Error when using UVR DeEcho models with UVR.uvr_wrapper()

[wAIfu-DEV]

Getting this error message when using UVR-De-Echo-Aggressive.pth or UVR-De-Echo-Normal.pth with UVR.uvr_wrapper()

Traceback (most recent call last):
  File "C:\Users\jeje9\Desktop\rvc_test\rvc_test.py", line 62, in <module>
    for item in generator:
  File "C:\Users\jeje9\Desktop\rvc_test\lib\site-packages\rvc\modules\uvr5\modules.py", line 49, in uvr_wrapper
    pre_fun = func(
  File "C:\Users\jeje9\Desktop\rvc_test\lib\site-packages\rvc\modules\uvr5\vr.py", line 34, in __init__
    model.load_state_dict(cpk)
  File "C:\Users\jeje9\Desktop\rvc_test\lib\site-packages\torch\nn\modules\module.py", line 2152, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for CascadedASPPNet:
        Missing key(s) in state_dict: "stg1_low_band_net.enc1.conv1.conv.0.weight", "stg1_low_band_net.enc1.conv1.conv.1.weight", "stg1_low_band_net.enc1.conv1.conv.1.bias", "stg1_low_band_net.enc1.conv1.conv.1.running_mean", "stg1_low_band_net.enc1.conv1.conv.1.running_var", "stg1_low_band_net.enc1.conv2.conv.0.weight", "stg1_low_band_net.enc1.conv2.conv.1.weight", "stg1_low_band_net.enc1.conv2.conv.1.bias", "stg1_low_band_net.enc1.conv2.conv.1.running_mean", "stg1_low_band_net.enc1.conv2.conv.1.running_var", "stg1_low_band_net.enc2.conv1.conv.0.weight", "stg1_low_band_net.enc2.conv1.conv.1.weight", "stg1_low_band_net.enc2.conv1.conv.1.bias", "stg1_low_band_net.enc2.conv1.conv.1.running_mean", "stg1_low_band_net.enc2.conv1.conv.1.running_var", "stg1_low_band_net.enc2.conv2.conv.0.weight", "stg1_low_band_net.enc2.conv2.conv.1.weight", "stg1_low_band_net.enc2.conv2.conv.1.bias", "stg1_low_band_net.enc2.conv2.conv.1.running_mean", "stg1_low_band_net.enc2.conv2.conv.1.running_var", "stg1_low_band_net.enc3.conv1.conv.0.weight", "stg1_low_band_net.enc3.conv1.conv.1.weight", "stg1_low_band_net.enc3.conv1.conv.1.bias", "stg1_low_band_net.enc3.conv1.conv.1.running_mean", "stg1_low_band_net.enc3.conv1.conv.1.running_var", "stg1_low_band_net.enc3.conv2.conv.0.weight", "stg1_low_band_net.enc3.conv2.conv.1.weight", "stg1_low_band_net.enc3.conv2.conv.1.bias", "stg1_low_band_net.enc3.conv2.conv.1.running_mean", "stg1_low_band_net.enc3.conv2.conv.1.running_var", "stg1_low_band_net.enc4.conv1.conv.0.weight", "stg1_low_band_net.enc4.conv1.conv.1.weight", "stg1_low_band_net.enc4.conv1.conv.1.bias", "stg1_low_band_net.enc4.conv1.conv.1.running_mean", "stg1_low_band_net.enc4.conv1.conv.1.running_var", "stg1_low_band_net.enc4.conv2.conv.0.weight", "stg1_low_band_net.enc4.conv2.conv.1.weight", "stg1_low_band_net.enc4.conv2.conv.1.bias", "stg1_low_band_net.enc4.conv2.conv.1.running_mean", "stg1_low_band_net.enc4.conv2.conv.1.running_var", "stg1_low_band_net.aspp.conv1.1.conv.0.weight", "stg1_low_band_net.aspp.conv1.1.conv.1.weight", "stg1_low_band_net.aspp.conv1.1.conv.1.bias", "stg1_low_band_net.aspp.conv1.1.conv.1.running_mean", "stg1_low_band_net.aspp.conv1.1.conv.1.running_var", "stg1_low_band_net.aspp.conv2.conv.0.weight", "stg1_low_band_net.aspp.conv2.conv.1.weight", "stg1_low_band_net.aspp.conv2.conv.1.bias", "stg1_low_band_net.aspp.conv2.conv.1.running_mean", "stg1_low_band_net.aspp.conv2.conv.1.running_var", "stg1_low_band_net.aspp.conv3.conv.0.weight", "stg1_low_band_net.aspp.conv3.conv.1.weight", "stg1_low_band_net.aspp.conv3.conv.2.weight", "stg1_low_band_net.aspp.conv3.conv.2.bias", "stg1_low_band_net.aspp.conv3.conv.2.running_mean", "stg1_low_band_net.aspp.conv3.conv.2.running_var", "stg1_low_band_net.aspp.conv4.conv.0.weight", "stg1_low_band_net.aspp.conv4.conv.1.weight", "stg1_low_band_net.aspp.conv4.conv.2.weight", "stg1_low_band_net.aspp.conv4.conv.2.bias", "stg1_low_band_net.aspp.conv4.conv.2.running_mean", "stg1_low_band_net.aspp.conv4.conv.2.running_var", "stg1_low_band_net.aspp.conv5.conv.0.weight", "stg1_low_band_net.aspp.conv5.conv.1.weight", "stg1_low_band_net.aspp.conv5.conv.2.weight", "stg1_low_band_net.aspp.conv5.conv.2.bias", "stg1_low_band_net.aspp.conv5.conv.2.running_mean