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https://github.com/Anjok07/ultimatevocalremovergui.git
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@ -1,36 +1,15 @@
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import json
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import pathlib
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default_param = {}
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default_param['bins'] = 768
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default_param['unstable_bins'] = 9 # training only
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default_param['reduction_bins'] = 762 # training only
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default_param['bins'] = -1
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default_param['unstable_bins'] = -1 # training only
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default_param['stable_bins'] = -1 # training only
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default_param['sr'] = 44100
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default_param['pre_filter_start'] = 757
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default_param['pre_filter_stop'] = 768
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default_param['pre_filter_start'] = -1
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default_param['pre_filter_stop'] = -1
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default_param['band'] = {}
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default_param['band'][1] = {
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'sr': 11025,
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'hl': 128,
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'n_fft': 960,
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'crop_start': 0,
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'crop_stop': 245,
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'lpf_start': 61, # inference only
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'res_type': 'polyphase'
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}
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default_param['band'][2] = {
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'sr': 44100,
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'hl': 512,
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'n_fft': 1536,
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'crop_start': 24,
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'crop_stop': 547,
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'hpf_start': 81, # inference only
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'res_type': 'sinc_best'
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}
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N_BINS = 'n_bins'
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def int_keys(d):
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r = {}
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@ -40,20 +19,14 @@ def int_keys(d):
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r[k] = v
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return r
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class ModelParameters(object):
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def __init__(self, config_path=''):
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if '.pth' == pathlib.Path(config_path).suffix:
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import zipfile
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with zipfile.ZipFile(config_path, 'r') as zip:
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self.param = json.loads(zip.read('param.json'), object_pairs_hook=int_keys)
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elif '.json' == pathlib.Path(config_path).suffix:
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with open(config_path, 'r') as f:
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self.param = json.loads(f.read(), object_pairs_hook=int_keys)
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else:
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self.param = default_param
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for k in ['mid_side', 'mid_side_b', 'mid_side_b2', 'stereo_w', 'stereo_n', 'reverse']:
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if not k in self.param:
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self.param[k] = False
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if N_BINS in self.param:
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self.param['bins'] = self.param[N_BINS]
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55
lib_v5/vr_network/modelparams/4band_v3_sn.json
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55
lib_v5/vr_network/modelparams/4band_v3_sn.json
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@ -0,0 +1,55 @@
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{
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"n_bins": 672,
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"unstable_bins": 8,
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"stable_bins": 530,
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"band": {
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"1": {
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"sr": 7350,
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"hl": 80,
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"n_fft": 640,
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"crop_start": 0,
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"crop_stop": 85,
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"lpf_start": 25,
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"lpf_stop": 53,
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"res_type": "polyphase"
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},
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"2": {
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"sr": 7350,
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"hl": 80,
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"n_fft": 320,
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"crop_start": 4,
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"crop_stop": 87,
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"hpf_start": 25,
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"hpf_stop": 12,
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"lpf_start": 31,
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"lpf_stop": 62,
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"res_type": "polyphase"
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},
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"3": {
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"sr": 14700,
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"hl": 160,
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"n_fft": 512,
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"crop_start": 17,
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"crop_stop": 216,
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"hpf_start": 48,
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"hpf_stop": 24,
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"lpf_start": 139,
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"lpf_stop": 210,
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"res_type": "polyphase"
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},
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"4": {
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"sr": 44100,
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"hl": 480,
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"n_fft": 960,
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"crop_start": 78,
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"crop_stop": 383,
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"hpf_start": 130,
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"hpf_stop": 86,
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"convert_channels": "stereo_n",
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"res_type": "kaiser_fast"
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}
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},
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"sr": 44100,
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"pre_filter_start": 668,
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"pre_filter_stop": 672
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}
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@ -40,20 +40,20 @@ class BaseNet(nn.Module):
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class CascadedNet(nn.Module):
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def __init__(self, n_fft, nn_arch_size, nout=32, nout_lstm=128):
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def __init__(self, n_fft, nn_arch_size=51000, nout=32, nout_lstm=128):
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super(CascadedNet, self).__init__()
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self.max_bin = n_fft // 2
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self.output_bin = n_fft // 2 + 1
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self.nin_lstm = self.max_bin // 2
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self.offset = 64
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nout = 64 if nn_arch_size == 218409 else nout
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#print(nout, nout_lstm, n_fft)
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self.stg1_low_band_net = nn.Sequential(
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BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
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layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0)
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)
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self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2)
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self.stg2_low_band_net = nn.Sequential(
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