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2
UVR.py
2
UVR.py
@ -599,7 +599,7 @@ class MainWindow(TkinterDnD.Tk):
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self.options_mdxnetModel_Optionmenu = ttk.OptionMenu(self.options_Frame,
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self.mdxnetModel_var,
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None, 'UVR-MDX-NET 1', 'UVR-MDX-NET 2', 'UVR-MDX-NET 3', 'UVR-MDX-NET Karaoke')
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None, 'UVR-MDX-NET 1', 'UVR-MDX-NET 2', 'UVR-MDX-NET 3', 'UVR-MDX-NET Karaoke')#, 'UVR-MDX-NET Full-B')
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# Ensemble Mode
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self.options_ensChoose_Label = tk.Label(master=self.options_Frame,
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text='Choose Ensemble', anchor=tk.CENTER,
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@ -65,7 +65,7 @@ class Predictor():
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self.onnx_models = {}
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c = 0
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self.models = get_models('tdf_extra', load=False, device=cpu, stems='vocals')
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self.models = get_models('tdf_extra', load=False, device=cpu, stems=modeltype)
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widget_text.write(base_text + 'Loading ONNX model... ')
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update_progress(**progress_kwargs,
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step=0.1)
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@ -701,6 +701,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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global default_noisereduc_s
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global _basename
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global _mixture
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global modeltype
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global progress_kwargs
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global base_text
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global model_set
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@ -743,15 +744,24 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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if data['mdxnetModel'] == 'UVR-MDX-NET 1':
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model_set = 'UVR_MDXNET_9703.onnx'
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model_set_name = 'UVR_MDXNET_9703'
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modeltype = 'vocals-one'
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if data['mdxnetModel'] == 'UVR-MDX-NET 2':
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model_set = 'UVR_MDXNET_9682.onnx'
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model_set_name = 'UVR_MDXNET_9682'
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modeltype = 'vocals-one'
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if data['mdxnetModel'] == 'UVR-MDX-NET 3':
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model_set = 'UVR_MDXNET_9662.onnx'
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model_set_name = 'UVR_MDXNET_9662'
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modeltype = 'vocals-one'
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if data['mdxnetModel'] == 'UVR-MDX-NET Karaoke':
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model_set = 'UVR_MDXNET_KARA.onnx'
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model_set_name = 'UVR_MDXNET_Karaoke'
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modeltype = 'vocals-one'
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if data['mdxnetModel'] == 'UVR-MDX-NET Full-B':
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model_set = 'UVR_MDXNET_FULL.onnx'
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model_set_name = 'UVR_MDXNET_FULL'
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modeltype = 'vocals-two'
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stime = time.perf_counter()
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progress_var.set(0)
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@ -71,7 +71,7 @@ class Predictor():
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self.onnx_models = {}
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c = 0
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self.models = get_models('tdf_extra', load=False, device=cpu, stems='vocals')
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self.models = get_models('tdf_extra', load=False, device=cpu, stems=modeltype)
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widget_text.write(base_text + 'Loading ONNX model... ')
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update_progress(**progress_kwargs,
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step=0.1)
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@ -90,6 +90,7 @@ class Predictor():
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print(run_type)
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print(str(device))
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print('model_set: ', model_set)
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self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', model_set), providers=run_type)
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widget_text.write('Done!\n')
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@ -609,12 +610,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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global base_name
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global progress_kwargs
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global base_text
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global modeltype
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global model_set
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global model_set_name
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global ModelName_2
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model_set = 'UVR_MDXNET_9703.onnx'
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model_set_name = 'UVR_MDXNET_9703'
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# Update default settings
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default_chunks = data['chunks']
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@ -1191,7 +1190,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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vr_ensem_mdx_c_name = 'pass'
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else:
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vr_ensem_mdx_c_name = data['vr_ensem_mdx_c']
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vr_ensem_mdx_c = f'models/Main_Models/{vr_ensem_mdx_c_name}.pth'
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vr_ensem_mdx_c = f'models/Main_Models/{vr_ensem_mdx_c_name}.pth'
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#MDX-Net Model
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@ -1202,7 +1201,8 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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if data['mdx_ensem'] == 'UVR-MDX-NET 3':
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mdx_ensem = 'UVR_MDXNET_9662'
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if data['mdx_ensem'] == 'UVR-MDX-NET Karaoke':
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mdx_ensem = 'UVR_MDXNET_Karaoke'
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mdx_ensem = 'UVR_MDXNET_KARA'
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#MDX-Net Model 2
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@ -1925,6 +1925,23 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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else:
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text_widget.write('Ensemble Mode - Running Model - ' + mdx_name + '\n\n')
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if mdx_name == 'UVR_MDXNET_9703':
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mdx_ensem_b = 'UVR_MDXNET_9703'
