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  1. lib_v5/mdxnet.py +140 -0
  2. lib_v5/mixer.ckpt +3 -0
  3. lib_v5/modules.py +74 -0
  4. lib_v5/pyrb.py +92 -0
  5. lib_v5/spec_utils.py +692 -0
  6. lib_v5/vr_network/__init__.py +1 -0
  7. lib_v5/vr_network/__pycache__/__init__.cpython-310.pyc +0 -0
  8. lib_v5/vr_network/__pycache__/layers.cpython-310.pyc +0 -0
  9. lib_v5/vr_network/__pycache__/layers_new.cpython-310.pyc +0 -0
  10. lib_v5/vr_network/__pycache__/model_param_init.cpython-310.pyc +0 -0
  11. lib_v5/vr_network/__pycache__/nets.cpython-310.pyc +0 -0
  12. lib_v5/vr_network/__pycache__/nets_new.cpython-310.pyc +0 -0
  13. lib_v5/vr_network/layers.py +143 -0
  14. lib_v5/vr_network/layers_new.py +126 -0
  15. lib_v5/vr_network/model_param_init.py +59 -0
  16. lib_v5/vr_network/modelparams/1band_sr16000_hl512.json +19 -0
  17. lib_v5/vr_network/modelparams/1band_sr32000_hl512.json +19 -0
  18. lib_v5/vr_network/modelparams/1band_sr33075_hl384.json +19 -0
  19. lib_v5/vr_network/modelparams/1band_sr44100_hl1024.json +19 -0
  20. lib_v5/vr_network/modelparams/1band_sr44100_hl256.json +19 -0
  21. lib_v5/vr_network/modelparams/1band_sr44100_hl512.json +19 -0
  22. lib_v5/vr_network/modelparams/1band_sr44100_hl512_cut.json +19 -0
  23. lib_v5/vr_network/modelparams/1band_sr44100_hl512_nf1024.json +19 -0
  24. lib_v5/vr_network/modelparams/2band_32000.json +30 -0
  25. lib_v5/vr_network/modelparams/2band_44100_lofi.json +30 -0
  26. lib_v5/vr_network/modelparams/2band_48000.json +30 -0
  27. lib_v5/vr_network/modelparams/3band_44100.json +42 -0
  28. lib_v5/vr_network/modelparams/3band_44100_mid.json +43 -0
  29. lib_v5/vr_network/modelparams/3band_44100_msb2.json +43 -0
  30. lib_v5/vr_network/modelparams/4band_44100.json +54 -0
  31. lib_v5/vr_network/modelparams/4band_44100_mid.json +55 -0
  32. lib_v5/vr_network/modelparams/4band_44100_msb.json +55 -0
  33. lib_v5/vr_network/modelparams/4band_44100_msb2.json +55 -0
  34. lib_v5/vr_network/modelparams/4band_44100_reverse.json +55 -0
  35. lib_v5/vr_network/modelparams/4band_44100_sw.json +55 -0
  36. lib_v5/vr_network/modelparams/4band_v2.json +54 -0
  37. lib_v5/vr_network/modelparams/4band_v2_sn.json +55 -0
  38. lib_v5/vr_network/modelparams/4band_v3.json +54 -0
  39. lib_v5/vr_network/modelparams/ensemble.json +43 -0
  40. lib_v5/vr_network/modelparams/lib_v5_vr_network_modelparams_4band_v3_sn.json +55 -0
  41. lib_v5/vr_network/nets.py +166 -0
  42. lib_v5/vr_network/nets_new.py +125 -0
lib_v5/mdxnet.py ADDED
@@ -0,0 +1,140 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from abc import ABCMeta
2
+
3
+ import torch
4
+ import torch.nn as nn
5
+ from pytorch_lightning import LightningModule
6
+ from .modules import TFC_TDF
7
+
8
+ dim_s = 4
9
+
10
+ class AbstractMDXNet(LightningModule):
11
+ __metaclass__ = ABCMeta
12
+
13
+ def __init__(self, target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length, overlap):
14
+ super().__init__()
15
+ self.target_name = target_name
16
+ self.lr = lr
17
+ self.optimizer = optimizer
18
+ self.dim_c = dim_c
19
+ self.dim_f = dim_f
20
+ self.dim_t = dim_t
21
+ self.n_fft = n_fft
22
+ self.n_bins = n_fft // 2 + 1
23
+ self.hop_length = hop_length
24
+ self.window = nn.Parameter(torch.hann_window(window_length=self.n_fft, periodic=True), requires_grad=False)
25
+ self.freq_pad = nn.Parameter(torch.zeros([1, dim_c, self.n_bins - self.dim_f, self.dim_t]), requires_grad=False)
26
+
27
+ def configure_optimizers(self):
28
+ if self.optimizer == 'rmsprop':
29
+ return torch.optim.RMSprop(self.parameters(), self.lr)
30
+
31
+ if self.optimizer == 'adamw':
32
+ return torch.optim.AdamW(self.parameters(), self.lr)
33
+
34
+ class ConvTDFNet(AbstractMDXNet):
35
+ def __init__(self, target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length,
36
+ num_blocks, l, g, k, bn, bias, overlap):
37
+
38
+ super(ConvTDFNet, self).__init__(
39
+ target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length, overlap)
40
+ self.save_hyperparameters()
41
+
42
+ self.num_blocks = num_blocks
43
+ self.l = l
44
+ self.g = g
45
+ self.k = k
46
+ self.bn = bn
47
+ self.bias = bias
48
+
49
+ if optimizer == 'rmsprop':
50
+ norm = nn.BatchNorm2d
51
+
52
+ if optimizer == 'adamw':
53
+ norm = lambda input:nn.GroupNorm(2, input)
54
+
55
+ self.n = num_blocks // 2
56
+ scale = (2, 2)
57
+
58
+ self.first_conv = nn.Sequential(
59
+ nn.Conv2d(in_channels=self.dim_c, out_channels=g, kernel_size=(1, 1)),
60
+ norm(g),
61
+ nn.ReLU(),
62
+ )
63
+
64
+ f = self.dim_f
65
+ c = g
66
+ self.encoding_blocks = nn.ModuleList()
67
+ self.ds = nn.ModuleList()
68
+ for i in range(self.n):
69
+ self.encoding_blocks.append(TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm))
70
+ self.ds.append(
71
+ nn.Sequential(
72
+ nn.Conv2d(in_channels=c, out_channels=c + g, kernel_size=scale, stride=scale),
73
+ norm(c + g),
74
+ nn.ReLU()
75
+ )
76
+ )
77
+ f = f // 2
78
+ c += g
79
+
80
+ self.bottleneck_block = TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm)
81
+
82
+ self.decoding_blocks = nn.ModuleList()
83
+ self.us = nn.ModuleList()
84
+ for i in range(self.n):
85
+ self.us.append(
86
+ nn.Sequential(
87
+ nn.ConvTranspose2d(in_channels=c, out_channels=c - g, kernel_size=scale, stride=scale),
88
+ norm(c - g),
89
+ nn.ReLU()
90
+ )
91
+ )
92
+ f = f * 2
93
+ c -= g
94
+
95
+ self.decoding_blocks.append(TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm))
96
+
97
+ self.final_conv = nn.Sequential(
98
+ nn.Conv2d(in_channels=c, out_channels=self.dim_c, kernel_size=(1, 1)),
99
+ )
100
+
101
+ def forward(self, x):
102
+
103
+ x = self.first_conv(x)
104
+
105
+ x = x.transpose(-1, -2)
106
+
107
+ ds_outputs = []
108
+ for i in range(self.n):
109
+ x = self.encoding_blocks[i](x)
110
+ ds_outputs.append(x)
111
+ x = self.ds[i](x)
112
+
113
+ x = self.bottleneck_block(x)
114
+
115
+ for i in range(self.n):
116
+ x = self.us[i](x)
117
+ x *= ds_outputs[-i - 1]
118
+ x = self.decoding_blocks[i](x)
119
+
120
+ x = x.transpose(-1, -2)
121
+
122
+ x = self.final_conv(x)
123
+
124
+ return x
125
+
126
+ class Mixer(nn.Module):
127
+ def __init__(self, device, mixer_path):
128
+
129
+ super(Mixer, self).__init__()
130
+
131
+ self.linear = nn.Linear((dim_s+1)*2, dim_s*2, bias=False)
132
+
133
+ self.load_state_dict(
134
+ torch.load(mixer_path, map_location=device)
135
+ )
136
+
137
+ def forward(self, x):
138
+ x = x.reshape(1,(dim_s+1)*2,-1).transpose(-1,-2)
139
+ x = self.linear(x)
140
+ return x.transpose(-1,-2).reshape(dim_s,2,-1)
lib_v5/mixer.ckpt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ea781bd52c6a523b825fa6cdbb6189f52e318edd8b17e6fe404f76f7af8caa9c
3
+ size 1208
lib_v5/modules.py ADDED
@@ -0,0 +1,74 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import torch.nn as nn
3
+
4
+
5
+ class TFC(nn.Module):
6
+ def __init__(self, c, l, k, norm):
7
+ super(TFC, self).__init__()
8
+
9
+ self.H = nn.ModuleList()
10
+ for i in range(l):
11
+ self.H.append(
12
+ nn.Sequential(
13
+ nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),
14
+ norm(c),
15
+ nn.ReLU(),
16
+ )
17
+ )
18
+
19
+ def forward(self, x):
20
+ for h in self.H:
21
+ x = h(x)
22
+ return x
23
+
24
+
25
+ class DenseTFC(nn.Module):
26
+ def __init__(self, c, l, k, norm):
27
+ super(DenseTFC, self).__init__()
28
+
29
+ self.conv = nn.ModuleList()
30
+ for i in range(l):
31
+ self.conv.append(
32
+ nn.Sequential(
33
+ nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),
34