", "stg1_low_band_net.aspp.conv5.conv.2.running_var", "stg1_low_band_net.aspp.bottleneck.0.conv.0.weight", "stg1_low_band_net.aspp.bottleneck.0.conv.1.weight", "stg1_low_band_net.aspp.bottleneck.0.conv.1.bias", "stg1_low_band_net.aspp.bottleneck.0.conv.1.running_mean", "stg1_low_band_net.aspp.bottleneck.0.conv.1.running_var", "stg1_low_band_net.dec4.conv.conv.0.weight", "stg1_low_band_net.dec4.conv.conv.1.weight", "stg1_low_band_net.dec4.conv.conv.1.bias", "stg1_low_band_net.dec4.conv.conv.1.running_mean", "stg1_low_band_net.dec4.conv.conv.1.running_var", "stg1_low_band_net.dec3.conv.conv.0.weight", "stg1_low_band_net.dec3.conv.conv.1.weight", "stg1_low_band_net.dec3.conv.conv.1.bias", "stg1_low_band_net.dec3.conv.conv.1.running_mean", "stg1_low_band_net.dec3.conv.conv.1.running_var", "stg1_low_band_net.dec2.conv.conv.0.weight", "stg1_low_band_net.dec2.conv.conv.1.weight", "stg1_low_band_net.dec2.conv.conv.1.bias", "stg1_low_band_net.dec2.conv.conv.1.running_mean", "stg1_low_band_net.dec2.conv.conv.1.running_var", "stg1_low_band_net.dec1.conv.conv.0.weight", "stg1_low_band_net.dec1.conv.conv.1.weight", "stg1_low_band_net.dec1.conv.conv.1.bias", "stg1_low_band_net.dec1.conv.conv.1.running_mean", "stg1_low_band_net.dec1.conv.conv.1.running_var", "stg1_high_band_net.enc1.conv1.conv.0.weight", "stg1_high_band_net.enc1.conv1.conv.1.weight", "stg1_high_band_net.enc1.conv1.conv.1.bias", "stg1_high_band_net.enc1.conv1.conv.1.running_mean", "stg1_high_band_net.enc1.conv1.conv.1.running_var", "stg1_high_band_net.enc1.conv2.conv.0.weight", "stg1_high_band_net.enc1.conv2.conv.1.weight", "stg1_high_band_net.enc1.conv2.conv.1.bias", "stg1_high_band_net.enc1.conv2.conv.1.running_mean", "stg1_high_band_net.enc1.conv2.conv.1.running_var", "stg1_high_band_net.aspp.conv3.conv.2.weight", "stg1_high_band_net.aspp.conv3.conv.2.bias", "stg1_high_band_net.aspp.conv3.conv.2.running_mean", "stg1_high_band_net.aspp.conv3.conv.2.running_var", "stg1_high_band_net.aspp.conv4.conv.2.weight", "stg1_high_band_net.aspp.conv4.conv.2.bias", "stg1_high_band_net.aspp.conv4.conv.2.running_mean", "stg1_high_band_net.aspp.conv4.conv.2.running_var", "stg1_high_band_net.aspp.conv5.conv.2.weight", "stg1_high_band_net.aspp.conv5.conv.2.bias", "stg1_high_band_net.aspp.conv5.conv.2.running_mean", "stg1_high_band_net.aspp.conv5.conv.2.running_var", "stg1_high_band_net.aspp.bottleneck.0.conv.0.weight", "stg1_high_band_net.aspp.bottleneck.0.conv.1.weight", "stg1_high_band_net.aspp.bottleneck.0.conv.1.bias", "stg1_high_band_net.aspp.bottleneck.0.conv.1.running_mean", "stg1_high_band_net.aspp.bottleneck.0.conv.1.running_var", "stg1_high_band_net.dec4.conv.conv.0.weight", "stg1_high_band_net.dec4.conv.conv.1.weight", "stg1_high_band_net.dec4.conv.conv.1.bias", "stg1_high_band_net.dec4.conv.conv.1.running_mean", "stg1_high_band_net.dec4.conv.conv.1.running_var", "stg1_high_band_net.dec3.conv.conv.0.weight", "stg1_high_band_net.dec3.conv.conv.1.weight", "stg1_high_band_net.dec3.conv.conv.1.bias", "stg1_high_band_net.dec3.conv.conv.1.running_mean", "stg1_high_band_net.dec3.conv.conv.1.running_var", "stg1_high_band_net.dec2.conv.conv.0.weight", "stg1_high_band_net.dec2.conv.conv.1.weight", "stg1_high_band_net.dec2.conv.conv.1.bias", "stg1_high_band_net.dec2.conv.conv.1.running_mean", "stg1_high_band_net.dec2.conv.conv.1.running_var", "stg1_high_band_net.dec1.conv.conv.0.weight", "stg1_high_band_net.dec1.conv.conv.1.weight", "stg1_high_band_net.dec1.conv.conv.1.bias", "stg1_high_band_net.dec1.conv.conv.1.running_mean", "stg1_high_band_net.dec1.conv.conv.1.running_var", "stg2_bridge.conv.0.weight", "stg2_bridge.conv.1.weight", "stg2_bridge.conv.1.bias", "stg2_bridge.conv.1.running_mean", "stg2_bridge.conv.1.running_var", "stg2_full_band_net.enc1.conv1.conv.0.weight", "stg2_full_band_net.enc1.conv1.conv.1.weight", "stg2_full_band_net.enc1.conv1.conv.1.bias", "stg2_full_band_net.enc1.conv1.conv.1.running_mean", "stg2_full_band_net.enc1.conv1.conv.1.running_var", "stg2_full_band_net.