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model_set = 'UVR_MDXNET_9703.onnx'
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model_set_name = 'UVR_MDXNET_9703'
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modeltype = 'vocals-one'
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if mdx_name == 'UVR_MDXNET_9682':
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model_set = 'UVR_MDXNET_9682.onnx'
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model_set_name = 'UVR_MDXNET_9682'
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modeltype = 'vocals-one'
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if mdx_name == 'UVR_MDXNET_9662':
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model_set = 'UVR_MDXNET_9662.onnx'
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model_set_name = 'UVR_MDXNET_9662'
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modeltype = 'vocals-one'
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if mdx_name == 'UVR_MDXNET_Karaoke':
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model_set = 'UVR_MDXNET_KARA.onnx'
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model_set_name = 'UVR_MDXNET_Karaoke'
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modeltype = 'vocals-one'
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update_progress(**progress_kwargs,
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step=0)
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@ -2116,7 +2133,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
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text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n')
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text_widget.write(base_text + 'Moving on... ')
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else:
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text_widget.write(base_text + 'Failed to save output(s) as Flac(s).\n')
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text_widget.write('\n' + base_text + 'Failed to save output(s) as Flac(s).\n')
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text_widget.write(base_text + 'Please check error log.\n')
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text_widget.write(base_text + 'Moving on... ')
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try:
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171
models.py
171
models.py
@ -5,139 +5,27 @@ import numpy as np
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import librosa
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dim_c = 4
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k = 3
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model_path = 'model'
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n_fft_scale = {'bass': 8, 'drums':2, 'other':4, 'vocals':3, '*':2}
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class Conv_TDF(nn.Module):
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def __init__(self, c, l, f, k, bn, bias=True):
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super(Conv_TDF, self).__init__()
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self.use_tdf = bn is not None
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self.H = nn.ModuleList()
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for i in range(l):
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self.H.append(
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nn.Sequential(
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nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k//2),
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nn.BatchNorm2d(c),
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nn.ReLU(),
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)
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)
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if self.use_tdf:
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if bn==0:
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self.tdf = nn.Sequential(
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nn.Linear(f,f, bias=bias),
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nn.BatchNorm2d(c),
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nn.ReLU()
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)
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else:
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self.tdf = nn.Sequential(
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nn.Linear(f,f//bn, bias=bias),
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nn.BatchNorm2d(c),
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nn.ReLU(),
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nn.Linear(f//bn,f, bias=bias),
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nn.BatchNorm2d(c),
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nn.ReLU()
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)
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def forward(self, x):
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for h in self.H:
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x = h(x)
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return x + self.tdf(x) if self.use_tdf else x
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n_fft_scale = {'vocals-one':6144, 'vocals-two':7680,'*':2}
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class Conv_TDF_net_trim(nn.Module):
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def __init__(self, device, load, model_name, target_name, lr, epoch,
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L, l, g, dim_f, dim_t, k=3, hop=1024, bn=None, bias=True):
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def __init__(self, device, load, model_name, target_name,
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L, dim_f, dim_t, hop=1024):
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super(Conv_TDF_net_trim, self).__init__()
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self.dim_f, self.dim_t = 2**dim_f, 2**dim_t
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self.n_fft = self.dim_f * n_fft_scale[target_name]
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self.dim_f, self.dim_t = dim_f, 2**dim_t
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self.n_fft = n_fft_scale[target_name]
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self.hop = hop
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self.n_bins = self.n_fft//2+1
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self.chunk_size = hop * (self.dim_t-1)
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self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
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self.target_name = target_name
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self.blender = 'blender' in model_name
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print(target_name)
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out_c = dim_c*4 if target_name=='*' else dim_c
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in_c = dim_c*2 if self.blender else dim_c
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#out_c = dim_c*2 if self.blender else dim_c
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self.freq_pad = torch.zeros([1, out_c, self.n_bins-self.dim_f, self.dim_t]).to(device)
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self.n = L//2
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if load:
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self.first_conv = nn.Sequential(
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nn.Conv2d(in_channels=in_c, out_channels=g, kernel_size=1, stride=1),
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nn.BatchNorm2d(g),
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nn.ReLU(),
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)
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f = self.dim_f
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c = g
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self.ds_dense = nn.ModuleList()
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self.ds = nn.ModuleList()
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for i in range(self.n):
