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13
UVR.py
13
UVR.py
@ -53,6 +53,9 @@ import sys
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import yaml
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from ml_collections import ConfigDict
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from collections import Counter
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import torch_directml
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is_gpu_available = torch.backends.mps.is_available() if (OPERATING_SYSTEM == 'Darwin') else (torch_directml.is_available() or torch.cuda.is_available())
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# Change the current working directory to the directory
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# this file sits in
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@ -109,6 +112,8 @@ elif OPERATING_SYSTEM=="Windows":
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right_click_button = '<Button-3>'
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application_extension = ".exe"
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clear_gpu_cache = torch.mps.empty_cache if is_macos else torch.cuda.empty_cache
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def right_click_release_linux(window, top_win=None):
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if OPERATING_SYSTEM=="Linux":
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root.bind('<Button-1>', lambda e:window.destroy())
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@ -1437,7 +1442,7 @@ class MainWindow(TkinterDnD.Tk if is_dnd_compatible else tk.Tk):
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self.mdx_hash_MAPPER = load_model_hash_data(MDX_HASH_JSON)
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self.mdx_name_select_MAPPER = load_model_hash_data(MDX_MODEL_NAME_SELECT)
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self.demucs_name_select_MAPPER = load_model_hash_data(DEMUCS_MODEL_NAME_SELECT)
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self.is_gpu_available = torch.cuda.is_available() if not OPERATING_SYSTEM == 'Darwin' else torch.backends.mps.is_available()
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self.is_gpu_available = is_gpu_available
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self.is_process_stopped = False
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self.inputs_from_dir = []
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self.iteration = 0
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@ -5479,7 +5484,7 @@ class MainWindow(TkinterDnD.Tk if is_dnd_compatible else tk.Tk):
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if self.clear_cache_torch:
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#self.set_app_font(is_chosen_font=True)
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torch.cuda.empty_cache()
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clear_gpu_cache()
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self.clear_cache_torch = False
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if self.is_process_stopped:
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@ -5492,7 +5497,7 @@ class MainWindow(TkinterDnD.Tk if is_dnd_compatible else tk.Tk):
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self.conversion_Button_Text_var.set(START_PROCESSING)
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self.conversion_Button.configure(state=tk.NORMAL)
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self.progress_bar_main_var.set(0)
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torch.cuda.empty_cache()
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clear_gpu_cache()
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self.is_process_stopped = False
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if self.is_confirm_error_var.get():
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@ -6589,7 +6594,7 @@ class MainWindow(TkinterDnD.Tk if is_dnd_compatible else tk.Tk):
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if os.path.isfile(audio_file):
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os.remove(audio_file)
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torch.cuda.empty_cache()
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clear_gpu_cache()
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shutil.rmtree(export_path) if is_ensemble and len(os.listdir(export_path)) == 0 else None
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49
separate.py
49
separate.py
@ -31,6 +31,10 @@ import math
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#import random
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from onnx import load
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from onnx2pytorch import ConvertModel
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import torch_directml
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import gc
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cpu = 'cpu'
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if TYPE_CHECKING:
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from UVR import ModelData
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@ -38,6 +42,8 @@ if TYPE_CHECKING:
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warnings.filterwarnings("ignore")
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cpu = torch.device('cpu')
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clear_gpu_cache = torch.mps.empty_cache if is_macos else torch.cuda.empty_cache
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class SeperateAttributes:
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def __init__(self, model_data: ModelData,
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process_data: dict,
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@ -137,8 +143,9 @@ class SeperateAttributes:
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self.stem_path_init = os.path.join(self.export_path, f'{self.audio_file_base}_({self.secondary_stem}).wav')
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self.deverb_vocal_opt = model_data.deverb_vocal_opt
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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.device = cpu
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self.run_type = ['CPUExecutionProvider']
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self.is_opencl = False
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if self.is_inst_only_voc_splitter or self.is_sec_bv_rebalance:
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self.is_primary_stem_only = False
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@ -149,11 +156,11 @@ class SeperateAttributes:
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if self.is_gpu_conversion >= 0:
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if OPERATING_SYSTEM == 'Darwin' and torch.backends.mps.is_available():
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self.device = 'mps'
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self.is_mps = True
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self.device, self.is_mps = 'mps', True
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elif torch_directml.is_available():
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self.device, self.is_mps, self.is_opencl = torch_directml.device(), True, True
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elif torch.cuda.is_available():
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self.device = 'cuda:0'
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self.run_type = ['CUDAExecutionProvider']
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self.device, self.run_type = 'cuda:0', ['CUDAExecutionProvider']
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if model_data.process_method == MDX_ARCH_TYPE:
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self.is_mdx_ckpt = model_data.is_mdx_ckpt
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@ -166,6 +173,7 @@ class SeperateAttributes:
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self.mdx_segment_size = model_data.mdx_segment_size
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if self.is_mdx_c:
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self.device = cpu if self.is_opencl else self.device