+ norm(c),
35
+ nn.ReLU(),
36
+ )
37
+ )
38
+
39
+ def forward(self, x):
40
+ for layer in self.conv[:-1]:
41
+ x = torch.cat([layer(x), x], 1)
42
+ return self.conv[-1](x)
43
+
44
+
45
+ class TFC_TDF(nn.Module):
46
+ def __init__(self, c, l, f, k, bn, dense=False, bias=True, norm=nn.BatchNorm2d):
47
+
48
+ super(TFC_TDF, self).__init__()
49
+
50
+ self.use_tdf = bn is not None
51
+
52
+ self.tfc = DenseTFC(c, l, k, norm) if dense else TFC(c, l, k, norm)
53
+
54
+ if self.use_tdf:
55
+ if bn == 0:
56
+ self.tdf = nn.Sequential(
57
+ nn.Linear(f, f, bias=bias),
58
+ norm(c),
59
+ nn.ReLU()
60
+ )
61
+ else:
62
+ self.tdf = nn.Sequential(
63
+ nn.Linear(f, f // bn, bias=bias),
64
+ norm(c),
65
+ nn.ReLU(),
66
+ nn.Linear(f // bn, f, bias=bias),
67
+ norm(c),
68
+ nn.ReLU()
69
+ )
70
+
71
+ def forward(self, x):
72
+ x = self.tfc(x)
73
+ return x + self.tdf(x) if self.use_tdf else x
74
+
lib_v5/pyrb.py ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import subprocess
3
+ import tempfile
4
+ import six
5
+ import numpy as np
6
+ import soundfile as sf
7
+ import sys
8
+
9
+ if getattr(sys, 'frozen', False):
10
+ BASE_PATH_RUB = sys._MEIPASS
11
+ else:
12
+ BASE_PATH_RUB = os.path.dirname(os.path.abspath(__file__))
13
+
14
+ __all__ = ['time_stretch', 'pitch_shift']
15
+
16
+ __RUBBERBAND_UTIL = os.path.join(BASE_PATH_RUB, 'rubberband')
17
+
18
+ if six.PY2:
19
+ DEVNULL = open(os.devnull, 'w')
20
+ else:
21
+ DEVNULL = subprocess.DEVNULL
22
+
23
+ def __rubberband(y, sr, **kwargs):
24
+
25
+ assert sr > 0
26
+
27
+ # Get the input and output tempfile
28
+ fd, infile = tempfile.mkstemp(suffix='.wav')
29
+ os.close(fd)
30
+ fd, outfile = tempfile.mkstemp(suffix='.wav')
31
+ os.close(fd)
32
+
33
+ # dump the audio
34
+ sf.write(infile, y, sr)
35
+
36
+ try:
37
+ # Execute rubberband
38
+ arguments = [__RUBBERBAND_UTIL, '-q']
39
+
40
+ for key, value in six.iteritems(kwargs):
41
+ arguments.append(str(key))
42
+ arguments.append(str(value))
43
+
44
+ arguments.extend([infile, outfile])
45
+
46
+ subprocess.check_call(arguments, stdout=DEVNULL, stderr=DEVNULL)
47
+
48
+ # Load the processed audio.
49
+ y_out, _ = sf.read(outfile, always_2d=True)
50
+
51
+ # make sure that output dimensions matches input
52
+ if y.ndim == 1:
53
+ y_out = np.squeeze(y_out)
54
+
55
+ except OSError as exc:
56
+ six.raise_from(RuntimeError('Failed to execute rubberband. '
57
+ 'Please verify that rubberband-cli '
58
+ 'is installed.'),
59
+ exc)
60
+
61
+ finally:
62
+ # Remove temp files
63
+ os.unlink(infile)
64
+ os.unlink(outfile)
65
+
66
+ return y_out
67
+
68
+ def time_stretch(y, sr, rate, rbargs=None):
69
+ if rate <= 0:
70
+ raise ValueError('rate must be strictly positive')
71
+
72
+ if rate == 1.0:
73
+ return y
74
+
75
+ if rbargs is None:
76
+ rbargs = dict()
77
+
78
+ rbargs.setdefault('--tempo', rate)
79
+
80
+ return __rubberband(y, sr, **rbargs)
81
+
82
+ def pitch_shift(y, sr, n_steps, rbargs=None):
83
+
84
+ if n_steps == 0:
85
+ return y
86
+
87
+ if rbargs is None:
88
+ rbargs = dict()
89
+
90
+ rbargs.setdefault('--pitch', n_steps)
91
+
92
+ return __rubberband(y, sr, **rbargs)
lib_v5/spec_utils.py ADDED
@@ -0,0 +1,692 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import librosa
2
+ import numpy as np
3
+ import soundfile as sf
4
+ import math
5
+ import random
6
+ import math
7
+ import platform
8
+ import traceback
9
+ from . import pyrb
10
+ #cur
11
+ OPERATING_SYSTEM = platform.system()
12
+ SYSTEM_ARCH = platform.platform()
13
+ SYSTEM_PROC = platform.processor()
14
+ ARM = 'arm'
15
+
16
+ if OPERATING_SYSTEM == 'Windows':
17
+ from pyrubberband import pyrb
18
+ else:
19
+ from . import pyrb
20
+
21
+ if OPERATING_SYSTEM == 'Darwin':
22
+ wav_resolution = "polyphase" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else "sinc_fastest"
23
+ else:
24
+ wav_resolution = "sinc_fastest"
25
+
26
+ MAX_SPEC = 'Max Spec'
27
+ MIN_SPEC = 'Min Spec'
28
+ AVERAGE = 'Average'
29
+
30
+ def crop_center(h1, h2):
31
+ h1_shape = h1.size()
32
+ h2_shape = h2.size()
33
+
34
+ if h1_shape[3] == h2_shape[3]:
35
+ return h1
36
+ elif h1_shape[3] < h2_shape[3]:
37
+ raise ValueError('h1_shape[3] must be greater than h2_shape[3]')
38
+
39
+ s_time = (h1_shape[3] - h2_shape[3]) // 2
40
+ e_time = s_time + h2_shape[3]
41
+ h1 = h1[:, :, :, s_time:e_time]
42
+
43
+ return h1
44
+
45
+ def preprocess(X_spec):
46
+ X_mag = np.abs(X_spec)
47
+ X_phase = np.angle(X_spec)
48
+
49
+ return X_mag, X_phase
50
+
51
+ def make_padding(width, cropsize, offset):
52
+ left = offset
53
+ roi_size = cropsize - offset * 2
54
+ if roi_size == 0:
55
+ roi_size = cropsize
56
+ right = roi_size - (width % roi_size) + left
57
+
58
+ return left, right, roi_size
59
+
60
+ def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
61
+ if reverse:
62
+ wave_left = np.flip(np.asfortranarray(wave[0]))
63
+ wave_right = np.flip(np.asfortranarray(wave[1]))
64
+ elif mid_side:
65
+ wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
66
+ wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
67
+ elif mid_side_b2:
68
+ wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
69
+ wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
70
+ else:
71
+ wave_left = np.asfortranarray(wave[0])
72
+ wave_right = np.asfortranarray(wave[1])
73
+
74
+ spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
75
+ spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
76
+
77
+ spec = np.asfortranarray([spec_left, spec_right])
78
+
79
+ return spec
80
+
81
+ def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
82
+ import threading
83
+
84
+ if reverse:
85
+ wave_left = np.flip(np.asfortranarray(wave[0]))
86
+ wave_right = np.flip(np.asfortranarray(wave[1]))
87
+ elif mid_side:
88
+ wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
89
+ wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
90
+ elif mid_side_b2:
91
+ wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
92
+ wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
93
+ else:
94
+ wave_left = np.asfortranarray(wave[0])
95
+ wave_right = np.asfortranarray(wave[1])
96
+
97
+ def run_thread(**kwargs):
98
+ global spec_left
99
+ spec_left = librosa.stft(**kwargs)
100
+
101
+ thread = threading.Thread(target=run_thread, kwargs={'y': wave_left, 'n_fft': n_fft, 'hop_length': hop_length})
102
+ thread.start()
103
+ spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
104
+ thread.join()
105
+
106
+ spec = np.asfortranarray([spec_left, spec_right])
107
+
108
+ return spec
109
+
110
+ def normalize(wave, is_normalize=False):
111
+ """Save output music files"""
112
+ maxv = np.abs(wave).max()
113
+ if maxv > 1.0:
114
+ print(f"\nNormalization Set {is_normalize}: Input above threshold for clipping. Max:{maxv}")
115
+ if is_normalize:
116
+ print(f"The result was normalized.")
117
+ wave /= maxv
118
+ else:
119
+ print(f"The result was not normalized.")
120
+ else:
121
+ print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}")
122
+
123
+ return wave
124
+
125
+ def normalize_two_stem(wave, mix, is_normalize=False):
126
+ """Save output music files"""
127
+
128
+ maxv = np.abs(wave).max()
129
+ max_mix = np.abs(mix).max()
130
+
131
+ if maxv > 1.0:
132
+ print(f"\nNormalization Set {is_normalize}: Primary source above threshold for clipping. Max:{maxv}")
133
+ print(f"\nNormalization Set {is_normalize}: Mixture above threshold for clipping. Max:{max_mix}")
134
+ if is_normalize:
135
+ print(f"The result was normalized.")
