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43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
import torch
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from const import EnumInferenceTypes
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from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
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from voice_changer.RVC.inferencer.Inferencer import Inferencer
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from .rvc_models.infer_pack.models import SynthesizerTrnMs256NSFsid
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class RVCInferencer(Inferencer):
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def loadModel(self, file: str, gpu: int):
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self.setProps(EnumInferenceTypes.pyTorchRVC, file, True, gpu)
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dev = DeviceManager.get_instance().getDevice(gpu)
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isHalf = DeviceManager.get_instance().halfPrecisionAvailable(gpu)
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cpt = torch.load(file, map_location="cpu")
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model = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=isHalf)
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model.eval()
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model.load_state_dict(cpt["weight"], strict=False)
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model = model.to(dev)
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if isHalf:
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model = model.half()
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self.model = model
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return self
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def infer(
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self,
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feats: torch.Tensor,
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pitch_length: torch.Tensor,
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pitch: torch.Tensor,
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pitchf: torch.Tensor,
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sid: torch.Tensor,
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convert_length: int | None,
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) -> torch.Tensor:
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res = self.model.infer(feats, pitch_length, pitch, pitchf, sid, convert_length=convert_length)
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res = res[0][0, 0].to(dtype=torch.float32)
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res = torch.clip(res, -1.0, 1.0)
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return res
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