enc1.conv2.conv.0.weight", "stg2_full_band_net.enc1.conv2.conv.1.weight", "stg2_full_band_net.enc1.conv2.conv.1.bias", "stg2_full_band_net.enc1.conv2.conv.1.running_mean", "stg2_full_band_net.enc1.conv2.conv.1.running_var", "stg2_full_band_net.enc2.conv1.conv.0.weight", "stg2_full_band_net.enc2.conv1.conv.1.weight", "stg2_full_band_net.enc2.conv1.conv.1.bias", "stg2_full_band_net.enc2.conv1.conv.1.running_mean", "stg2_full_band_net.enc2.conv1.conv.1.running_var", "stg2_full_band_net.enc2.conv2.conv.0.weight", "stg2_full_band_net.enc2.conv2.conv.1.weight", "stg2_full_band_net.enc2.conv2.conv.1.bias", "stg2_full_band_net.enc2.conv2.conv.1.running_mean", "stg2_full_band_net.enc2.conv2.conv.1.running_var", "stg2_full_band_net.enc3.conv1.conv.0.weight", "stg2_full_band_net.enc3.conv1.conv.1.weight", "stg2_full_band_net.enc3.conv1.conv.1.bias", "stg2_full_band_net.enc3.conv1.conv.1.running_mean", "stg2_full_band_net.enc3.conv1.conv.1.running_var", "stg2_full_band_net.enc3.conv2.conv.0.weight", "stg2_full_band_net.enc3.conv2.conv.1.weight", "stg2_full_band_net.enc3.conv2.conv.1.bias", "stg2_full_band_net.enc3.conv2.conv.1.running_mean", "stg2_full_band_net.enc3.conv2.conv.1.running_var", "stg2_full_band_net.enc4.conv1.conv.0.weight", "stg2_full_band_net.enc4.conv1.conv.1.weight", "stg2_full_band_net.enc4.conv1.conv.1.bias", "stg2_full_band_net.enc4.conv1.conv.1.running_mean", "stg2_full_band_net.enc4.conv1.conv.1.running_var", "stg2_full_band_net.enc4.conv2.conv.0.weight", "stg2_full_band_net.enc4.conv2.conv.1.weight", "stg2_full_band_net.enc4.conv2.conv.1.bias", "stg2_full_band_net.enc4.conv2.conv.1.running_mean", "stg2_full_band_net.enc4.conv2.conv.1.running_var", "stg2_full_band_net.aspp.conv1.1.conv.0.weight", "stg2_full_band_net.aspp.conv1.1.conv.1.weight", "stg2_full_band_net.aspp.conv1.1.conv.1.bias", "stg2_full_band_net.aspp.conv1.1.conv.1.running_mean", "stg2_full_band_net.aspp.conv1.1.conv.1.running_var", "stg2_full_band_net.aspp.conv2.conv.0.weight", "stg2_full_band_net.aspp.conv2.conv.1.weight", "stg2_full_band_net.aspp.conv2.conv.1.bias", "stg2_full_band_net.aspp.conv2.conv.1.running_mean", "stg2_full_band_net.aspp.conv2.conv.1.running_var", "stg2_full_band_net.aspp.conv3.conv.0.weight", "stg2_full_band_net.aspp.conv3.conv.1.weight", "stg2_full_band_net.aspp.conv3.conv.2.weight", "stg2_full_band_net.aspp.conv3.conv.2.bias", "stg2_full_band_net.aspp.conv3.conv.2.running_mean", "stg2_full_band_net.aspp.conv3.conv.2.running_var", "stg2_full_band_net.aspp.conv4.conv.0.weight", "stg2_full_band_net.aspp.conv4.conv.1.weight", "stg2_full_band_net.aspp.conv4.conv.2.weight", "stg2_full_band_net.aspp.conv4.conv.2.bias", "stg2_full_band_net.aspp.conv4.conv.2.running_mean", "stg2_full_band_net.aspp.conv4.conv.2.running_var", "stg2_full_band_net.aspp.conv5.conv.0.weight", "stg2_full_band_net.aspp.conv5.conv.1.weight", "stg2_full_band_net.aspp.conv5.conv.2.weight", "stg2_full_band_net.aspp.conv5.conv.2.bias", "stg2_full_band_net.aspp.conv5.conv.2.running_mean", "stg2_full_band_net.aspp.conv5.conv.2.running_var", "stg2_full_band_net.aspp.bottleneck.0.conv.0.weight", "stg2_full_band_net.aspp.bottleneck.0.conv.1.weight", "stg2_full_band_net.aspp.bottleneck.0.conv.1.bias", "stg2_full_band_net.aspp.bottleneck.0.conv.1.running_mean", "stg2_full_band_net.aspp.bottleneck.0.conv.1.running_var", "stg2_full_band_net.dec4.conv.conv.0.weight", "stg2_full_band_net.dec4.conv.conv.1.weight", "stg2_full_band_net.dec4.conv.conv.1.bias", "stg2_full_band_net.dec4.conv.conv.1.running_mean", "stg2_full_band_net.dec4.conv.conv.1.running_var", "stg2_full_band_net.dec3.conv.conv.0.weight", "stg2_full_band_net.dec3.conv.conv.1.weight", "stg2_full_band_net.dec3.conv.conv.1.bias", "stg2_full_band_net.dec3.conv.conv.1.running_mean", "stg2_full_band_net.dec3.conv.conv.1.running_var", "stg2_full_band_net.dec2.conv.conv.0.weight", "stg2_full_band_net.dec2.conv.conv.1.weight", "stg2_full_band_net.dec2.conv.conv.1.bias", "stg2_full_band_net.dec2.conv.conv.1.running_mean