136
+ wave /= maxv
137
+ mix /= maxv
138
+ else:
139
+ print(f"The result was not normalized.")
140
+ else:
141
+ print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}")
142
+
143
+
144
+ print(f"\nNormalization Set {is_normalize}: Primary source - Max:{np.abs(wave).max()}")
145
+ print(f"\nNormalization Set {is_normalize}: Mixture - Max:{np.abs(mix).max()}")
146
+
147
+ return wave, mix
148
+
149
+ def combine_spectrograms(specs, mp):
150
+ l = min([specs[i].shape[2] for i in specs])
151
+ spec_c = np.zeros(shape=(2, mp.param['bins'] + 1, l), dtype=np.complex64)
152
+ offset = 0
153
+ bands_n = len(mp.param['band'])
154
+
155
+ for d in range(1, bands_n + 1):
156
+ h = mp.param['band'][d]['crop_stop'] - mp.param['band'][d]['crop_start']
157
+ spec_c[:, offset:offset+h, :l] = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l]
158
+ offset += h
159
+
160
+ if offset > mp.param['bins']:
161
+ raise ValueError('Too much bins')
162
+
163
+ # lowpass fiter
164
+ if mp.param['pre_filter_start'] > 0: # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
165
+ if bands_n == 1:
166
+ spec_c = fft_lp_filter(spec_c, mp.param['pre_filter_start'], mp.param['pre_filter_stop'])
167
+ else:
168
+ gp = 1
169
+ for b in range(mp.param['pre_filter_start'] + 1, mp.param['pre_filter_stop']):
170
+ g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0)
171
+ gp = g
172
+ spec_c[:, b, :] *= g
173
+
174
+ return np.asfortranarray(spec_c)
175
+
176
+ def spectrogram_to_image(spec, mode='magnitude'):
177
+ if mode == 'magnitude':
178
+ if np.iscomplexobj(spec):
179
+ y = np.abs(spec)
180
+ else:
181
+ y = spec
182
+ y = np.log10(y ** 2 + 1e-8)
183
+ elif mode == 'phase':
184
+ if np.iscomplexobj(spec):
185
+ y = np.angle(spec)
186
+ else:
187
+ y = spec
188
+
189
+ y -= y.min()
190
+ y *= 255 / y.max()
191
+ img = np.uint8(y)
192
+
193
+ if y.ndim == 3:
194
+ img = img.transpose(1, 2, 0)
195
+ img = np.concatenate([
196
+ np.max(img, axis=2, keepdims=True), img
197
+ ], axis=2)
198
+
199
+ return img
200
+
201
+ def reduce_vocal_aggressively(X, y, softmask):
202
+ v = X - y
203
+ y_mag_tmp = np.abs(y)
204
+ v_mag_tmp = np.abs(v)
205
+
206
+ v_mask = v_mag_tmp > y_mag_tmp
207
+ y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
208
+
209
+ return y_mag * np.exp(1.j * np.angle(y))
210
+
211
+ def merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):
212
+ mask = y_mask
213
+
214
+ try:
215
+ if min_range < fade_size * 2:
216
+ raise ValueError('min_range must be >= fade_size * 2')
217
+
218
+ idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]
219
+ start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
220
+ end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
221
+ artifact_idx = np.where(end_idx - start_idx > min_range)[0]
222
+ weight = np.zeros_like(y_mask)
223
+ if len(artifact_idx) > 0:
224
+ start_idx = start_idx[artifact_idx]
225
+ end_idx = end_idx[artifact_idx]
226
+ old_e = None
227
+ for s, e in zip(start_idx, end_idx):
228
+ if old_e is not None and s - old_e < fade_size:
229
+ s = old_e - fade_size * 2
230
+
231
+ if s != 0:
232
+ weight[:, :, s:s + fade_size] = np.linspace(0, 1, fade_size)
233
+ else:
234
+ s -= fade_size
235
+
236
+ if e != y_mask.shape[2]:
237
+ weight[:, :, e - fade_size:e] = np.linspace(1, 0, fade_size)
238
+ else:
239
+ e += fade_size
240
+
241
+ weight[:, :, s + fade_size:e - fade_size] = 1
242
+ old_e = e
243
+
244
+ v_mask = 1 - y_mask
245
+ y_mask += weight * v_mask
246
+
247
+ mask = y_mask
248
+ except Exception as e:
249
+ error_name = f'{type(e).__name__}'
250
+ traceback_text = ''.join(traceback.format_tb(e.__traceback__))
251
+ message = f'{error_name}: "{e}"\n{traceback_text}"'
252
+ print('Post Process Failed: ', message)
253
+
254
+
255
+ return mask
256
+
257
+ def align_wave_head_and_tail(a, b):
258
+ l = min([a[0].size, b[0].size])
259
+
260
+ return a[:l,:l], b[:l,:l]
261
+
262
+ def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse, clamp=False):
263
+ spec_left = np.asfortranarray(spec[0])
264
+ spec_right = np.asfortranarray(spec[1])
265
+
266
+ wave_left = librosa.istft(spec_left, hop_length=hop_length)
267
+ wave_right = librosa.istft(spec_right, hop_length=hop_length)
268
+
269
+ if reverse:
270
+ return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
271
+ elif mid_side:
272
+ return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
273
+ elif mid_side_b2:
274
+ return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
275
+ else:
276
+ return np.asfortranarray([wave_left, wave_right])
277
+
278
+ def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
279
+ import threading
280
+
281
+ spec_left = np.asfortranarray(spec[0])
282
+ spec_right = np.asfortranarray(spec[1])
283
+
284
+ def run_thread(**kwargs):
285
+ global wave_left
286
+ wave_left = librosa.istft(**kwargs)
287
+
288
+ thread = threading.Thread(target=run_thread, kwargs={'stft_matrix': spec_left, 'hop_length': hop_length})
289
+ thread.start()
290
+ wave_right = librosa.istft(spec_right, hop_length=hop_length)
291
+ thread.join()
292
+
293
+ if reverse:
294
+ return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
295
+ elif mid_side:
296
+ return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
297
+ elif mid_side_b2:
298
+ return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
299
+ else:
300
+ return np.asfortranarray([wave_left, wave_right])
301
+
302
+ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
303
+ bands_n = len(mp.param['band'])
304
+ offset = 0
305
+
306
+ for d in range(1, bands_n + 1):
307
+ bp = mp.param['band'][d]
308
+ spec_s = np.ndarray(shape=(2, bp['n_fft'] // 2 + 1, spec_m.shape[2]), dtype=complex)
309
+ h = bp['crop_stop'] - bp['crop_start']
310
+ spec_s[:, bp['crop_start']:bp['crop_stop'], :] = spec_m[:, offset:offset+h, :]
311
+
312
+ offset += h
313
+ if d == bands_n: # higher
314
+ if extra_bins_h: # if --high_end_process bypass
315
+ max_bin = bp['n_fft'] // 2
316
+ spec_s[:, max_bin-extra_bins_h:max_bin, :] = extra_bins[:, :extra_bins_h, :]
317
+ if bp['hpf_start'] > 0:
318
+ spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
319
+ if bands_n == 1:
320
+ wave = spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse'])
321
+ else:
322
+ wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
323
+ else:
324
+ sr = mp.param['band'][d+1]['sr']
325
+ if d == 1: # lower
326
+ spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
327
+ wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']), bp['sr'], sr, res_type=wav_resolution)
328
+ else: # mid
329
+ spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
330
+ spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
331
+ wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
332
+ wave = librosa.resample(wave2, bp['sr'], sr, res_type=wav_resolution)
333
+
334
+ return wave
335
+
336
+ def fft_lp_filter(spec, bin_start, bin_stop):
337
+ g = 1.0
338
+ for b in range(bin_start, bin_stop):
339
+ g -= 1 / (bin_stop - bin_start)
340
+ spec[:, b, :] = g * spec[:, b, :]
341
+
342
+ spec[:, bin_stop:, :] *= 0
343
+
344
+ return spec
345
+
346
+ def fft_hp_filter(spec, bin_start, bin_stop):
347
+ g = 1.0
348
+ for b in range(bin_start, bin_stop, -1):
349
+ g -= 1 / (bin_start - bin_stop)
350
+ spec[:, b, :] = g * spec[:, b, :]
351
+
352
+ spec[:, 0:bin_stop+1, :] *= 0
353
+
354
+ return spec
355
+
356
+ def mirroring(a, spec_m, input_high_end, mp):
357
+ if 'mirroring' == a:
358
+ mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
359
+ mirror = mirror * np.exp(1.j * np.angle(input_high_end))
360
+
361
+ return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)
