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0000000000000000000000000000000000000000..80866f8a046e791f118085c1d3ba4cbc08dd2c0c Binary files /dev/null and b/uvr5_pack/lib_v5/__pycache__/spec_utils.cpython-39.pyc differ diff --git a/uvr5_pack/lib_v5/dataset.py b/uvr5_pack/lib_v5/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..ba0e45be1e8878da0b07eb2128e218bbd7de82ef --- /dev/null +++ b/uvr5_pack/lib_v5/dataset.py @@ -0,0 +1,183 @@ +import os +import random + +import numpy as np +import torch +import torch.utils.data +from tqdm import tqdm + +from uvr5_pack.lib_v5 import spec_utils + + +class VocalRemoverValidationSet(torch.utils.data.Dataset): + def __init__(self, patch_list): + self.patch_list = patch_list + + def __len__(self): + return len(self.patch_list) + + def __getitem__(self, idx): + path = self.patch_list[idx] + data = np.load(path) + + X, y = data["X"], data["y"] + + X_mag = np.abs(X) + y_mag = np.abs(y) + + return X_mag, y_mag + + +def make_pair(mix_dir, inst_dir): + input_exts = [".wav", ".m4a", ".mp3", ".mp4", ".flac"] + + X_list = sorted( + [ + os.path.join(mix_dir, fname) + for fname in os.listdir(mix_dir) + if os.path.splitext(fname)[1] in input_exts + ] + ) + y_list = sorted( + [ + os.path.join(inst_dir, fname) + for fname in os.listdir(inst_dir) + if os.path.splitext(fname)[1] in input_exts + ] + ) + + filelist = list(zip(X_list, y_list)) + + return filelist + + +def train_val_split(dataset_dir, split_mode, val_rate, val_filelist): + if split_mode == "random": + filelist = make_pair( + os.path.join(dataset_dir, "mixtures"), + os.path.join(dataset_dir, "instruments"), + ) + + random.shuffle(filelist) + + if len(val_filelist) == 0: + val_size = int(len(filelist) * val_rate) + train_filelist = filelist[:-val_size] + val_filelist = filelist[-val_size:] + else: + train_filelist = [ + pair for pair in filelist if list(pair) not in val_filelist + ] + elif split_mode == "subdirs": + if len(val_filelist) != 0: + raise ValueError( + "The `val_filelist` option is not available in `subdirs` mode" + ) + + train_filelist = make_pair( + os.path.join(dataset_dir, "training/mixtures"), + os.path.join(dataset_dir, "training/instruments"), + ) + + val_filelist = make_pair( + os.path.join(dataset_dir, "validation/mixtures"), + os.path.join(dataset_dir, "validation/instruments"), + ) + + return train_filelist, val_filelist + + +def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha): + perm = np.random.permutation(len(X)) + for i, idx in enumerate(tqdm(perm)): + if np.random.uniform() < reduction_rate: + y[idx] = spec_utils.reduce_vocal_aggressively( + X[idx], y[idx], reduction_mask + ) + + if np.random.uniform() < 0.5: + # swap channel + X[idx] = X[idx, ::-1] + y[idx] = y[idx, ::-1] + if np.random.uniform() < 0.02: + # mono + X[idx] = X[idx].mean(axis=0, keepdims=True) + y[idx] = y[idx].mean(axis=0, keepdims=True) + if np.random.uniform() < 0.02: + # inst + X[idx] = y[idx] + + if np.random.uniform() < mixup_rate and i < len(perm) - 1: + lam = np.random.beta(mixup_alpha, mixup_alpha) + X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]] + y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]] + + return X, y + + +def make_padding(width, cropsize, offset): + left = offset + roi_size = cropsize - left * 2 + if roi_size == 0: + roi_size = cropsize + right = roi_size - (width % roi_size) + left + + return left, right, roi_size + + +def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset): + len_dataset = patches * len(filelist) + + X_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64) + y_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64) + + for i, (X_path, y_path) in enumerate(tqdm(filelist)): + X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft) + coef = np.max([np.abs(X).max(), np.abs(y).max()]) + X, y = X / coef, y / coef + + l, r, roi_size = make_padding(X.shape[2], cropsize, offset) + X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant") + y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant") + + starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches) + ends = starts + cropsize + for j in range(patches): + idx = i * patches + j + X_dataset[idx] = X_pad[:, :, starts[j] : ends[j]] + y_dataset[idx] = y_pad[:, :, starts[j] : ends[j]] + + return X_dataset, y_dataset + + +def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset): + patch_list = [] + patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format( + cropsize, sr, hop_length, n_fft, offset + ) + os.makedirs(patch_dir, exist_ok=True) + + for i, (X_path, y_path) in enumerate(tqdm(filelist)): + basename = os.path.splitext(os.path.basename(X_path))[0] + + X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft) + coef = np.max([np.abs(X).max(), np.abs(y).max()]) + X, y = X / coef, y / coef + + l, r, roi_size = make_padding(X.shape[2], cropsize, offset) + X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant") + y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant") + + len_dataset = int(np.ceil(X.shape[2] / roi_size)) + for j in range(len_dataset): + outpath = os.path.join(patch_dir, "{}_p{}.npz".format(basename, j)) + start = j * roi_size + if not os.path.exists(outpath): + np.savez( + outpath, + X=X_pad[:, :, start : start + cropsize], + y=y_pad[:, :, start : start + cropsize], + ) + patch_list.append(outpath) + + return VocalRemoverValidationSet(patch_list) diff --git a/uvr5_pack/lib_v5/layers.py b/uvr5_pack/lib_v5/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..9835dc0f0dd66a7ef3517101180ec2c54eb6011d --- /dev/null +++ b/uvr5_pack/lib_v5/layers.py @@ -0,0 +1,118 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import spec_utils + + +class Conv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(Conv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nout, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + bias=False, + ), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class SeperableConv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(SeperableConv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nin, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + groups=nin, + bias=False, + ), + nn.Conv2d(nin, nout, kernel_size=1, bias=False), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class Encoder(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): + super(Encoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) + + def __call__(self, x): + skip = self.conv1(x) + h = self.conv2(skip) + + return h, skip + + +class Decoder(nn.Module): + def __init__( + self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False + ): + super(Decoder, self).__init__() + self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.dropout = nn.Dropout2d(0.1) if dropout else None + + def __call__(self, x, skip=None): + x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) + if skip is not None: + skip = spec_utils.crop_center(skip, x) + x = torch.cat([x, skip], dim=1) + h = self.conv(x) + + if self.dropout is not None: + h = self.dropout(h) + + return h + + +class ASPPModule(nn.Module): + def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU): + super(ASPPModule, self).__init__() + self.conv1 = nn.Sequential( + nn.AdaptiveAvgPool2d((1, None)), + Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), + ) + self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) + self.conv3 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[0], dilations[0], activ=activ + ) + self.conv4 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[1], dilations[1], activ=activ + ) + self.conv5 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.bottleneck = nn.Sequential( + Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) + ) + + def forward(self, x): + _, _, h, w = x.size() + feat1 = F.interpolate( + self.conv1(x), size=(h, w), mode="bilinear", align_corners=True + ) + feat2 = self.conv2(x) + feat3 = self.conv3(x) + feat4 = self.conv4(x) + feat5 = self.conv5(x) + out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) + bottle = self.bottleneck(out) + return bottle diff --git a/uvr5_pack/lib_v5/layers_123812KB .py b/uvr5_pack/lib_v5/layers_123812KB .py new file mode 100644 index 0000000000000000000000000000000000000000..9835dc0f0dd66a7ef3517101180ec2c54eb6011d --- /dev/null +++ b/uvr5_pack/lib_v5/layers_123812KB .py @@ -0,0 +1,118 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import spec_utils + + +class Conv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(Conv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nout, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + bias=False, + ), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class SeperableConv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(SeperableConv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nin, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + groups=nin, + bias=False, + ), + nn.Conv2d(nin, nout, kernel_size=1, bias=False), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class Encoder(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): + super(Encoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) + + def __call__(self, x): + skip = self.conv1(x) + h = self.conv2(skip) + + return h, skip + + +class Decoder(nn.Module): + def __init__( + self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False + ): + super(Decoder, self).__init__() + self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.dropout = nn.Dropout2d(0.1) if dropout else None + + def __call__(self, x, skip=None): + x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) + if skip is not None: + skip = spec_utils.crop_center(skip, x) + x = torch.cat([x, skip], dim=1) + h = self.conv(x) + + if self.dropout is not None: + h = self.dropout(h) + + return h + + +class ASPPModule(nn.Module): + def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU): + super(ASPPModule, self).__init__() + self.conv1 = nn.Sequential( + nn.AdaptiveAvgPool2d((1, None)), + Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), + ) + self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) + self.conv3 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[0], dilations[0], activ=activ + ) + self.conv4 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[1], dilations[1], activ=activ + ) + self.conv5 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.bottleneck = nn.Sequential( + Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) + ) + + def forward(self, x): + _, _, h, w = x.size() + feat1 = F.interpolate( + self.conv1(x), size=(h, w), mode="bilinear", align_corners=True + ) + feat2 = self.conv2(x) + feat3 = self.conv3(x) + feat4 = self.conv4(x) + feat5 = self.conv5(x) + out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) + bottle = self.bottleneck(out) + return bottle diff --git a/uvr5_pack/lib_v5/layers_123821KB.py b/uvr5_pack/lib_v5/layers_123821KB.py new file mode 100644 index 0000000000000000000000000000000000000000..9835dc0f0dd66a7ef3517101180ec2c54eb6011d --- /dev/null +++ b/uvr5_pack/lib_v5/layers_123821KB.py @@ -0,0 +1,118 