145 lines
4.2 KiB
Python
145 lines
4.2 KiB
Python
import argparse
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import json
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import torch
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import utils
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from onnxexport.model_onnx_speaker_mix import SynthesizerTrn
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parser = argparse.ArgumentParser(description='SoVitsSvc OnnxExport')
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def OnnxExport(path=None):
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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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num_frames = 200
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test_hidden_unit = torch.rand(1, num_frames, SVCVITS.gin_channels)
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test_pitch = torch.rand(1, num_frames)
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test_vol = torch.rand(1, num_frames)
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test_mel2ph = torch.LongTensor(torch.arange(0, num_frames)).unsqueeze(0)
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test_uv = torch.ones(1, num_frames, dtype=torch.float32)
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test_noise = torch.randn(1, 192, num_frames)
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test_sid = torch.LongTensor([0])
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export_mix = True
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if len(hps.spk) < 2:
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export_mix = False
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if export_mix:
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spk_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(hps.spk)
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test_sid = test_sid.unsqueeze(0)
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test_sid = test_sid.repeat(num_frames, 1)
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SVCVITS.eval()
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if export_mix:
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daxes = {
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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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else:
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daxes = {
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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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}
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input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"]
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output_names = ["audio", ]
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if SVCVITS.vol_embedding:
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input_names.append("vol")
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vol_dadict = {"vol" : [1]}
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daxes.update(vol_dadict)
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test_inputs = (
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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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test_vol.to(device)
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)
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else:
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test_inputs = (
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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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# SVCVITS = torch.jit.script(SVCVITS)
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SVCVITS(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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test_vol.to(device))
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SVCVITS.dec.OnnxExport()
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torch.onnx.export(
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SVCVITS,
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test_inputs,
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f"checkpoints/{path}/{path}_SoVits.onnx",
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dynamic_axes=daxes,
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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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)
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vec_lay = "layer-12" if SVCVITS.gin_channels == 768 else "layer-9"
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spklist = []
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for key in hps.spk.keys():
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spklist.append(key)
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MoeVSConf = {
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"Folder" : f"{path}",
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"Name" : f"{path}",
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"Type" : "SoVits",
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"Rate" : hps.data.sampling_rate,
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"Hop" : hps.data.hop_length,
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"Hubert": f"vec-{SVCVITS.gin_channels}-{vec_lay}",
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"SoVits4": True,
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"SoVits3": False,
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"CharaMix": export_mix,
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"Volume": SVCVITS.vol_embedding,
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"HiddenSize": SVCVITS.gin_channels,
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"Characters": spklist,
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"Cluster": ""
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}
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with open(f"checkpoints/{path}.json", 'w') as MoeVsConfFile:
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json.dump(MoeVSConf, MoeVsConfFile, indent = 4)
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if __name__ == '__main__':
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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）')
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args = parser.parse_args()
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path = args.model_name
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OnnxExport(path)
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