362
+
363
+ if 'mirroring2' == a:
364
+ mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
365
+ mi = np.multiply(mirror, input_high_end * 1.7)
366
+
367
+ return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
368
+
369
+ def adjust_aggr(mask, is_non_accom_stem, aggressiveness):
370
+ aggr = aggressiveness['value']
371
+
372
+ if aggr != 0:
373
+ if is_non_accom_stem:
374
+ aggr = 1 - aggr
375
+
376
+ aggr = [aggr, aggr]
377
+
378
+ if aggressiveness['aggr_correction'] is not None:
379
+ aggr[0] += aggressiveness['aggr_correction']['left']
380
+ aggr[1] += aggressiveness['aggr_correction']['right']
381
+
382
+ for ch in range(2):
383
+ mask[ch, :aggressiveness['split_bin']] = np.power(mask[ch, :aggressiveness['split_bin']], 1 + aggr[ch] / 3)
384
+ mask[ch, aggressiveness['split_bin']:] = np.power(mask[ch, aggressiveness['split_bin']:], 1 + aggr[ch])
385
+
386
+ # if is_non_accom_stem:
387
+ # mask = (1.0 - mask)
388
+
389
+ return mask
390
+
391
+ def stft(wave, nfft, hl):
392
+ wave_left = np.asfortranarray(wave[0])
393
+ wave_right = np.asfortranarray(wave[1])
394
+ spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
395
+ spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
396
+ spec = np.asfortranarray([spec_left, spec_right])
397
+
398
+ return spec
399
+
400
+ def istft(spec, hl):
401
+ spec_left = np.asfortranarray(spec[0])
402
+ spec_right = np.asfortranarray(spec[1])
403
+ wave_left = librosa.istft(spec_left, hop_length=hl)
404
+ wave_right = librosa.istft(spec_right, hop_length=hl)
405
+ wave = np.asfortranarray([wave_left, wave_right])
406
+
407
+ return wave
408
+
409
+ def spec_effects(wave, algorithm='Default', value=None):
410
+ spec = [stft(wave[0],2048,1024), stft(wave[1],2048,1024)]
411
+ if algorithm == 'Min_Mag':
412
+ v_spec_m = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0])
413
+ wave = istft(v_spec_m,1024)
414
+ elif algorithm == 'Max_Mag':
415
+ v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])
416
+ wave = istft(v_spec_m,1024)
417
+ elif algorithm == 'Default':
418
+ wave = (wave[1] * value) + (wave[0] * (1-value))
419
+ elif algorithm == 'Invert_p':
420
+ X_mag = np.abs(spec[0])
421
+ y_mag = np.abs(spec[1])
422
+ max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
423
+ v_spec = spec[1] - max_mag * np.exp(1.j * np.angle(spec[0]))
424
+ wave = istft(v_spec,1024)
425
+
426
+ return wave
427
+
428
+ def spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024):
429
+ wave = librosa.istft(spec, n_fft=n_fft, hop_length=hop_length)
430
+
431
+ if wave.ndim == 1:
432
+ wave = np.asfortranarray([wave,wave])
433
+
434
+ return wave
435
+
436
+ def wave_to_spectrogram_no_mp(wave):
437
+
438
+ spec = librosa.stft(wave, n_fft=2048, hop_length=1024)
439
+
440
+ if spec.ndim == 1:
441
+ spec = np.asfortranarray([spec,spec])
442
+
443
+ return spec
444
+
445
+ def invert_audio(specs, invert_p=True):
446
+
447
+ ln = min([specs[0].shape[2], specs[1].shape[2]])
448
+ specs[0] = specs[0][:,:,:ln]
449
+ specs[1] = specs[1][:,:,:ln]
450
+
451
+ if invert_p:
452
+ X_mag = np.abs(specs[0])
453
+ y_mag = np.abs(specs[1])
454
+ max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
455
+ v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0]))
456
+ else:
457
+ specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
458
+ v_spec = specs[0] - specs[1]
459
+
460
+ return v_spec
461
+
462
+ def invert_stem(mixture, stem):
463
+
464
+ mixture = wave_to_spectrogram_no_mp(mixture)
465
+ stem = wave_to_spectrogram_no_mp(stem)
466
+ output = spectrogram_to_wave_no_mp(invert_audio([mixture, stem]))
467
+
468
+ return -output.T
469
+
470
+ def ensembling(a, specs):
471
+ for i in range(1, len(specs)):
472
+ if i == 1:
473
+ spec = specs[0]
474
+
475
+ ln = min([spec.shape[2], specs[i].shape[2]])
476
+ spec = spec[:,:,:ln]
477
+ specs[i] = specs[i][:,:,:ln]
478
+
479
+ if MIN_SPEC == a:
480
+ spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
481
+ if MAX_SPEC == a:
482
+ spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
483
+ if AVERAGE == a:
484
+ spec = np.where(np.abs(specs[i]) == np.abs(spec), specs[i], spec)
485
+
486
+ return spec
487
+
488
+ def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path):
489
+
490
+ wavs_ = []
491
+
492
+ if algorithm == AVERAGE:
493
+ output = average_audio(audio_input)
494
+ samplerate = 44100
495
+ else:
496
+ specs = []
497
+
498
+ for i in range(len(audio_input)):
499
+ wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100)
500
+ wavs_.append(wave)
501
+ spec = wave_to_spectrogram_no_mp(wave)
502
+ specs.append(spec)
503
+
504
+ wave_shapes = [w.shape[1] for w in wavs_]
505
+ target_shape = wavs_[wave_shapes.index(max(wave_shapes))]
506
+
507
+ output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))
508
+ output = to_shape(output, target_shape.shape)
509
+
510
+ sf.write(save_path, normalize(output.T, is_normalization), samplerate, subtype=wav_type_set)
511
+
512
+ def to_shape(x, target_shape):
513
+ padding_list = []
514
+ for x_dim, target_dim in zip(x.shape, target_shape):
515
+ pad_value = (target_dim - x_dim)
516
+ pad_tuple = ((0, pad_value))
517
+ padding_list.append(pad_tuple)
518
+
519
+ return np.pad(x, tuple(padding_list), mode='constant')
520
+
521
+ def to_shape_minimize(x: np.ndarray, target_shape):
522
+
523
+ padding_list = []
524
+ for x_dim, target_dim in zip(x.shape, target_shape):
525
+ pad_value = (target_dim - x_dim)
526
+ pad_tuple = ((0, pad_value))
527
+ padding_list.append(pad_tuple)
528
+
529
+ return np.pad(x, tuple(padding_list), mode='constant')
530
+
531
+ def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False):
532
+
533
+ wav, sr = librosa.load(audio_file, sr=44100, mono=False)
534
+
535
+ if wav.ndim == 1:
536
+ wav = np.asfortranarray([wav,wav])
537
+
538
+ if is_pitch:
539
+ wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)
540
+ wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)
541
+ else:
542
+ wav_1 = pyrb.time_stretch(wav[0], sr, rate, rbargs=None)
543
+ wav_2 = pyrb.time_stretch(wav[1], sr, rate, rbargs=None)
544
+
545
+ if wav_1.shape > wav_2.shape:
546
+ wav_2 = to_shape(wav_2, wav_1.shape)
547
+ if wav_1.shape < wav_2.shape:
548
+ wav_1 = to_shape(wav_1, wav_2.shape)
549
+
550
+ wav_mix = np.asfortranarray([wav_1, wav_2])
551
+
552
+ sf.write(export_path, normalize(wav_mix.T, is_normalization), sr, subtype=wav_type_set)
553
+ save_format(export_path)
554
+
555
+ def average_audio(audio):
556
+
557
+ waves = []
558
+ wave_shapes = []
559
+ final_waves = []
560
+
561
+ for i in range(len(audio)):
562
+ wave = librosa.load(audio[i], sr=44100, mono=False)
563
+ waves.append(wave[0])
564
+ wave_shapes.append(wave[0].shape[1])
565
+
566
+ wave_shapes_index = wave_shapes.index(max(wave_shapes))
567
+ target_shape = waves[wave_shapes_index]
568
+ waves.pop(wave_shapes_index)
569
+ final_waves.append(target_shape)
570
+
571
+ for n_array in waves:
572
+ wav_target = to_shape(n_array, target_shape.shape)
573
+ final_waves.append(wav_target)
574
+
575
+ waves = sum(final_waves)
576
+ waves = waves/len(audio)
577
+
578
+ return waves
579
+
580
+ def average_dual_sources(wav_1, wav_2, value):
581
+
582
+ if wav_1.shape > wav_2.shape:
583
+ wav_2 = to_shape(wav_2, wav_1.shape)
584
+ if wav_1.shape < wav_2.shape:
585
+ wav_1 = to_shape(wav_1, wav_2.shape)
586
+
587
+ wave = (wav_1 * value) + (wav_2 * (1-value))
588
+
589
+ return wave
590
+
591
+ def reshape_sources(wav_1: np.ndarray, wav_2: np.ndarray):
592
+
593
+ if wav_1.shape > wav_2.shape:
594
+ wav_2 = to_shape(wav_2, wav_1.shape)
595
+ if wav_1.shape < wav_2.shape:
596