", "stg2_full_band_net.dec2.conv.conv.1.running_var", "stg2_full_band_net.dec1.conv.conv.0.weight", "stg2_full_band_net.dec1.conv.conv.1.weight", "stg2_full_band_net.dec1.conv.conv.1.bias", "stg2_full_band_net.dec1.conv.conv.1.running_mean", "stg2_full_band_net.dec1.conv.conv.1.running_var", "stg3_bridge.conv.0.weight", "stg3_bridge.conv.1.weight", "stg3_bridge.conv.1.bias", "stg3_bridge.conv.1.running_mean", "stg3_bridge.conv.1.running_var", "stg3_full_band_net.enc1.conv1.conv.0.weight", "stg3_full_band_net.enc1.conv1.conv.1.weight", "stg3_full_band_net.enc1.conv1.conv.1.bias", "stg3_full_band_net.enc1.conv1.conv.1.running_mean", "stg3_full_band_net.enc1.conv1.conv.1.running_var", "stg3_full_band_net.enc1.conv2.conv.0.weight", "stg3_full_band_net.enc1.conv2.conv.1.weight", "stg3_full_band_net.enc1.conv2.conv.1.bias", "stg3_full_band_net.enc1.conv2.conv.1.running_mean", "stg3_full_band_net.enc1.conv2.conv.1.running_var", "stg3_full_band_net.aspp.conv3.conv.2.weight", "stg3_full_band_net.aspp.conv3.conv.2.bias", "stg3_full_band_net.aspp.conv3.conv.2.running_mean", "stg3_full_band_net.aspp.conv3.conv.2.running_var", "stg3_full_band_net.aspp.conv4.conv.2.weight", "stg3_full_band_net.aspp.conv4.conv.2.bias", "stg3_full_band_net.aspp.conv4.conv.2.running_mean", "stg3_full_band_net.aspp.conv4.conv.2.running_var", "stg3_full_band_net.aspp.conv5.conv.2.weight", "stg3_full_band_net.aspp.conv5.conv.2.bias", "stg3_full_band_net.aspp.conv5.conv.2.running_mean", "stg3_full_band_net.aspp.conv5.conv.2.running_var", "stg3_full_band_net.aspp.bottleneck.0.conv.0.weight", "stg3_full_band_net.aspp.bottleneck.0.conv.1.weight", "stg3_full_band_net.aspp.bottleneck.0.conv.1.bias", "stg3_full_band_net.aspp.bottleneck.0.conv.1.running_mean", "stg3_full_band_net.aspp.bottleneck.0.conv.1.running_var", "stg3_full_band_net.dec4.conv.conv.0.weight", "stg3_full_band_net.dec4.conv.conv.1.weight", "stg3_full_band_net.dec4.conv.conv.1.bias", "stg3_full_band_net.dec4.conv.conv.1.running_mean", "stg3_full_band_net.dec4.conv.conv.1.running_var", "stg3_full_band_net.dec3.conv.conv.0.weight", "stg3_full_band_net.dec3.conv.conv.1.weight", "stg3_full_band_net.dec3.conv.conv.1.bias", "stg3_full_band_net.dec3.conv.conv.1.running_mean", "stg3_full_band_net.dec3.conv.conv.1.running_var", "stg3_full_band_net.dec2.conv.conv.0.weight", "stg3_full_band_net.dec2.conv.conv.1.weight", "stg3_full_band_net.dec2.conv.conv.1.bias", "stg3_full_band_net.dec2.conv.conv.1.running_mean", "stg3_full_band_net.dec2.conv.conv.1.running_var", "stg3_full_band_net.dec1.conv.conv.0.weight", "stg3_full_band_net.dec1.conv.conv.1.weight", "stg3_full_band_net.dec1.conv.conv.1.bias", "stg3_full_band_net.dec1.conv.conv.1.running_mean", "stg3_full_band_net.dec1.conv.conv.1.running_var", "aux1_out.weight", "aux2_out.weight".
        Unexpected key(s) in state_dict: "stg2_low_band_net.0.enc1.conv.0.weight", "stg2_low_band_net.0.enc1.conv.1.weight", "stg2_low_band_net.0.enc1.conv.1.bias", "stg2_low_band_net.0.enc1.conv.1.running_mean", "stg2_low_band_net.0.enc1.conv.1.running_var", "stg2_low_band_net.0.enc1.conv.1.num_batches_tracked", "stg2_low_band_net.0.enc2.conv1.conv.0.weight", "stg2_low_band_net.0.enc2.conv1.conv.1.weight", "stg2_low_band_net.0.enc2.conv1.conv.1.bias", "stg2_low_band_net.0.enc2.conv1.conv.1.running_mean", "stg2_low_band_net.0.enc2.conv1.conv.1.running_var", "stg2_low_band_net.0.enc2.conv1.conv.1.num_batches_tracked", "stg2_low_band_net.0.enc2.conv2.conv.0.weight", "stg2_low_band_net.0.enc2.conv2.conv.1.weight", "stg2_low_band_net.0.enc2.conv2.conv.1.bias", "stg2_low_band_net.0.enc2.conv2.conv.1.running_mean", "stg2_low_band_net.0.enc2.conv2.conv.1.running_var", "stg2_low_band_net.0.enc2.conv2.conv.1.num_batches_tracked", "stg2_low_band_net.0.enc3.conv1.conv.0.weight", 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"stg3_full_band_net.dec2.conv1.conv.1.weight", "stg3_full_band_net.dec2.conv1.conv.1.bias", "stg3_full_band_net.dec2.conv1.conv.1.running_mean", "stg3_full_band_net.dec2.conv1.conv.1.running_var", "stg3_full_band_net.dec2.conv1.conv.1.num_batches_tracked", "stg3_full_band_net.dec1.conv1.conv.0.weight", "stg3_full_band_net.dec1.conv1.conv.1.weight", "stg3_full_band_net.dec1.conv1.conv.1.bias", "stg3_full_band_net.dec1.conv1.conv.1.running_mean", "stg3_full_band_net.dec1.conv1.conv.1.running_var", "stg3_full_band_net.dec1.conv1.conv.1.num_batches_tracked".
