VC PROCESSING!!!! EXCEPTION!!! "slow_conv2d_cpu"

対応：v.1.5.1.15aにダウングレードしたらうごいた

code:zsh

VC PROCESSING!!!! EXCEPTION!!! "slow_conv2d_cpu" not implemented for 'Half'

Traceback (most recent call last):

File "voice_changer\VoiceChanger.py", line 197, in on_request

File "voice_changer\RVC\RVC.py", line 277, in inference

File "voice_changer\RVC\RVC.py", line 267, in _pyTorch_inference

File "voice_changer\RVC\custom_vc_infer_pipeline.py", line 149, in pipeline

File "voice_changer\RVC\custom_vc_infer_pipeline.py", line 86, in vc

File "fairseq\models\hubert\hubert.py", line 535, in extract_features

res = self.forward(

File "fairseq\models\hubert\hubert.py", line 437, in forward

features = self.forward_features(source)

File "fairseq\models\hubert\hubert.py", line 392, in forward_features

features = self.feature_extractor(source)

File "torch\nn\modules\module.py", line 1194, in _call_impl

return forward_call(*input, **kwargs)

File "fairseq\models\wav2vec\wav2vec2.py", line 895, in forward

x = conv(x)

File "torch\nn\modules\module.py", line 1194, in _call_impl

return forward_call(*input, **kwargs)

File "torch\nn\modules\container.py", line 204, in forward

input = module(input)

File "torch\nn\modules\module.py", line 1194, in _call_impl

return forward_call(*input, **kwargs)

File "torch\nn\modules\conv.py", line 313, in forward

return self._conv_forward(input, self.weight, self.bias)

File "torch\nn\modules\conv.py", line 309, in _conv_forward

return F.conv1d(input, weight, bias, self.stride,

RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'

ChatGPT.icon

The error message "slow_conv2d_cpu" not implemented for 'Half' usually occurs when you try to perform convolutional operations on half-precision floating-point numbers (float16 or "Half" in PyTorch) using a CPU...

To solve this issue, you can try either of the following solutions:

Convert your inputs to a higher-precision floating-point format (e.g., float32) before performing the convolution operation.

Use a GPU for your computations instead of a CPU. GPUs are optimized for matrix operations and can handle half-precision floating-point numbers much more efficiently than CPUs.

v.1.5.1.15b

v.1.5.1.15aにしたらうごいた