+ ln = min([wav_1.shape[1], wav_2.shape[1]])
597
+ wav_2 = wav_2[:,:ln]
598
+
599
+ ln = min([wav_1.shape[1], wav_2.shape[1]])
600
+ wav_1 = wav_1[:,:ln]
601
+ wav_2 = wav_2[:,:ln]
602
+
603
+ return wav_2
604
+
605
+ def align_audio(file1, file2, file2_aligned, file_subtracted, wav_type_set, is_normalization, command_Text, progress_bar_main_var, save_format):
606
+ def get_diff(a, b):
607
+ corr = np.correlate(a, b, "full")
608
+ diff = corr.argmax() - (b.shape[0] - 1)
609
+ return diff
610
+
611
+ progress_bar_main_var.set(10)
612
+
613
+ # read tracks
614
+ wav1, sr1 = librosa.load(file1, sr=44100, mono=False)
615
+ wav2, sr2 = librosa.load(file2, sr=44100, mono=False)
616
+ wav1 = wav1.transpose()
617
+ wav2 = wav2.transpose()
618
+
619
+ command_Text(f"Audio file shapes: {wav1.shape} / {wav2.shape}\n")
620
+
621
+ wav2_org = wav2.copy()
622
+ progress_bar_main_var.set(20)
623
+
624
+ command_Text("Processing files... \n")
625
+
626
+ # pick random position and get diff
627
+
628
+ counts = {} # counting up for each diff value
629
+ progress = 20
630
+
631
+ check_range = 64
632
+
633
+ base = (64 / check_range)
634
+
635
+ for i in range(check_range):
636
+ index = int(random.uniform(44100 * 2, min(wav1.shape[0], wav2.shape[0]) - 44100 * 2))
637
+ shift = int(random.uniform(-22050,+22050))
638
+ samp1 = wav1[index :index +44100, 0] # currently use left channel
639
+ samp2 = wav2[index+shift:index+shift+44100, 0]
640
+ progress += 1 * base
641
+ progress_bar_main_var.set(progress)
642
+ diff = get_diff(samp1, samp2)
643
+ diff -= shift
644
+
645
+ if abs(diff) < 22050:
646
+ if not diff in counts:
647
+ counts[diff] = 0
648
+ counts[diff] += 1
649
+
650
+ # use max counted diff value
651
+ max_count = 0
652
+ est_diff = 0
653
+ for diff in counts.keys():
654
+ if counts[diff] > max_count:
655
+ max_count = counts[diff]
656
+ est_diff = diff
657
+
658
+ command_Text(f"Estimated difference is {est_diff} (count: {max_count})\n")
659
+
660
+ progress_bar_main_var.set(90)
661
+
662
+ audio_files = []
663
+
664
+ def save_aligned_audio(wav2_aligned):
665
+ command_Text(f"Aligned File 2 with File 1.\n")
666
+ command_Text(f"Saving files... ")
667
+ sf.write(file2_aligned, normalize(wav2_aligned, is_normalization), sr2, subtype=wav_type_set)
668
+ save_format(file2_aligned)
669
+ min_len = min(wav1.shape[0], wav2_aligned.shape[0])
670
+ wav_sub = wav1[:min_len] - wav2_aligned[:min_len]
671
+ audio_files.append(file2_aligned)
672
+ return min_len, wav_sub
673
+
674
+ # make aligned track 2
675
+ if est_diff > 0:
676
+ wav2_aligned = np.append(np.zeros((est_diff, 2)), wav2_org, axis=0)
677
+ min_len, wav_sub = save_aligned_audio(wav2_aligned)
678
+ elif est_diff < 0:
679
+ wav2_aligned = wav2_org[-est_diff:]
680
+ min_len, wav_sub = save_aligned_audio(wav2_aligned)
681
+ else:
682
+ command_Text(f"Audio files already aligned.\n")
683
+ command_Text(f"Saving inverted track... ")
684
+ min_len = min(wav1.shape[0], wav2.shape[0])
685
+ wav_sub = wav1[:min_len] - wav2[:min_len]
686
+
687
+ wav_sub = np.clip(wav_sub, -1, +1)
688
+
689
+ sf.write(file_subtracted, normalize(wav_sub, is_normalization), sr1, subtype=wav_type_set)
690
+ save_format(file_subtracted)
691
+
692
+ progress_bar_main_var.set(95)
lib_v5/vr_network/__init__.py ADDED
@@ -0,0 +1 @@
 
 
1
+ # VR init.
lib_v5/vr_network/__pycache__/__init__.cpython-310.pyc ADDED
Binary file (152 Bytes). View file
 
lib_v5/vr_network/__pycache__/layers.cpython-310.pyc ADDED
Binary file (4.47 kB). View file
 
lib_v5/vr_network/__pycache__/layers_new.cpython-310.pyc ADDED
Binary file (4.44 kB). View file
 
lib_v5/vr_network/__pycache__/model_param_init.cpython-310.pyc ADDED
Binary file (1.62 kB). View file
 
lib_v5/vr_network/__pycache__/nets.cpython-310.pyc ADDED
Binary file (4.39 kB). View file
 
lib_v5/vr_network/__pycache__/nets_new.cpython-310.pyc ADDED
Binary file (4 kB). View file
 
lib_v5/vr_network/layers.py ADDED
@@ -0,0 +1,143 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch import nn
3
+ import torch.nn.functional as F
4
+
5
+ from lib_v5 import spec_utils
6
+
7
+ class Conv2DBNActiv(nn.Module):
8
+
9
+ def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
10
+ super(Conv2DBNActiv, self).__init__()
11
+ self.conv = nn.Sequential(
12
+ nn.Conv2d(
13
+ nin, nout,
14
+ kernel_size=ksize,
15
+ stride=stride,
16
+ padding=pad,
17
+ dilation=dilation,
18
+ bias=False),
19
+ nn.BatchNorm2d(nout),
20
+ activ()
21
+ )
22
+
23
+ def __call__(self, x):
24
+ return self.conv(x)
25
+
26
+ class SeperableConv2DBNActiv(nn.Module):
27
+
28
+ def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
29
+ super(SeperableConv2DBNActiv, self).__init__()
30
+ self.conv = nn.Sequential(
31
+ nn.Conv2d(
32
+ nin, nin,
33
+ kernel_size=ksize,
34
+ stride=stride,
35
+ padding=pad,
36
+ dilation=dilation,
37
+ groups=nin,
38
+ bias=False),
39
+ nn.Conv2d(
40
+ nin, nout,
41
+ kernel_size=1,
42
+ bias=False),
43
+ nn.BatchNorm2d(nout),
44
+ activ()
45
+ )
46
+
47
+ def __call__(self, x):
48
+ return self.conv(x)
49
+
50
+
51
+ class Encoder(nn.Module):
52
+
53
+ def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
54
+ super(Encoder, self).__init__()
55
+ self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
56
+ self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
57
+
58
+ def __call__(self, x):
59
+ skip = self.conv1(x)
60
+ h = self.conv2(skip)
61
+
62
+ return h, skip
63
+
64
+
65
+ class Decoder(nn.Module):
66
+
67
+ def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):
68
+ super(Decoder, self).__init__()
69
+ self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
70
+ self.dropout = nn.Dropout2d(0.1) if dropout else None
71
+
72
+ def __call__(self, x, skip=None):
73
+ x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
74
+ if skip is not None:
75
+ skip = spec_utils.crop_center(skip, x)
76
+ x = torch.cat([x, skip], dim=1)
77
+ h = self.conv(x)
78
+
79
+ if self.dropout is not None:
80
+ h = self.dropout(h)
81
+
82
+ return h
83
+
84
+
85
+ class ASPPModule(nn.Module):
86
+
87
+ def __init__(self, nn_architecture, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
88
+ super(ASPPModule, self).__init__()
89
+ self.conv1 = nn.Sequential(
90
+ nn.AdaptiveAvgPool2d((1, None)),
91
+ Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
92
+ )
93
+
94
+ self.nn_architecture = nn_architecture
95
+ self.six_layer = [129605]
96
+ self.seven_layer = [537238, 537227, 33966]
97
+
98
+ extra_conv = SeperableConv2DBNActiv(
99
+ nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
100
+
101
+ self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
102
+ self.conv3 = SeperableConv2DBNActiv(
103
+ nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)
104
+ self.conv4 = SeperableConv2DBNActiv(
105
+ nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)
106
+ self.conv5 = SeperableConv2DBNActiv(
107
+ nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
108
+
109
+ if self.nn_architecture in self.six_layer:
110
+ self.conv6 = extra_conv
111
+ nin_x = 6
112
+ elif self.nn_architecture in self.seven_layer:
113
+ self.conv6 = extra_conv
114
+ self.conv7 = extra_conv
115
+ nin_x = 7
116
+ else:
117
+ nin_x = 5
118
+
119
+ self.bottleneck = nn.Sequential(
120
+ Conv2DBNActiv(nin * nin_x, nout, 1, 1, 0, activ=activ),
121
+ nn.Dropout2d(0.1)
122
+ )
123
+
124
+ def forward(self, x):
125
+ _, _, h, w = x.size()
126
+ feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
127
+ feat2 = self.conv2(x)
128
+ feat3 = self.conv3(x)
129
+ feat4 = self.conv4(x)
130
+ feat5 = self.conv5(x)
131
+
132
+ if self.nn_architecture in self.six_layer:
133