        size mismatch for stg1_high_band_net.enc2.conv1.conv.0.weight: copying a param with shape torch.Size([24, 12, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 32, 3, 3]).
        size mismatch for stg1_high_band_net.enc2.conv1.conv.1.weight: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for stg1_high_band_net.enc2.conv1.conv.1.bias: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for stg1_high_band_net.enc2.conv1.conv.1.running_mean: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for stg1_high_band_net.enc2.conv1.conv.1.running_var: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for stg1_high_band_net.enc2.conv2.conv.0.weight: copying a param with shape torch.Size([24, 24, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
        size mismatch for stg1_high_band_net.enc2.conv2.conv.1.weight: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for stg1_high_band_net.enc2.conv2.conv.1.bias: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for stg1_high_band_net.enc2.conv2.conv.1.running_mean: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for stg1_high_band_net.enc2.conv2.conv.1.running_var: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for stg1_high_band_net.enc3.conv1.conv.0.weight: copying a param with shape torch.Size([48, 24, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 64, 3, 3]).
        size mismatch for stg1_high_band_net.enc3.conv1.conv.1.weight: copying a param with shape torch.Size([48]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg1_high_band_net.enc3.conv1.conv.1.bias: copying a param with shape torch.Size([48]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg1_high_band_net.enc3.conv1.conv.1.running_mean: copying a param with shape torch.Size([48]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg1_high_band_net.enc3.conv1.conv.1.running_var: copying a param with shape torch.Size([48]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg1_high_band_net.enc3.conv2.conv.0.weight: copying a param with shape torch.Size([48, 48, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
        size mismatch for stg1_high_band_net.enc3.conv2.conv.1.weight: copying a param with shape torch.Size([48]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg1_high_band_net.enc3.conv2.conv.1.bias: copying a param with shape torch.Size([48]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg1_high_band_net.enc3.conv2.conv.1.running_mean: copying a param with shape torch.Size([48]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg1_high_band_net.enc3.conv2.conv.1.running_var: copying a param with shape torch.Size([48]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg1_high_band_net.enc4.conv1.conv.0.weight: copying a param with shape torch.Size([72, 48, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 128, 3, 3]).
        size mismatch for stg1_high_band_net.enc4.conv1.conv.1.weight: copying a param with shape torch.Size([72]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.enc4.conv1.conv.1.bias: copying a param with shape torch.Size([72]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.enc4.conv1.conv.1.running_mean: copying a param with shape torch.Size([72]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.enc4.conv1.conv.1.running_var: copying a param with shape torch.Size([72]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.enc4.conv2.conv.0.weight: copying a param with shape torch.Size([72, 72, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
