mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-02-02 16:23:58 +03:00
73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
import numpy as np
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import torch
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import json
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from voice_changer.LLVC.model.llvc import Net
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from voice_changer.utils.VoiceChangerModel import AudioInOutFloat
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class LLVCInferencer:
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def loadModel(self, checkpoint_path: str, config_path: str):
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with open(config_path) as f:
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config = json.load(f)
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model = Net(**config["model_params"])
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model.load_state_dict(torch.load(checkpoint_path, map_location="cpu")["model"])
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self.config = config
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self.model = model
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self.enc_buf, self.dec_buf, self.out_buf = self.model.init_buffers(1, torch.device("cpu"))
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if hasattr(self.model, "convnet_pre"):
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self.convnet_pre_ctx = self.model.convnet_pre.init_ctx_buf(1, torch.device("cpu"))
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else:
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self.convnet_pre_ctx = None
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self.audio_buffer: AudioInOutFloat = np.zeros(0, dtype=np.float32)
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self.front_ctx: AudioInOutFloat | None = None
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return self
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def infer(
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self,
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audio: AudioInOutFloat,
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) -> torch.Tensor:
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# print(f"[infer] inputsize:{audio.shape} + rest:{self.audio_buffer.shape}")
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self.audio_buffer = np.concatenate([self.audio_buffer, audio])
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# print(f"[infer] concat size", self.audio_buffer.shape)
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try:
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L = self.model.L
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processing_unit = self.model.dec_chunk_size * L
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chunk_size = (len(self.audio_buffer) // processing_unit) * processing_unit
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chunk = self.audio_buffer[:chunk_size]
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self.audio_buffer = self.audio_buffer[chunk_size:]
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inputTensor = torch.from_numpy(chunk.astype(np.float32)).to("cpu")
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if self.front_ctx is None:
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inputTensor = torch.cat([torch.zeros(L * 2), inputTensor])
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else:
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inputTensor = torch.cat([self.front_ctx, inputTensor])
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self.front_ctx = inputTensor[-L * 2 :]
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# すさまじい勢いでメモリリークしていく。。。？要調査
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audio1, self.enc_buf, self.dec_buf, self.out_buf, self.convnet_pre_ctx = self.model(
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inputTensor.unsqueeze(0).unsqueeze(0),
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self.enc_buf,
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self.dec_buf,
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self.out_buf,
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self.convnet_pre_ctx,
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pad=(not self.model.lookahead),
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)
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# print(f"[infer] input chunk size {chunk.shape} ->(+32) lookaheadsize{inputTensor.shape}->(same chunk) inferedsize{audio1.shape}")
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audio1 = audio1.squeeze(0).squeeze(0)
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return audio1
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except Exception as e:
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raise RuntimeError(f"Exeption in {self.__class__.__name__}", e)
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# def isTorch(self):
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# return True
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