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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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@ -192,7 +200,7 @@ class SeperateAttributes:
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self.is_demucs_combine_stems = model_data.is_demucs_combine_stems
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self.demucs_stem_count = model_data.demucs_stem_count
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self.pre_proc_model = model_data.pre_proc_model
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self.device = 'cpu' if self.is_mps and not self.demucs_version == DEMUCS_V4 else self.device
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self.device = cpu if self.is_mps and not self.demucs_version == DEMUCS_V4 else self.device
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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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@ -458,6 +466,7 @@ class SeperateMDX(SeperateAttributes):
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self.running_inference_console_write()
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mix = prepare_mix(self.audio_file)
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source = self.demix(mix)
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if not self.is_vocal_split_model:
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@ -484,8 +493,8 @@ class SeperateMDX(SeperateAttributes):
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self.primary_source = source.T
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self.primary_source_map = self.final_process(primary_stem_path, self.primary_source, self.secondary_source_primary, self.primary_stem, samplerate)
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torch.cuda.empty_cache()
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clear_gpu_cache()
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secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
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@ -499,7 +508,7 @@ class SeperateMDX(SeperateAttributes):
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self.trim = self.n_fft//2
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self.chunk_size = self.hop * (self.mdx_segment_size-1)
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self.gen_size = self.chunk_size-2*self.trim
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self.stft = STFT(self.n_fft, self.hop, self.dim_f)
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self.stft = STFT(self.n_fft, self.hop, self.dim_f, self.device)
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def demix(self, mix, is_match_mix=False):
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self.initialize_model_settings()
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@ -548,13 +557,13 @@ class SeperateMDX(SeperateAttributes):
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mix_part = torch.tensor([mix_part_], dtype=torch.float32).to(self.device)
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mix_waves = mix_part.split(self.mdx_batch_size)
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with torch.no_grad():
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for mix_wave in mix_waves:
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self.running_inference_progress_bar(total_chunks, is_match_mix=is_match_mix)
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tar_waves = self.run_model(mix_wave, is_match_mix=is_match_mix)
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if window is not None:
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tar_waves[..., :chunk_size_actual] *= window
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divider[..., start:end] += window
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@ -562,7 +571,7 @@ class SeperateMDX(SeperateAttributes):
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divider[..., start:end] += 1
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result[..., start:end] += tar_waves[..., :end-start]
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tar_waves = result / divider
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tar_waves_.append(tar_waves)
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@ -685,7 +694,7 @@ class SeperateMDXC(SeperateAttributes):
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self.primary_source_map = self.final_process(primary_stem_path, self.primary_source, self.secondary_source_primary, self.primary_stem, samplerate)
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torch.cuda.empty_cache()
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clear_gpu_cache()
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secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
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self.process_vocal_split_chain(secondary_sources)
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@ -698,9 +707,9 @@ class SeperateMDXC(SeperateAttributes):
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org_mix = mix
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if self.is_pitch_change:
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mix, sr_pitched = spec_utils.change_pitch_semitones(mix, 44100, semitone_shift=-self.semitone_shift)
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model = TFC_TDF_net(self.mdx_c_configs).eval().to(self.device)
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model.load_state_dict(torch.load(self.model_path, map_location=self.device))
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model = TFC_TDF_net(self.mdx_c_configs, device=self.device).eval().to(self.device)
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model.load_state_dict(torch.load(self.model_path, map_location=cpu))
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mix = torch.tensor(mix, dtype=torch.float32)
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try:
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@ -830,7 +839,7 @@ class SeperateDemucs(SeperateAttributes):
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self.write_to_console(DONE, base_text='')
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del self.demucs
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torch.cuda.empty_cache()
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clear_gpu_cache()
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if isinstance(inst_source, np.ndarray):
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source_reshape = spec_utils.reshape_sources(inst_source[self.demucs_source_map[VOCAL_STEM]], source[self.demucs_source_map[VOCAL_STEM]])
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@ -1050,7 +1059,7 @@ class SeperateVR(SeperateAttributes):
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self.secondary_source_map = self.final_process(secondary_stem_path, self.secondary_source, self.secondary_source_secondary, self.secondary_stem, 44100)
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torch.cuda.empty_cache()
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clear_gpu_cache()
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secondary_sources = {**self.primary_source_map, **self.secondary_source_map}
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self.process_vocal_split_chain(secondary_sources)
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@ -1330,8 +1339,10 @@ def vr_denoiser(X, device, hop_length=1024, n_fft=2048, cropsize=256, is_deverbe
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hop_length=1024
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nout, nout_lstm = 16, 128
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device = cpu if not device in ['cuda', 'cpu'] else device
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model = nets_new.CascadedNet(n_fft, nout=nout, nout_lstm=nout_lstm)
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model.load_state_dict(torch.load(model_path, map_location=device))
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model.load_state_dict(torch.load(model_path, map_location=cpu))
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model.to(device)
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if mp is None:
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