vr_model: UVR-BVE-4B_SN-44100-1 ERROR LOG

[VirtualFire]

Last Error Received:

Process: VR Architecture

If this error persists, please contact the developers with the error details.

Raw Error Details:

RuntimeError: "Error(s) in loading state_dict for CascadedNet:
size mismatch for stg1_low_band_net.0.lstm_dec2.lstm.weight_ih_l0: copying a param with shape torch.Size([256, 168]) from checkpoint, the shape in current model is torch.Size([256, 320]).
size mismatch for stg1_low_band_net.0.lstm_dec2.lstm.weight_ih_l0_reverse: copying a param with shape torch.Size([256, 168]) from checkpoint, the shape in current model is torch.Size([256, 320]).
size mismatch for stg1_low_band_net.0.lstm_dec2.dense.0.weight: copying a param with shape torch.Size([168, 128]) from checkpoint, the shape in current model is torch.Size([320, 128]).
size mismatch for stg1_low_band_net.0.lstm_dec2.dense.0.bias: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg1_low_band_net.0.lstm_dec2.dense.1.weight: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg1_low_band_net.0.lstm_dec2.dense.1.bias: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg1_low_band_net.0.lstm_dec2.dense.1.running_mean: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg1_low_band_net.0.lstm_dec2.dense.1.running_var: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg1_high_band_net.lstm_dec2.lstm.weight_ih_l0: copying a param with shape torch.Size([128, 168]) from checkpoint, the shape in current model is torch.Size([128, 320]).
size mismatch for stg1_high_band_net.lstm_dec2.lstm.weight_ih_l0_reverse: copying a param with shape torch.Size([128, 168]) from checkpoint, the shape in current model is torch.Size([128, 320]).
size mismatch for stg1_high_band_net.lstm_dec2.dense.0.weight: copying a param with shape torch.Size([168, 64]) from checkpoint, the shape in current model is torch.Size([320, 64]).
size mismatch for stg1_high_band_net.lstm_dec2.dense.0.bias: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg1_high_band_net.lstm_dec2.dense.1.weight: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg1_high_band_net.lstm_dec2.dense.1.bias: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg1_high_band_net.lstm_dec2.dense.1.running_mean: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg1_high_band_net.lstm_dec2.dense.1.running_var: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg2_low_band_net.0.lstm_dec2.lstm.weight_ih_l0: copying a param with shape torch.Size([256, 168]) from checkpoint, the shape in current model is torch.Size([256, 320]).
size mismatch for stg2_low_band_net.0.lstm_dec2.lstm.weight_ih_l0_reverse: copying a param with shape torch.Size([256, 168]) from checkpoint, the shape in current model is torch.Size([256, 320]).
size mismatch for stg2_low_band_net.0.lstm_dec2.dense.0.weight: copying a param with shape torch.Size([168, 128]) from checkpoint, the shape in current model is torch.Size([320, 128]).
size mismatch for stg2_low_band_net.0.lstm_dec2.dense.0.bias: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg2_low_band_net.0.lstm_dec2.dense.1.weight: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg2_low_band_net.0.lstm_dec2.dense.1.bias: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg2_low_band_net.0.lstm_dec2.dense.1.running_mean: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg2_low_band_net.0.lstm_dec2.dense.1.running_var: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg2_high_band_net.lstm_dec2.lstm.weight_ih_l0: copying a param with shape torch.Size([128, 168]) from checkpoint, the shape in current model is torch.Size([128, 320]).
size mismatch for stg2_high_band_net.lstm_dec2.lstm.weight_ih_l0_reverse: copying a param with shape torch.Size([128, 168]) from checkpoint, the shape in current model is torch.Size([128, 320]).
size mismatch for stg2_high_band_net.lstm_dec2.dense.0.weight: copying a param with shape torch.Size([168, 64]) from checkpoint, the shape in current model is torch.Size([320, 64]).
size mismatch for stg2_high_band_net.lstm_dec2.dense.0.bias: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg2_high_band_net.lstm_dec2.dense.1.weight: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg2_high_band_net.lstm_dec2.dense.1.bias: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg2_high_band_net.lstm_dec2.dense.1.running_mean: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg2_high_band_net.lstm_dec2.dense.1.running_var: copying a param with shape torch.Size([168]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for stg3_full_band_net.lstm_dec2.lstm.weight_ih_l0: copying a param with shape torch.Size([256, 336]) from checkpoint, the shape in current model is torch.Size([256, 640]).
size mismatch for stg3_full_band_net.lstm_dec2.lstm.weight_ih_l0_reverse: copying a param with shape torch.Size([256, 336]) from checkpoint, the shape in current model is torch.Size([256, 640]).
size mismatch for stg3_full_band_net.lstm_dec2.dense.0.weight: copying a param with shape torch.Size([336, 128]) from checkpoint, the shape in current model is torch.Size([640, 128]).
size mismatch for stg3_full_band_net.lstm_dec2.dense.0.bias: copying a param with shape torch.Size([336]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for stg3_full_band_net.lstm_dec2.dense.1.weight: copying a param with shape torch.Size([336]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for stg3_full_band_net.lstm_dec2.dense.1.bias: copying a param with shape torch.Size([336]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for stg3_full_band_net.lstm_dec2.dense.1.running_mean: copying a param with shape torch.Size([336]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for stg3_full_band_net.lstm_dec2.dense.1.running_var: copying a param with shape torch.Size([336]) from checkpoint, the shape in current model is torch.Size([640])."
Traceback Error: "
File "UVR.py", line 6565, in process_start
File "separate.py", line 1029, in seperate
File "torch\nn\modules\module.py", line 1667, in load_state_dict
"