        size mismatch for stg1_high_band_net.enc4.conv2.conv.1.weight: copying a param with shape torch.Size([72]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.enc4.conv2.conv.1.bias: copying a param with shape torch.Size([72]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.enc4.conv2.conv.1.running_mean: copying a param with shape torch.Size([72]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.enc4.conv2.conv.1.running_var: copying a param with shape torch.Size([72]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.aspp.conv1.1.conv.0.weight: copying a param with shape torch.Size([96, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
        size mismatch for stg1_high_band_net.aspp.conv1.1.conv.1.weight: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.aspp.conv1.1.conv.1.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.aspp.conv1.1.conv.1.running_mean: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.aspp.conv1.1.conv.1.running_var: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.aspp.conv2.conv.0.weight: copying a param with shape torch.Size([96, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
        size mismatch for stg1_high_band_net.aspp.conv2.conv.1.weight: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.aspp.conv2.conv.1.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.aspp.conv2.conv.1.running_mean: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.aspp.conv2.conv.1.running_var: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg1_high_band_net.aspp.conv3.conv.0.weight: copying a param with shape torch.Size([96, 96, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1, 3, 3]).
        size mismatch for stg1_high_band_net.aspp.conv3.conv.1.weight: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
        size mismatch for stg1_high_band_net.aspp.conv4.conv.0.weight: copying a param with shape torch.Size([96, 96, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1, 3, 3]).
        size mismatch for stg1_high_band_net.aspp.conv4.conv.1.weight: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
        size mismatch for stg1_high_band_net.aspp.conv5.conv.0.weight: copying a param with shape torch.Size([96, 96, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1, 3, 3]).
        size mismatch for stg1_high_band_net.aspp.conv5.conv.1.weight: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
        size mismatch for stg3_full_band_net.enc2.conv1.conv.0.weight: copying a param with shape torch.Size([96, 48, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 64, 3, 3]).
        size mismatch for stg3_full_band_net.enc2.conv1.conv.1.weight: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg3_full_band_net.enc2.conv1.conv.1.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg3_full_band_net.enc2.conv1.conv.1.running_mean: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg3_full_band_net.enc2.conv1.conv.1.running_var: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg3_full_band_net.enc2.conv2.conv.0.weight: copying a param with shape torch.Size([96, 96, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
        size mismatch for stg3_full_band_net.enc2.conv2.conv.1.weight: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg3_full_band_net.enc2.conv2.conv.1.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg3_full_band_net.enc2.conv2.conv.1.running_mean: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg3_full_band_net.enc2.conv2.conv.1.running_var: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for stg3_full_band_net.enc3.conv1.conv.0.weight: copying a param with shape torch.Size([192, 96, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 128, 3, 3]).
        size mismatch for stg3_full_band_net.enc3.conv1.conv.1.weight: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg3_full_band_net.enc3.conv1.conv.1.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg3_full_band_net.enc3.conv1.conv.1.running_mean: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg3_full_band_net.enc3.conv1.conv.1.running_var: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg3_full_band_net.enc3.conv2.conv.0.weight: copying a param with shape torch.Size([192, 192, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