+ feat6 = self.conv6(x)
134
+ out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1)
135
+ elif self.nn_architecture in self.seven_layer:
136
+ feat6 = self.conv6(x)
137
+ feat7 = self.conv7(x)
138
+ out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
139
+ else:
140
+ out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
141
+
142
+ bottle = self.bottleneck(out)
143
+ return bottle
lib_v5/vr_network/layers_new.py ADDED
@@ -0,0 +1,126 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch import nn
3
+ import torch.nn.functional as F
4
+
5
+ from lib_v5 import spec_utils
6
+
7
+ class Conv2DBNActiv(nn.Module):
8
+
9
+ def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
10
+ super(Conv2DBNActiv, self).__init__()
11
+ self.conv = nn.Sequential(
12
+ nn.Conv2d(
13
+ nin, nout,
14
+ kernel_size=ksize,
15
+ stride=stride,
16
+ padding=pad,
17
+ dilation=dilation,
18
+ bias=False),
19
+ nn.BatchNorm2d(nout),
20
+ activ()
21
+ )
22
+
23
+ def __call__(self, x):
24
+ return self.conv(x)
25
+
26
+ class Encoder(nn.Module):
27
+
28
+ def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
29
+ super(Encoder, self).__init__()
30
+ self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ)
31
+ self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)
32
+
33
+ def __call__(self, x):
34
+ h = self.conv1(x)
35
+ h = self.conv2(h)
36
+
37
+ return h
38
+
39
+
40
+ class Decoder(nn.Module):
41
+
42
+ def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):
43
+ super(Decoder, self).__init__()
44
+ self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
45
+ # self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)
46
+ self.dropout = nn.Dropout2d(0.1) if dropout else None
47
+
48
+ def __call__(self, x, skip=None):
49
+ x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
50
+
51
+ if skip is not None:
52
+ skip = spec_utils.crop_center(skip, x)
53
+ x = torch.cat([x, skip], dim=1)
54
+
55
+ h = self.conv1(x)
56
+ # h = self.conv2(h)
57
+
58
+ if self.dropout is not None:
59
+ h = self.dropout(h)
60
+
61
+ return h
62
+
63
+
64
+ class ASPPModule(nn.Module):
65
+
66
+ def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False):
67
+ super(ASPPModule, self).__init__()
68
+ self.conv1 = nn.Sequential(
69
+ nn.AdaptiveAvgPool2d((1, None)),
70
+ Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)
71
+ )
72
+ self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)
73
+ self.conv3 = Conv2DBNActiv(
74
+ nin, nout, 3, 1, dilations[0], dilations[0], activ=activ
75
+ )
76
+ self.conv4 = Conv2DBNActiv(
77
+ nin, nout, 3, 1, dilations[1], dilations[1], activ=activ
78
+ )
79
+ self.conv5 = Conv2DBNActiv(
80
+ nin, nout, 3, 1, dilations[2], dilations[2], activ=activ
81
+ )
82
+ self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ)
83
+ self.dropout = nn.Dropout2d(0.1) if dropout else None
84
+
85
+ def forward(self, x):
86
+ _, _, h, w = x.size()
87
+ feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
88
+ feat2 = self.conv2(x)
89
+ feat3 = self.conv3(x)
90
+ feat4 = self.conv4(x)
91
+ feat5 = self.conv5(x)
92
+ out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
93
+ out = self.bottleneck(out)
94
+
95
+ if self.dropout is not None:
96
+ out = self.dropout(out)
97
+
98
+ return out
99
+
100
+
101
+ class LSTMModule(nn.Module):
102
+
103
+ def __init__(self, nin_conv, nin_lstm, nout_lstm):
104
+ super(LSTMModule, self).__init__()
105
+ self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0)
106
+ self.lstm = nn.LSTM(
107
+ input_size=nin_lstm,
108
+ hidden_size=nout_lstm // 2,
109
+ bidirectional=True
110
+ )
111
+ self.dense = nn.Sequential(
112
+ nn.Linear(nout_lstm, nin_lstm),
113
+ nn.BatchNorm1d(nin_lstm),
114
+ nn.ReLU()
115
+ )
116
+
117
+ def forward(self, x):
118
+ N, _, nbins, nframes = x.size()
119
+ h = self.conv(x)[:, 0] # N, nbins, nframes
120
+ h = h.permute(2, 0, 1) # nframes, N, nbins
121
+ h, _ = self.lstm(h)
122
+ h = self.dense(h.reshape(-1, h.size()[-1])) # nframes * N, nbins
123
+ h = h.reshape(nframes, N, 1, nbins)
124
+ h = h.permute(1, 2, 3, 0)
125
+
126
+ return h
lib_v5/vr_network/model_param_init.py ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
2
+ import pathlib
3
+
4
+ default_param = {}
5
+ default_param['bins'] = 768
6
+ default_param['unstable_bins'] = 9 # training only
7
+ default_param['reduction_bins'] = 762 # training only
8
+ default_param['sr'] = 44100
9
+ default_param['pre_filter_start'] = 757
10
+ default_param['pre_filter_stop'] = 768
11
+ default_param['band'] = {}
12
+
13
+
14
+ default_param['band'][1] = {
15
+ 'sr': 11025,
16
+ 'hl': 128,
17
+ 'n_fft': 960,
18
+ 'crop_start': 0,
19
+ 'crop_stop': 245,
20
+ 'lpf_start': 61, # inference only
21
+ 'res_type': 'polyphase'
22
+ }
23
+
24
+ default_param['band'][2] = {
25
+ 'sr': 44100,
26
+ 'hl': 512,
27
+ 'n_fft': 1536,
28
+ 'crop_start': 24,
29
+ 'crop_stop': 547,
30
+ 'hpf_start': 81, # inference only
31
+ 'res_type': 'sinc_best'
32
+ }
33
+
34
+
35
+ def int_keys(d):
36
+ r = {}
37
+ for k, v in d:
38
+ if k.isdigit():
39
+ k = int(k)
40
+ r[k] = v
41
+ return r
42
+
43
+
44
+ class ModelParameters(object):
45
+ def __init__(self, config_path=''):
46
+ if '.pth' == pathlib.Path(config_path).suffix:
47
+ import zipfile
48
+
49
+ with zipfile.ZipFile(config_path, 'r') as zip:
50
+ self.param = json.loads(zip.read('param.json'), object_pairs_hook=int_keys)
51
+ elif '.json' == pathlib.Path(config_path).suffix:
52
+ with open(config_path, 'r') as f:
53
+ self.param = json.loads(f.read(), object_pairs_hook=int_keys)
54
+ else:
55
+ self.param = default_param
56
+
57
+ for k in ['mid_side', 'mid_side_b', 'mid_side_b2', 'stereo_w', 'stereo_n', 'reverse']:
58
+ if not k in self.param:
59
+ self.param[k] = False
lib_v5/vr_network/modelparams/1band_sr16000_hl512.json ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bins": 1024,
3
+ "unstable_bins": 0,
4
+ "reduction_bins": 0,
5
+ "band": {
6
+ "1": {
7
+ "sr": 16000,
8
+ "hl": 512,
9
+ "n_fft": 2048,
10
+ "crop_start": 0,
11
+ "crop_stop": 1024,
12
+ "hpf_start": -1,
13
+ "res_type": "sinc_best"
14
+ }
15
+ },
16
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50
+ }
51
+ },
52
+ "sr": 44100,
53
+ "pre_filter_start": 740,
54
+ "pre_filter_stop": 768
55
+ }
lib_v5/vr_network/modelparams/4band_44100_reverse.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "reverse": true,
3
+ "bins": 768,
4
+ "unstable_bins": 7,
5
+ "reduction_bins": 668,
6
+ "band": {
7
+ "1": {
8
+ "sr": 11025,
9
+ "hl": 128,
10
+ "n_fft": 1024,
11
+ "crop_start": 0,
12
+ "crop_stop": 186,
13
+ "lpf_start": 37,
14
+ "lpf_stop": 73,
15
+ "res_type": "polyphase"
16
+ },
17
+ "2": {
18
+ "sr": 11025,
19
+ "hl": 128,
20
+ "n_fft": 512,
21
+ "crop_start": 4,
22
+ "crop_stop": 185,
23
+ "hpf_start": 36,
24
+ "hpf_stop": 18,
25
+ "lpf_start": 93,
26
+ "lpf_stop": 185,
27
+ "res_type": "polyphase"
28
+ },
29
+ "3": {
30
+ "sr": 22050,
31
+ "hl": 256,
32
+ "n_fft": 512,
33
+ "crop_start": 46,
34
+ "crop_stop": 186,
35
+ "hpf_start": 93,
36
+ "hpf_stop": 46,
37
+ "lpf_start": 164,
38
+ "lpf_stop": 186,
39
+ "res_type": "polyphase"
40
+ },
41
+ "4": {
42
+ "sr": 44100,
43
+ "hl": 512,
44
+ "n_fft": 768,
45
+ "crop_start": 121,
46
+ "crop_stop": 382,
47
+ "hpf_start": 138,
48
+ "hpf_stop": 123,
49
+ "res_type": "sinc_medium"
50
+ }
51
+ },
52
+ "sr": 44100,
53
+ "pre_filter_start": 740,
54
+ "pre_filter_stop": 768
55
+ }
lib_v5/vr_network/modelparams/4band_44100_sw.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "stereo_w": true,
3
+ "bins": 768,
4
+ "unstable_bins": 7,
5
+ "reduction_bins": 668,
6
+ "band": {
7
+ "1": {
8
+ "sr": 11025,
9
+ "hl": 128,
10
