mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-24 22:15:02 +03:00
68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
import torch
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import onnxruntime
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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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import numpy as np
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class OnnxRVCInferencer(Inferencer):
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def loadModel(self, file: str, gpu: int):
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(
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onnxProviders,
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onnxProviderOptions,
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) = DeviceManager.get_instance().getOnnxExecutionProvider(gpu)
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onnx_session = onnxruntime.InferenceSession(
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file, providers=onnxProviders, provider_options=onnxProviderOptions
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)
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# check half-precision
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first_input_type = onnx_session.get_inputs()[0].type
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if first_input_type == "tensor(float)":
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self.isHalf = False
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else:
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self.isHalf = True
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self.model = onnx_session
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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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) -> torch.Tensor:
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if pitch is None or pitchf is None:
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raise RuntimeError("[Voice Changer] Pitch or Pitchf is not found.")
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# print("INFER1", self.model.get_providers())
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# print("INFER2", self.model.get_provider_options())
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# print("INFER3", self.model.get_session_options())
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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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"pitch": pitch.cpu().numpy().astype(np.int64),
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"pitchf": pitchf.cpu().numpy().astype(np.float32),
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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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"pitch": pitch.cpu().numpy().astype(np.int64),
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"pitchf": pitchf.cpu().numpy().astype(np.float32),
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"sid": sid.cpu().numpy().astype(np.int64),
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},
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)
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return torch.tensor(np.array(audio1))
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