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13
UVR.py
13
UVR.py
@ -526,6 +526,7 @@ class ModelData():
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self.mdx_segment_size = int(root.mdx_segment_size_var.get())
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self.get_mdx_model_path()
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self.get_model_hash()
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print(self.model_hash)
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if self.model_hash:
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self.model_hash_dir = os.path.join(MDX_HASH_DIR, f"{self.model_hash}.json")
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if is_change_def:
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@ -554,8 +555,11 @@ class ModelData():
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if self.is_roformer and self.mdx_c_configs.training.target_instrument == VOCAL_STEM and self.is_ensemble_mode:
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self.mdxnet_stem_select = self.ensemble_primary_stem
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if self.is_roformer and self.mdx_c_configs.training.target_instrument == INST_STEM and self.is_ensemble_mode:
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self.mdxnet_stem_select = self.ensemble_primary_stem
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print("self.primary_stem target", self.primary_stem)
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#print("self.primary_stem target", self.primary_stem)
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else:
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# If no specific target_instrument, use all instruments in the training config
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@ -583,10 +587,12 @@ class ModelData():
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self.check_if_karaokee_model()
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print("self.primary_stem final", self.primary_stem)
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#print("self.primary_stem final", self.primary_stem)
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self.secondary_stem = secondary_stem(self.primary_stem)
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else:
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self.model_status = False
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#print("self.mdxnet_stem_select", self.mdxnet_stem_select)
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if self.process_method == DEMUCS_ARCH_TYPE:
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self.is_secondary_model_activated = root.demucs_is_secondary_model_activate_var.get() if not is_secondary_model else False
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@ -1217,9 +1223,6 @@ class ComboBoxEditableMenu(ttk.Combobox):
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self.bind('<FocusIn>', self.focusin)
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self.bind('<FocusOut>', lambda e: self.var_validation(is_focus_only=True))
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self.bind('<MouseWheel>', lambda e: "break")
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#self.bind('<MouseWheel>', lambda e: print("Scrolling"))
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# self.bind('<Shift-MouseWheel>', lambda e: "break")
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# self.bind('<Control-MouseWheel>', lambda e: "break")
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if is_macos:
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self.bind('<Enter>', lambda e:self.button_released())
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@ -1,4 +1,4 @@
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VERSION = 'v5.6.0'
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PATCH = 'UVR_Patch_9_29_23_1_39'
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PATCH_MAC = 'UVR_Patch_9_29_23_1_39'
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PATCH_LINUX = 'UVR_Patch_9_29_23_1_39'
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VERSION = 'v5.6.1'
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PATCH = 'UVR_Patch_11_25_24_1_48_BETA'
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PATCH_MAC = 'UVR_Patch_12_29_22_07_20'
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PATCH_LINUX = 'UVR_Patch_12_29_22_07_20'
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29
separate.py
29
separate.py
@ -134,9 +134,11 @@ class SeperateAttributes:
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self.primary_model_primary_stem = model_data.primary_model_primary_stem
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self.primary_stem_native = model_data.primary_stem_native
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self.primary_stem = model_data.primary_stem #
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print(self.primary_stem)
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self.secondary_stem = model_data.secondary_stem #
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self.is_invert_spec = model_data.is_invert_spec #
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self.is_deverb_vocals = model_data.is_deverb_vocals
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self.is_target_instrument = model_data.is_target_instrument
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self.is_mixer_mode = model_data.is_mixer_mode #
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self.secondary_model_scale = model_data.secondary_model_scale #
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self.is_demucs_pre_proc_model_inst_mix = model_data.is_demucs_pre_proc_model_inst_mix #
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@ -172,7 +174,7 @@ class SeperateAttributes:
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self.is_save_vocal_only = model_data.is_save_vocal_only
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self.device = cpu
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self.run_type = ['CPUExecutionProvider']
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self.is_opencl = False
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self.is_using_opencl = False
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self.device_set = model_data.device_set
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self.is_use_opencl = model_data.is_use_opencl
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#Roformer