+ "n_fft": 1024,
11
+ "crop_start": 0,
12
+ "crop_stop": 186,
13
+ "lpf_start": 37,
14
+ "lpf_stop": 73,
15
+ "res_type": "polyphase"
16
+ },
17
+ "2": {
18
+ "sr": 11025,
19
+ "hl": 128,
20
+ "n_fft": 512,
21
+ "crop_start": 4,
22
+ "crop_stop": 185,
23
+ "hpf_start": 36,
24
+ "hpf_stop": 18,
25
+ "lpf_start": 93,
26
+ "lpf_stop": 185,
27
+ "res_type": "polyphase"
28
+ },
29
+ "3": {
30
+ "sr": 22050,
31
+ "hl": 256,
32
+ "n_fft": 512,
33
+ "crop_start": 46,
34
+ "crop_stop": 186,
35
+ "hpf_start": 93,
36
+ "hpf_stop": 46,
37
+ "lpf_start": 164,
38
+ "lpf_stop": 186,
39
+ "res_type": "polyphase"
40
+ },
41
+ "4": {
42
+ "sr": 44100,
43
+ "hl": 512,
44
+ "n_fft": 768,
45
+ "crop_start": 121,
46
+ "crop_stop": 382,
47
+ "hpf_start": 138,
48
+ "hpf_stop": 123,
49
+ "res_type": "sinc_medium"
50
+ }
51
+ },
52
+ "sr": 44100,
53
+ "pre_filter_start": 740,
54
+ "pre_filter_stop": 768
55
+ }
lib_v5/vr_network/modelparams/4band_v2.json ADDED
@@ -0,0 +1,54 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bins": 672,
3
+ "unstable_bins": 8,
4
+ "reduction_bins": 637,
5
+ "band": {
6
+ "1": {
7
+ "sr": 7350,
8
+ "hl": 80,
9
+ "n_fft": 640,
10
+ "crop_start": 0,
11
+ "crop_stop": 85,
12
+ "lpf_start": 25,
13
+ "lpf_stop": 53,
14
+ "res_type": "polyphase"
15
+ },
16
+ "2": {
17
+ "sr": 7350,
18
+ "hl": 80,
19
+ "n_fft": 320,
20
+ "crop_start": 4,
21
+ "crop_stop": 87,
22
+ "hpf_start": 25,
23
+ "hpf_stop": 12,
24
+ "lpf_start": 31,
25
+ "lpf_stop": 62,
26
+ "res_type": "polyphase"
27
+ },
28
+ "3": {
29
+ "sr": 14700,
30
+ "hl": 160,
31
+ "n_fft": 512,
32
+ "crop_start": 17,
33
+ "crop_stop": 216,
34
+ "hpf_start": 48,
35
+ "hpf_stop": 24,
36
+ "lpf_start": 139,
37
+ "lpf_stop": 210,
38
+ "res_type": "polyphase"
39
+ },
40
+ "4": {
41
+ "sr": 44100,
42
+ "hl": 480,
43
+ "n_fft": 960,
44
+ "crop_start": 78,
45
+ "crop_stop": 383,
46
+ "hpf_start": 130,
47
+ "hpf_stop": 86,
48
+ "res_type": "kaiser_fast"
49
+ }
50
+ },
51
+ "sr": 44100,
52
+ "pre_filter_start": 668,
53
+ "pre_filter_stop": 672
54
+ }
lib_v5/vr_network/modelparams/4band_v2_sn.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bins": 672,
3
+ "unstable_bins": 8,
4
+ "reduction_bins": 637,
5
+ "band": {
6
+ "1": {
7
+ "sr": 7350,
8
+ "hl": 80,
9
+ "n_fft": 640,
10
+ "crop_start": 0,
11
+ "crop_stop": 85,
12
+ "lpf_start": 25,
13
+ "lpf_stop": 53,
14
+ "res_type": "polyphase"
15
+ },
16
+ "2": {
17
+ "sr": 7350,
18
+ "hl": 80,
19
+ "n_fft": 320,
20
+ "crop_start": 4,
21
+ "crop_stop": 87,
22
+ "hpf_start": 25,
23
+ "hpf_stop": 12,
24
+ "lpf_start": 31,
25
+ "lpf_stop": 62,
26
+ "res_type": "polyphase"
27
+ },
28
+ "3": {
29
+ "sr": 14700,
30
+ "hl": 160,
31
+ "n_fft": 512,
32
+ "crop_start": 17,
33
+ "crop_stop": 216,
34
+ "hpf_start": 48,
35
+ "hpf_stop": 24,
36
+ "lpf_start": 139,
37
+ "lpf_stop": 210,
38
+ "res_type": "polyphase"
39
+ },
40
+ "4": {
41
+ "sr": 44100,
42
+ "hl": 480,
43
+ "n_fft": 960,
44
+ "crop_start": 78,
45
+ "crop_stop": 383,
46
+ "hpf_start": 130,
47
+ "hpf_stop": 86,
48
+ "convert_channels": "stereo_n",
49
+ "res_type": "kaiser_fast"
50
+ }
51
+ },
52
+ "sr": 44100,
53
+ "pre_filter_start": 668,
54
+ "pre_filter_stop": 672
55
+ }
lib_v5/vr_network/modelparams/4band_v3.json ADDED
@@ -0,0 +1,54 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bins": 672,
3
+ "unstable_bins": 8,
4
+ "reduction_bins": 530,
5
+ "band": {
6
+ "1": {
7
+ "sr": 7350,
8
+ "hl": 80,
9
+ "n_fft": 640,
10
+ "crop_start": 0,
11
+ "crop_stop": 85,
12
+ "lpf_start": 25,
13
+ "lpf_stop": 53,
14
+ "res_type": "polyphase"
15
+ },
16
+ "2": {
17
+ "sr": 7350,
18
+ "hl": 80,
19
+ "n_fft": 320,
20
+ "crop_start": 4,
21
+ "crop_stop": 87,
22
+ "hpf_start": 25,
23
+ "hpf_stop": 12,
24
+ "lpf_start": 31,
25
+ "lpf_stop": 62,
26
+ "res_type": "polyphase"
27
+ },
28
+ "3": {
29
+ "sr": 14700,
30
+ "hl": 160,
31
+ "n_fft": 512,
32
+ "crop_start": 17,
33
+ "crop_stop": 216,
34
+ "hpf_start": 48,
35
+ "hpf_stop": 24,
36
+ "lpf_start": 139,
37
+ "lpf_stop": 210,
38
+ "res_type": "polyphase"
39
+ },
40
+ "4": {
41
+ "sr": 44100,
42
+ "hl": 480,
43
+ "n_fft": 960,
44
+ "crop_start": 78,
45
+ "crop_stop": 383,
46
+ "hpf_start": 130,
47
+ "hpf_stop": 86,
48
+ "res_type": "kaiser_fast"
49
+ }
50
+ },
51
+ "sr": 44100,
52
+ "pre_filter_start": 668,
53
+ "pre_filter_stop": 672
54
+ }
lib_v5/vr_network/modelparams/ensemble.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "mid_side_b2": true,
3
+ "bins": 1280,
4
+ "unstable_bins": 7,
5
+ "reduction_bins": 565,
6
+ "band": {
7
+ "1": {
8
+ "sr": 11025,
9
+ "hl": 108,
10
+ "n_fft": 2048,
11
+ "crop_start": 0,
12
+ "crop_stop": 374,
13
+ "lpf_start": 92,
14
+ "lpf_stop": 186,
15
+ "res_type": "polyphase"
16
+ },
17
+ "2": {
18
+ "sr": 22050,
19
+ "hl": 216,
20
+ "n_fft": 1536,
21
+ "crop_start": 0,
22
+ "crop_stop": 424,
23
+ "hpf_start": 68,
24
+ "hpf_stop": 34,
25
+ "lpf_start": 348,
26
+ "lpf_stop": 418,
27
+ "res_type": "polyphase"
28
+ },
29
+ "3": {
30
+ "sr": 44100,
31
+ "hl": 432,
32
+ "n_fft": 1280,
33
+ "crop_start": 132,
34
+ "crop_stop": 614,
35
+ "hpf_start": 172,
36
+ "hpf_stop": 144,
37
+ "res_type": "polyphase"
38
+ }
39
+ },
40
+ "sr": 44100,
41
+ "pre_filter_start": 1280,
42
+ "pre_filter_stop": 1280
43
+ }
lib_v5/vr_network/modelparams/lib_v5_vr_network_modelparams_4band_v3_sn.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "n_bins": 672,
3
+ "unstable_bins": 8,
4
+ "stable_bins": 530,
5
+ "band": {
6
+ "1": {
7
+ "sr": 7350,
8
+ "hl": 80,
9
+ "n_fft": 640,
10
+ "crop_start": 0,
11
+ "crop_stop": 85,
12
+ "lpf_start": 25,
13
+ "lpf_stop": 53,
14
+ "res_type": "polyphase"
15
+ },
16
+ "2": {
17
+ "sr": 7350,
18
+ "hl": 80,
19
+ "n_fft": 320,
20
+ "crop_start": 4,
21
+ "crop_stop": 87,
22
+ "hpf_start": 25,
23
+ "hpf_stop": 12,
24
+ "lpf_start": 31,
25
+ "lpf_stop": 62,
26
+ "res_type": "polyphase"
27
+ },
28
+ "3": {
29
+ "sr": 14700,
30
+ "hl": 160,
31
+ "n_fft": 512,
32
+ "crop_start": 17,
33
+ "crop_stop": 216,
34
+ "hpf_start": 48,
35
+ "hpf_stop": 24,
36
+ "lpf_start": 139,
37
+ "lpf_stop": 210,
38
+ "res_type": "polyphase"
39
+ },
40
+ "4": {
41
+ "sr": 44100,
42
+ "hl": 480,
43
+ "n_fft": 960,
44
+ "crop_start": 78,
45
+ "crop_stop": 383,
46
+ "hpf_start": 130,
47
+ "hpf_stop": 86,
48
+ "convert_channels": "stereo_n",
49
+ "res_type": "kaiser_fast"
50
+ }
51
+ },
52
+ "sr": 44100,
53
+ "pre_filter_start": 668,
54
+ "pre_filter_stop": 672
55
+ }
lib_v5/vr_network/nets.py ADDED
@@ -0,0 +1,166 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch import nn
3
+ import torch.nn.functional as F
4
+
5
+ from . import layers
6
+
7
+ class BaseASPPNet(nn.Module):
8
+
9
+ def __init__(self, nn_architecture, nin, ch, dilations=(4, 8, 16)):
10
+ super(BaseASPPNet, self).__init__()
11
+ self.nn_architecture = nn_architecture
12
+ self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
13
+ self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
14
+ self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
15
+ self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
16
+
17
+ if self.nn_architecture == 129605:
18
+ self.enc5 = layers.Encoder(ch * 8, ch * 16, 3, 2, 1)
19
+ self.aspp = layers.ASPPModule(nn_architecture, ch * 16, ch * 32, dilations)
20
+ self.dec5 = layers.Decoder(ch * (16 + 32), ch * 16, 3, 1, 1)
21
+ else:
22
+ self.aspp = layers.ASPPModule(nn_architecture, ch * 8, ch * 16, dilations)
23
+
24
+ self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
25
+ self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
26
+ self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
27
+ self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
28
+
29
+ def __call__(self, x):
30