        size mismatch for stg3_full_band_net.enc3.conv2.conv.1.weight: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg3_full_band_net.enc3.conv2.conv.1.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg3_full_band_net.enc3.conv2.conv.1.running_mean: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg3_full_band_net.enc3.conv2.conv.1.running_var: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for stg3_full_band_net.enc4.conv1.conv.0.weight: copying a param with shape torch.Size([288, 192, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 256, 3, 3]).
        size mismatch for stg3_full_band_net.enc4.conv1.conv.1.weight: copying a param with shape torch.Size([288]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.enc4.conv1.conv.1.bias: copying a param with shape torch.Size([288]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.enc4.conv1.conv.1.running_mean: copying a param with shape torch.Size([288]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.enc4.conv1.conv.1.running_var: copying a param with shape torch.Size([288]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.enc4.conv2.conv.0.weight: copying a param with shape torch.Size([288, 288, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3]).
        size mismatch for stg3_full_band_net.enc4.conv2.conv.1.weight: copying a param with shape torch.Size([288]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.enc4.conv2.conv.1.bias: copying a param with shape torch.Size([288]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.enc4.conv2.conv.1.running_mean: copying a param with shape torch.Size([288]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.enc4.conv2.conv.1.running_var: copying a param with shape torch.Size([288]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.aspp.conv1.1.conv.0.weight: copying a param with shape torch.Size([384, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
        size mismatch for stg3_full_band_net.aspp.conv1.1.conv.1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.aspp.conv1.1.conv.1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.aspp.conv1.1.conv.1.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.aspp.conv1.1.conv.1.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.aspp.conv2.conv.0.weight: copying a param with shape torch.Size([384, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
        size mismatch for stg3_full_band_net.aspp.conv2.conv.1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.aspp.conv2.conv.1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.aspp.conv2.conv.1.running_mean: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.aspp.conv2.conv.1.running_var: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
        size mismatch for stg3_full_band_net.aspp.conv3.conv.0.weight: copying a param with shape torch.Size([384, 384, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1, 3, 3]).
        size mismatch for stg3_full_band_net.aspp.conv3.conv.1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
        size mismatch for stg3_full_band_net.aspp.conv4.conv.0.weight: copying a param with shape torch.Size([384, 384, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1, 3, 3]).
        size mismatch for stg3_full_band_net.aspp.conv4.conv.1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
        size mismatch for stg3_full_band_net.aspp.conv5.conv.0.weight: copying a param with shape torch.Size([384, 384, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1, 3, 3]).
        size mismatch for stg3_full_band_net.aspp.conv5.conv.1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
        size mismatch for out.weight: copying a param with shape torch.Size([2, 48, 1, 1]) from checkpoint, the shape in current model is torch.Size([2, 64, 1, 1]).