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@ -198,6 +200,7 @@ class SeperateAttributes:
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if directml_available and self.is_use_opencl:
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self.device = torch_directml.device() if not device_prefix else f'{device_prefix}:{self.device_set}'
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self.is_other_gpu = True
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self.is_using_opencl = True
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elif cuda_available and not self.is_use_opencl:
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self.device = CUDA_DEVICE if not device_prefix else f'{device_prefix}:{self.device_set}'
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self.run_type = ['CUDAExecutionProvider']
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@ -214,8 +217,9 @@ class SeperateAttributes:
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if self.is_mdx_c:
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if not self.is_4_stem_ensemble:
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self.primary_stem = model_data.ensemble_primary_stem if process_data['is_ensemble_master'] else model_data.primary_stem
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self.secondary_stem = model_data.ensemble_secondary_stem if process_data['is_ensemble_master'] else model_data.secondary_stem
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if not self.is_target_instrument:
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self.primary_stem = model_data.ensemble_primary_stem if process_data['is_ensemble_master'] else model_data.primary_stem
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self.secondary_stem = model_data.ensemble_secondary_stem if process_data['is_ensemble_master'] else model_data.secondary_stem
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else:
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self.dim_f, self.dim_t = model_data.mdx_dim_f_set, 2**model_data.mdx_dim_t_set
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@ -286,13 +290,10 @@ class SeperateAttributes:
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'aggr_correction': self.mp.param.get('aggr_correction')}
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def check_label_secondary_stem_runs(self):
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# For ensemble master that's not a 4-stem ensemble, and not mdx_c
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if self.process_data['is_ensemble_master'] and not self.is_4_stem_ensemble and not self.is_mdx_c:
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if (self.process_data['is_ensemble_master'] and not self.is_4_stem_ensemble and not self.is_mdx_c) or (self.process_data['is_ensemble_master'] and self.is_target_instrument):
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if self.ensemble_primary_stem != self.primary_stem:
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self.is_primary_stem_only, self.is_secondary_stem_only = self.is_secondary_stem_only, self.is_primary_stem_only
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# For secondary models
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self.is_primary_stem_only, self.is_secondary_stem_only = self.is_secondary_stem_only, self.is_primary_stem_only
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if self.is_pre_proc_model or self.is_secondary_model:
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self.is_primary_stem_only = False
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self.is_secondary_stem_only = False
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@ -695,10 +696,14 @@ class SeperateMDXC(SeperateAttributes):
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if stem == VOCAL_STEM and not self.is_sec_bv_rebalance:
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self.process_vocal_split_chain({VOCAL_STEM:stem})
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else:
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print(stem_list)
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if len(stem_list) == 1:
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source_primary = sources
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else:
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source_primary = sources[stem_list[0]] if self.is_multi_stem_ensemble and len(stem_list) == 2 else sources[self.mdxnet_stem_select]
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if self.is_multi_stem_ensemble or len(stem_list) == 2:
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source_primary = sources[stem_list[0]]
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else:
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sources[self.mdxnet_stem_select]
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if self.is_secondary_model_activated and self.secondary_model:
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self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(self.secondary_model,
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@ -748,7 +753,7 @@ class SeperateMDXC(SeperateAttributes):
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return secondary_sources
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def overlap_add(self, result, x, l, j, start, window):
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if self.device == 'mps':
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if self.device == 'mps' or self.is_other_gpu:
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x = x.to(self.device)
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result[..., start:start + l] += x[j][..., :l] * window[..., :l]
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return result
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@ -808,7 +813,7 @@ class SeperateMDXC(SeperateAttributes):
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batch_len = int(mix.shape[1] / step)
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with torch.inference_mode():
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with torch.no_grad() if self.is_using_opencl else torch.inference_mode():
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req_shape = (S, ) + tuple(mix.shape)
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result = torch.zeros(req_shape, dtype=torch.float32, device=device)
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counter = torch.zeros(req_shape, dtype=torch.float32, device=device)
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