+ h, e1 = self.enc1(x)
31
+ h, e2 = self.enc2(h)
32
+ h, e3 = self.enc3(h)
33
+ h, e4 = self.enc4(h)
34
+
35
+ if self.nn_architecture == 129605:
36
+ h, e5 = self.enc5(h)
37
+ h = self.aspp(h)
38
+ h = self.dec5(h, e5)
39
+ else:
40
+ h = self.aspp(h)
41
+
42
+ h = self.dec4(h, e4)
43
+ h = self.dec3(h, e3)
44
+ h = self.dec2(h, e2)
45
+ h = self.dec1(h, e1)
46
+
47
+ return h
48
+
49
+ def determine_model_capacity(n_fft_bins, nn_architecture):
50
+
51
+ sp_model_arch = [31191, 33966, 129605]
52
+ hp_model_arch = [123821, 123812]
53
+ hp2_model_arch = [537238, 537227]
54
+
55
+ if nn_architecture in sp_model_arch:
56
+ model_capacity_data = [
57
+ (2, 16),
58
+ (2, 16),
59
+ (18, 8, 1, 1, 0),
60
+ (8, 16),
61
+ (34, 16, 1, 1, 0),
62
+ (16, 32),
63
+ (32, 2, 1),
64
+ (16, 2, 1),
65
+ (16, 2, 1),
66
+ ]
67
+
68
+ if nn_architecture in hp_model_arch:
69
+ model_capacity_data = [
70
+ (2, 32),
71
+ (2, 32),
72
+ (34, 16, 1, 1, 0),
73
+ (16, 32),
74
+ (66, 32, 1, 1, 0),
75
+ (32, 64),
76
+ (64, 2, 1),
77
+ (32, 2, 1),
78
+ (32, 2, 1),
79
+ ]
80
+
81
+ if nn_architecture in hp2_model_arch:
82
+ model_capacity_data = [
83
+ (2, 64),
84
+ (2, 64),
85
+ (66, 32, 1, 1, 0),
86
+ (32, 64),
87
+ (130, 64, 1, 1, 0),
88
+ (64, 128),
89
+ (128, 2, 1),
90
+ (64, 2, 1),
91
+ (64, 2, 1),
92
+ ]
93
+
94
+ cascaded = CascadedASPPNet
95
+ model = cascaded(n_fft_bins, model_capacity_data, nn_architecture)
96
+
97
+ return model
98
+
99
+ class CascadedASPPNet(nn.Module):
100
+
101
+ def __init__(self, n_fft, model_capacity_data, nn_architecture):
102
+ super(CascadedASPPNet, self).__init__()
103
+ self.stg1_low_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[0])
104
+ self.stg1_high_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[1])
105
+
106
+ self.stg2_bridge = layers.Conv2DBNActiv(*model_capacity_data[2])
107
+ self.stg2_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[3])
108
+
109
+ self.stg3_bridge = layers.Conv2DBNActiv(*model_capacity_data[4])
110
+ self.stg3_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[5])
111
+
112
+ self.out = nn.Conv2d(*model_capacity_data[6], bias=False)
113
+ self.aux1_out = nn.Conv2d(*model_capacity_data[7], bias=False)
114
+ self.aux2_out = nn.Conv2d(*model_capacity_data[8], bias=False)
115
+
116
+ self.max_bin = n_fft // 2
117
+ self.output_bin = n_fft // 2 + 1
118
+
119
+ self.offset = 128
120
+
121
+ def forward(self, x):
122
+ mix = x.detach()
123
+ x = x.clone()
124
+
125
+ x = x[:, :, :self.max_bin]
126
+
127
+ bandw = x.size()[2] // 2
128
+ aux1 = torch.cat([
129
+ self.stg1_low_band_net(x[:, :, :bandw]),
130
+ self.stg1_high_band_net(x[:, :, bandw:])
131
+ ], dim=2)
132
+
133
+ h = torch.cat([x, aux1], dim=1)
134
+ aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
135
+
136
+ h = torch.cat([x, aux1, aux2], dim=1)
137
+ h = self.stg3_full_band_net(self.stg3_bridge(h))
138
+
139
+ mask = torch.sigmoid(self.out(h))
140
+ mask = F.pad(
141
+ input=mask,
142
+ pad=(0, 0, 0, self.output_bin - mask.size()[2]),
143
+ mode='replicate')
144
+
145
+ if self.training:
146
+ aux1 = torch.sigmoid(self.aux1_out(aux1))
147
+ aux1 = F.pad(
148
+ input=aux1,
149
+ pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
150
+ mode='replicate')
151
+ aux2 = torch.sigmoid(self.aux2_out(aux2))
152
+ aux2 = F.pad(
153
+ input=aux2,
154
+ pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
155
+ mode='replicate')
156
+ return mask * mix, aux1 * mix, aux2 * mix
157
+ else:
158
+ return mask# * mix
159
+
160
+ def predict_mask(self, x):
161
+ mask = self.forward(x)
162
+
163
+ if self.offset > 0:
164
+ mask = mask[:, :, :, self.offset:-self.offset]
165
+
166
+ return mask
lib_v5/vr_network/nets_new.py ADDED
@@ -0,0 +1,125 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch import nn
3
+ import torch.nn.functional as F
4
+ from . import layers_new as layers
5
+
6
+ class BaseNet(nn.Module):
7
+
8
+ def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))):
9
+ super(BaseNet, self).__init__()
10
+ self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1)
11
+ self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1)
12
+ self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1)
13
+ self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1)
14
+ self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1)
15
+
16
+ self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)
17
+
18
+ self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)
19
+ self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)
20
+ self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)
21
+ self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm)
22
+ self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)
23
+
24
+ def __call__(self, x):
25
+ e1 = self.enc1(x)
26
+ e2 = self.enc2(e1)
27
+ e3 = self.enc3(e2)
28
+ e4 = self.enc4(e3)
29
+ e5 = self.enc5(e4)
30
+
31
+ h = self.aspp(e5)
32
+
33
+ h = self.dec4(h, e4)
34
+ h = self.dec3(h, e3)
35
+ h = self.dec2(h, e2)
36
+ h = torch.cat([h, self.lstm_dec2(h)], dim=1)
37
+ h = self.dec1(h, e1)
38
+
39
+ return h
40
+
41
+ class CascadedNet(nn.Module):
42
+
43
+ def __init__(self, n_fft, nn_arch_size, nout=32, nout_lstm=128):
44
+ super(CascadedNet, self).__init__()
45
+
46
+ self.max_bin = n_fft // 2
47
+ self.output_bin = n_fft // 2 + 1
48
+ self.nin_lstm = self.max_bin // 2
49
+ self.offset = 64
50
+ nout = 64 if nn_arch_size == 218409 else nout
51
+
52
+ self.stg1_low_band_net = nn.Sequential(
53
+ BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
54
+ layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0)
55
+ )
56
+
57
+ self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2)
58
+
59
+ self.stg2_low_band_net = nn.Sequential(
60
+ BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
61
+ layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0)
62
+ )
63
+ self.stg2_high_band_net = BaseNet(nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2)
64
+
65
+ self.stg3_full_band_net = BaseNet(3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm)
66
+
67
+ self.out = nn.Conv2d(nout, 2, 1, bias=False)
68
+ self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
69
+
70
+ def forward(self, x):
71
+ x = x[:, :, :self.max_bin]
72
+
73
+ bandw = x.size()[2] // 2
74
+ l1_in = x[:, :, :bandw]
75
+ h1_in = x[:, :, bandw:]
76
+ l1 = self.stg1_low_band_net(l1_in)
77
+ h1 = self.stg1_high_band_net(h1_in)
78
+ aux1 = torch.cat([l1, h1], dim=2)
79
+
80
+ l2_in = torch.cat([l1_in, l1], dim=1)
81
+ h2_in = torch.cat([h1_in, h1], dim=1)
82
+ l2 = self.stg2_low_band_net(l2_in)
83
+ h2 = self.stg2_high_band_net(h2_in)
84
+ aux2 = torch.cat([l2, h2], dim=2)
85
+
86
+ f3_in = torch.cat([x, aux1, aux2], dim=1)
87
+ f3 = self.stg3_full_band_net(f3_in)
88
+
89
+ mask = torch.sigmoid(self.out(f3))
90
+ mask = F.pad(
91
+ input=mask,
92
+ pad=(0, 0, 0, self.output_bin - mask.size()[2]),
93
+ mode='replicate'
94
+ )
95
+
96
+ if self.training:
97
+ aux = torch.cat([aux1, aux2], dim=1)
98
+ aux = torch.sigmoid(self.aux_out(aux))
99
+ aux = F.pad(
100
+ input=aux,
101
+ pad=(0, 0, 0, self.output_bin - aux.size()[2]),
102
+ mode='replicate'
103
+ )
104
+ return mask, aux
105
+ else:
106
+ return mask
107
+
108
+ def predict_mask(self, x):
109
+ mask = self.forward(x)
110
+
111
+ if self.offset > 0:
112
+ mask = mask[:, :, :, self.offset:-self.offset]
113
+ assert mask.size()[3] > 0
114
+
115
+ return mask
116
+
117
+ def predict(self, x):
118
+ mask = self.forward(x)
119
+ pred_mag = x * mask
120
+
121
+ if self.offset > 0:
122
+ pred_mag = pred_mag[:, :, :, self.offset:-self.offset]
123
+ assert pred_mag.size()[3] > 0
124
+
125
+ return pred_mag