Error Time Stamp [2023-10-03 18:03:27]

Full Application Settings:

vr_model: UVR-BVE-4B_SN-44100-1
aggression_setting: 5
window_size: 512
mdx_segment_size: 256
batch_size: Default
crop_size: 256
is_tta: False
is_output_image: False
is_post_process: False
is_high_end_process: False
post_process_threshold: 0.2
vr_voc_inst_secondary_model: No Model Selected
vr_other_secondary_model: No Model Selected
vr_bass_secondary_model: No Model Selected
vr_drums_secondary_model: No Model Selected
vr_is_secondary_model_activate: False
vr_voc_inst_secondary_model_scale: 0.9
vr_other_secondary_model_scale: 0.7
vr_bass_secondary_model_scale: 0.5
vr_drums_secondary_model_scale: 0.5
demucs_model: v4 | htdemucs
segment: Default
overlap: 0.25
overlap_mdx: Default
overlap_mdx23: 8
shifts: 2
chunks_demucs: Auto
margin_demucs: 44100
is_chunk_demucs: False
is_chunk_mdxnet: False
is_primary_stem_only_Demucs: False
is_secondary_stem_only_Demucs: False
is_split_mode: True
is_demucs_combine_stems: True
is_mdx23_combine_stems: True
demucs_voc_inst_secondary_model: No Model Selected
demucs_other_secondary_model: No Model Selected
demucs_bass_secondary_model: No Model Selected
demucs_drums_secondary_model: No Model Selected
demucs_is_secondary_model_activate: False
demucs_voc_inst_secondary_model_scale: 0.9
demucs_other_secondary_model_scale: 0.7
demucs_bass_secondary_model_scale: 0.5
demucs_drums_secondary_model_scale: 0.5
demucs_pre_proc_model: No Model Selected
is_demucs_pre_proc_model_activate: False
is_demucs_pre_proc_model_inst_mix: False
mdx_net_model: UVR-MDX-NET Karaoke 2
chunks: Auto
margin: 44100
compensate: Auto
denoise_option: None
is_match_frequency_pitch: True
phase_option: Automatic
phase_shifts: None
is_save_align: False
is_match_silence: True
is_spec_match: False
is_mdx_c_seg_def: False
is_invert_spec: False
is_deverb_vocals: False
deverb_vocal_opt: Main Vocals Only
voc_split_save_opt: Lead Only
is_mixer_mode: False
mdx_batch_size: Default
mdx_voc_inst_secondary_model: No Model Selected
mdx_other_secondary_model: No Model Selected
mdx_bass_secondary_model: No Model Selected
mdx_drums_secondary_model: No Model Selected
mdx_is_secondary_model_activate: False
mdx_voc_inst_secondary_model_scale: 0.9
mdx_other_secondary_model_scale: 0.7
mdx_bass_secondary_model_scale: 0.5
mdx_drums_secondary_model_scale: 0.5
is_save_all_outputs_ensemble: True
is_append_ensemble_name: False
chosen_audio_tool: Manual Ensemble
choose_algorithm: Min Spec
time_stretch_rate: 2.0
pitch_rate: 2.0
is_time_correction: True
is_gpu_conversion: True
is_primary_stem_only: False
is_secondary_stem_only: False
is_testing_audio: False
is_auto_update_model_params: True
is_add_model_name: False
is_accept_any_input: False
is_task_complete: False
is_normalization: False
is_wav_ensemble: False
is_create_model_folder: False
mp3_bit_set: 320k
semitone_shift: 0
save_format: WAV
wav_type_set: PCM_16
help_hints_var: True
set_vocal_splitter: No Model Selected
is_set_vocal_splitter: False
is_save_inst_set_vocal_splitter: False
model_sample_mode: False
model_sample_mode_duration: 30
demucs_stems: All Stems
mdx_stems: All Stems

[RoyDubnium]

I had this problem with the UVR-BVE model too. It was fixed when i reinstalled the latest version and redownloaded the model. (Warning: This will cause the program to forget all the models you previously downloaded, and you will have to reinstall them).

[Anjok07]

If you open the application directory, you can copy the models folder to a new directory. Then once the install for the version of complete, you can move the old models back.