkpoints/{path}/{path}_SoVits.onnx",
dynamic_axes=daxes,
do_constant_folding=False,
opset_version=16,
verbose=False,
input_names=input_names,
output_names=output_names
)
vec_lay = "layer-12" if SVCVITS.gin_channels == 768 else "layer-9"
spklist = []
for key in hps.spk.keys():
spklist.append(key)
MoeVSConf = {
"Folder" : f"{path}",
"Name" : f"{path}",
"Type" : "SoVits",
"Rate" : hps.data.sampling_rate,
"Hop" : hps.data.hop_length,
"Hubert": f"vec-{SVCVITS.gin_channels}-{vec_lay}",
"SoVits4": True,
"SoVits3": False,
"CharaMix": export_mix,
"Volume": SVCVITS.vol_embedding,
"HiddenSize": SVCVITS.gin_channels,
"Characters": spklist,
"Cluster": ""
}
with open(f"checkpoints/{path}.json", 'w') as MoeVsConfFile:
json.dump(MoeVSConf, MoeVsConfFile, indent = 4)
if __name__ == '__main__':
parser.add_argument('-n', '--model_name', type=str, default="TransformerFlow", help='模型文件夹名（根目录下新建ckeckpoints文件夹，在此文件夹下建立一个新的文件夹，放置模型，该文件夹名即为此项）')
args = parser.parse_args()
path = args.model_name
OnnxExport(path)
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