Here is my code:

import os
import pydub

cwd = os.getcwd()
ffmpeg_exec = cwd + "\\ffmpeg.exe" # or any other path to ffmpeg, as long as it is absolute and not relative.

pydub.AudioSegment.converter = ffmpeg_exec

from dotenv import load_dotenv

from rvc.modules.uvr5.modules import UVR

load_dotenv(".env")

print("Loading UVR")
uvr = UVR()

print("Extracting vocals...")

os.chdir(cwd + "\\Lib\\site-packages")

# downloaded model from:
# https://github.com/TRvlvr/model_repo/releases/

generator = uvr.uvr_wrapper(
    model_name="2_HP-UVR.pth",
    audio_path=cwd + "\\audio.wav",
    save_vocal_path=cwd + "\\vocal",
    save_ins_path=cwd + "\\inst",
    agg=5,
    export_format="wav",
    temp_path=cwd + "\\tmp")

for item in generator:
    print(item)

voc_file = cwd + "\\inst\\vocal_audio.wav_5.wav"

generator = uvr.uvr_wrapper(
    model_name="5_HP-Karaoke-UVR.pth",
    audio_path=voc_file,
    save_vocal_path=cwd + "\\main",
    save_ins_path=cwd + "\\other",
    agg=5,
    export_format="wav",
    temp_path=cwd + "\\tmp")

for item in generator:
    print(item)

main_voc_file = cwd + "\\other\\vocal_vocal_audio.wav_5.wav_5.wav"

generator = uvr.uvr_wrapper(
    model_name="UVR-De-Echo-Aggressive.pth",
    audio_path=main_voc_file,
    save_vocal_path=cwd + "\\noecho",
    save_ins_path=cwd + "\\echo",
    agg=5,
    export_format="wav",
    temp_path=cwd + "\\tmp")

for item in generator:
    print(item)

Note that both 2_HP-UVR.pth and 5-HP-Karaoke-UVR.pth work just fine.

I also ended up trying out the VR-DeEchoNormal.pth from my own RVC WebUI install and ended up with another error:

Traceback (most recent call last):
  File "C:\Users\jeje9\Desktop\rvc_test\rvc_test.py", line 62, in <module>
    for item in generator:
  File "C:\Users\jeje9\Desktop\rvc_test\lib\site-packages\rvc\modules\uvr5\modules.py", line 85, in uvr_wrapper
    pre_fun._path_audio_(
  File "C:\Users\jeje9\Desktop\rvc_test\lib\site-packages\rvc\modules\uvr5\vr.py", line 239, in _path_audio_
    ) = librosa.core.load(
TypeError: load() takes 1 positional argument but 3 positional arguments (and 2 keyword-only arguments) were given

code:

generator = uvr.uvr_wrapper(
    model_name="VR-DeEchoNormal.pth",
    audio_path=main_voc_file,
    save_vocal_path=cwd + "\\noecho",
    save_ins_path=cwd + "\\echo",
    agg=5,
    export_format="wav",
    temp_path=cwd + "\\tmp")

for item in generator:
    print(item)

[wAIfu-DEV]

I think the first error could be explained by line 48 in rvc\modules\uvr5\modules.py Since the model I used was called De-Echo instead of DeEcho, the check failed. Not entirely sure what is the cause of the second error though.

[wAIfu-DEV]

Alright, so I figured out the second error, it seems like the function definition for librosa.core.load(), as well as librosa.core.resample() have been changed in a recent version, meaning that the current version of librosa (which is installed by default by pip with this repo) will not work.

To fix the issue, the function calls at line 239 and 249 in rvc\modules\uvr5\vr.py must be changed to this:

[Tps-F]

Thank you! The bugs was fixed on Commit 0c3c512396825ac418618f5235e4863dbba41e92