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self.ds_dense.append(Conv_TDF(c, l, f, k, bn, bias=bias))
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scale = (2,2)
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self.ds.append(
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nn.Sequential(
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nn.Conv2d(in_channels=c, out_channels=c+g, kernel_size=scale, stride=scale),
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nn.BatchNorm2d(c+g),
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nn.ReLU()
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)
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)
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f = f//2
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c += g
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self.mid_dense = Conv_TDF(c, l, f, k, bn, bias=bias)
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#if bn is None and mid_tdf:
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# self.mid_dense = Conv_TDF(c, l, f, k, bn=0, bias=False)
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self.us_dense = nn.ModuleList()
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self.us = nn.ModuleList()
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for i in range(self.n):
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scale = (2,2)
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self.us.append(
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nn.Sequential(
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nn.ConvTranspose2d(in_channels=c, out_channels=c-g, kernel_size=scale, stride=scale),
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nn.BatchNorm2d(c-g),
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nn.ReLU()
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)
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)
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f = f*2
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c -= g
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self.us_dense.append(Conv_TDF(c, l, f, k, bn, bias=bias))
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self.final_conv = nn.Sequential(
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nn.Conv2d(in_channels=c, out_channels=out_c, kernel_size=1, stride=1),
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)
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model_cfg = f'L{L}l{l}g{g}'
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model_cfg += ', ' if (bn is None or bn==0) else f'bn{bn}, '
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stft_cfg = f'f{dim_f}t{dim_t}, '
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model_name = model_name[:model_name.index('(')+1] + model_cfg + stft_cfg + model_name[model_name.index('(')+1:]
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try:
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self.load_state_dict(
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torch.load('{0}/{1}/{2}_lr{3}_e{4:05}.ckpt'.format(model_path, model_name, target_name, lr, epoch), map_location=device)
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)
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print(f'Loading model ({target_name})')
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except FileNotFoundError:
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print(f'Random init ({target_name})')
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print(self.n_bins)
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def stft(self, x):
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x = x.reshape([-1, self.chunk_size])
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@ -154,32 +42,6 @@ class Conv_TDF_net_trim(nn.Module):
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x = x.permute([0,2,3,1])
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x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
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return x.reshape([-1,c,self.chunk_size])
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def forward(self, x):
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x = self.first_conv(x)
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x = x.transpose(-1,-2)
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ds_outputs = []
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for i in range(self.n):
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x = self.ds_dense[i](x)
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ds_outputs.append(x)
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x = self.ds[i](x)
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x = self.mid_dense(x)
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for i in range(self.n):
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x = self.us[i](x)
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x *= ds_outputs[-i-1]
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x = self.us_dense[i](x)
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x = x.transpose(-1,-2)
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x = self.final_conv(x)
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return x
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def stft(wave, nfft, hl):
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wave_left = np.asfortranarray(wave[0])
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@ -193,7 +55,6 @@ def stft(wave, nfft, hl):
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def istft(spec, hl):
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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wave_left = librosa.istft(spec_left, hop_length=hl)
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wave_right = librosa.istft(spec_right, hop_length=hl)
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wave = np.asfortranarray([wave_left, wave_right])
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@ -220,18 +81,24 @@ def spec_effects(wave, algorithm='default', value=None):
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return wave
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def get_models(name, device, load=True, stems='vocals'):
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def get_models(name, device, load=True, stems='vocals-onevocals-two'):
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if name=='tdf_extra':
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models = []
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if 'vocals' in stems:
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if 'vocals-one' in stems:
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models.append(
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Conv_TDF_net_trim(
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device=device, load=load,
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model_name='Conv-TDF', target_name='vocals',
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lr=0.0001, epoch=0,
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L=11, l=3, g=32, bn=8, bias=False,
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dim_f=11, dim_t=8
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model_name='Conv-TDF', target_name='vocals-one',
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L=11, dim_f=2048, dim_t=8
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)
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)
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if 'vocals-two' in stems:
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models.append(
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Conv_TDF_net_trim(
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device=device, load=load,
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model_name='Conv-TDF', target_name='vocals-two',
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L=11, dim_f=3072, dim_t=8
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)
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)
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return models
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