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48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
import torch
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import numpy as np
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from const import EnumInferenceTypes
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from voice_changer.RVC.inferencer.OnnxRVCInferencer import OnnxRVCInferencer
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class OnnxRVCInferencerNono(OnnxRVCInferencer):
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def loadModel(self, file: str, gpu: int, inferencerTypeVersion: str | None = None):
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super().loadModel(file, gpu, inferencerTypeVersion)
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self.setProps(EnumInferenceTypes.onnxRVCNono, file, self.isHalf, gpu)
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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 | None,
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pitchf: torch.Tensor | None,
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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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if self.isHalf:
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audio1 = self.model.run(
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["audio"],
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{
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"feats": feats.cpu().numpy().astype(np.float16),
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"p_len": pitch_length.cpu().numpy().astype(np.int64),
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"sid": sid.cpu().numpy().astype(np.int64),
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},
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)
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else:
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audio1 = self.model.run(
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["audio"],
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{
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"feats": feats.cpu().numpy().astype(np.float32),
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"p_len": pitch_length.cpu().numpy().astype(np.int64),
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"sid": sid.cpu().numpy().astype(np.int64),
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},
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)
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if self.inferencerTypeVersion == "v2.1" or self.inferencerTypeVersion == "v1.1":
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res = audio1[0]
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else:
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res = np.array(audio1)[0][0, 0]
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res = np.clip(res, -1.0, 1.0)
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return torch.tensor(res)
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