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.