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import spec_utils + + +class Conv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(Conv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nout, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + bias=False, + ), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class SeperableConv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(SeperableConv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nin, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + groups=nin, + bias=False, + ), + nn.Conv2d(nin, nout, kernel_size=1, bias=False), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class Encoder(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): + super(Encoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) + + def __call__(self, x): + skip = self.conv1(x) + h = self.conv2(skip) + + return h, skip + + +class Decoder(nn.Module): + def __init__( + self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False + ): + super(Decoder, self).__init__() + self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.dropout = nn.Dropout2d(0.1) if dropout else None + + def __call__(self, x, skip=None): + x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) + if skip is not None: + skip = spec_utils.crop_center(skip, x) + x = torch.cat([x, skip], dim=1) + h = self.conv(x) + + if self.dropout is not None: + h = self.dropout(h) + + return h + + +class ASPPModule(nn.Module): + def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU): + super(ASPPModule, self).__init__() + self.conv1 = nn.Sequential( + nn.AdaptiveAvgPool2d((1, None)), + Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), + ) + self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) + self.conv3 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[0], dilations[0], activ=activ + ) + self.conv4 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[1], dilations[1], activ=activ + ) + self.conv5 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.bottleneck = nn.Sequential( + Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) + ) + + def forward(self, x): + _, _, h, w = x.size() + feat1 = F.interpolate( + self.conv1(x), size=(h, w), mode="bilinear", align_corners=True + ) + feat2 = self.conv2(x) + feat3 = self.conv3(x) + feat4 = self.conv4(x) + feat5 = self.conv5(x) + out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) + bottle = self.bottleneck(out) + return bottle diff --git a/uvr5_pack/lib_v5/layers_33966KB.py b/uvr5_pack/lib_v5/layers_33966KB.py new file mode 100644 index 0000000000000000000000000000000000000000..78e539250075d7fed2f349d05e3317dfe2c96804 --- /dev/null +++ b/uvr5_pack/lib_v5/layers_33966KB.py @@ -0,0 +1,126 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import spec_utils + + +class Conv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(Conv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nout, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + bias=False, + ), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class SeperableConv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(SeperableConv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nin, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + groups=nin, + bias=False, + ), + nn.Conv2d(nin, nout, kernel_size=1, bias=False), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class Encoder(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): + super(Encoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) + + def __call__(self, x): + skip = self.conv1(x) + h = self.conv2(skip) + + return h, skip + + +class Decoder(nn.Module): + def __init__( + self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False + ): + super(Decoder, self).__init__() + self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.dropout = nn.Dropout2d(0.1) if dropout else None + + def __call__(self, x, skip=None): + x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) + if skip is not None: + skip = spec_utils.crop_center(skip, x) + x = torch.cat([x, skip], dim=1) + h = self.conv(x) + + if self.dropout is not None: + h = self.dropout(h) + + return h + + +class ASPPModule(nn.Module): + def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): + super(ASPPModule, self).__init__() + self.conv1 = nn.Sequential( + nn.AdaptiveAvgPool2d((1, None)), + Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), + ) + self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) + self.conv3 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[0], dilations[0], activ=activ + ) + self.conv4 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[1], dilations[1], activ=activ + ) + self.conv5 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.conv6 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.conv7 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.bottleneck = nn.Sequential( + Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) + ) + + def forward(self, x): + _, _, h, w = x.size() + feat1 = F.interpolate( + self.conv1(x), size=(h, w), mode="bilinear", align_corners=True + ) + feat2 = self.conv2(x) + feat3 = self.conv3(x) + feat4 = self.conv4(x) + feat5 = self.conv5(x) + feat6 = self.conv6(x) + feat7 = self.conv7(x) + out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) + bottle = self.bottleneck(out) + return bottle diff --git a/uvr5_pack/lib_v5/layers_537227KB.py b/uvr5_pack/lib_v5/layers_537227KB.py new file mode 100644 index 0000000000000000000000000000000000000000..78e539250075d7fed2f349d05e3317dfe2c96804 --- /dev/null +++ b/uvr5_pack/lib_v5/layers_537227KB.py @@ -0,0 +1,126 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import spec_utils + + +class Conv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(Conv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nout, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + bias=False, + ), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class SeperableConv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(SeperableConv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nin, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + groups=nin, + bias=False, + ), + nn.Conv2d(nin, nout, kernel_size=1, bias=False), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class Encoder(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): + super(Encoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) + + def __call__(self, x): + skip = self.conv1(x) + h = self.conv2(skip) + + return h, skip + + +class Decoder(nn.Module): + def __init__( + self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False + ): + super(Decoder, self).__init__() + self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.dropout = nn.Dropout2d(0.1) if dropout else None + + def __call__(self, x, skip=None): + x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) + if skip is not None: + skip = spec_utils.crop_center(skip, x) + x = torch.cat([x, skip], dim=1) + h = self.conv(x) + + if self.dropout is not None: + h = self.dropout(h) + + return h + + +class ASPPModule(nn.Module): + def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): + super(ASPPModule, self).__init__() + self.conv1 = nn.Sequential( + nn.AdaptiveAvgPool2d((1, None)), + Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), + ) + self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) + self.conv3 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[0], dilations[0], activ=activ + ) + self.conv4 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[1], dilations[1], activ=activ + ) + self.conv5 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.conv6 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.conv7 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.bottleneck = nn.Sequential( + Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) + ) + + def forward(self, x): + _, _, h, w = x.size() + feat1 = F.interpolate( + self.conv1(x), size=(h, w), mode="bilinear", align_corners=True + ) + feat2 = self.conv2(x) + feat3 = self.conv3(x) + feat4 = self.conv4(x) + feat5 = self.conv5(x) + feat6 = self.conv6(x) + feat7 = self.conv7(x) + out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) + bottle = self.bottleneck(out) + return bottle diff --git a/uvr5_pack/lib_v5/layers_537238KB.py b/uvr5_pack/lib_v5/layers_537238KB.py new file mode 100644 index 0000000000000000000000000000000000000000..78e539250075d7fed2f349d05e3317dfe2c96804 --- /dev/null +++ b/uvr5_pack/lib_v5/layers_537238KB.py @@ -0,0 +1,126 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import spec_utils + + +class Conv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(Conv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nout, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + bias=False, + ), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class SeperableConv2DBNActiv(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(SeperableConv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, + nin, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + groups=nin, + bias=False, + ), + nn.Conv2d(nin, nout, kernel_size=1, bias=False), + nn.BatchNorm2d(nout), + activ(), + ) + + def __call__(self, x): + return self.conv(x) + + +class Encoder(nn.Module): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): + super(Encoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) + + def __call__(self, x): + skip = self.conv1(x) + h = self.conv2(skip) + + return h, skip + + +class Decoder(nn.Module): + def __init__( + self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False + ): + super(Decoder, self).__init__() + self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.dropout = nn.Dropout2d(0.1) if dropout else None + + def __call__(self, x, skip=None): + x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) + if skip is not None: + skip = spec_utils.crop_center(skip, x) + x = torch.cat([x, skip], dim=1) + h = self.conv(x) + + if self.dropout is not None: + h = self.dropout(h) + + return h + + +class ASPPModule(nn.Module): + def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): + super(ASPPModule, self).__init__() + self.conv1 = nn.Sequential( + nn.AdaptiveAvgPool2d((1, None)), + Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), + ) + self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) + self.conv3 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[0], dilations[0], activ=activ + ) + self.conv4 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[1], dilations[1], activ=activ + ) + self.conv5 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.conv6 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.conv7 = SeperableConv2DBNActiv( + nin, nin, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.bottleneck = nn.Sequential( + Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) + ) + + def forward(self, x): + _, _, h, w = x.size() + feat1 = F.interpolate( + self.conv1(x), size=(h, w), mode="bilinear", align_corners=True + ) + feat2 = self.conv2(x) + feat3 = self.conv3(x) + feat4 = self.conv4(x) + feat5 = self.conv5(x) + feat6 = self.conv6(x) + feat7 = self.conv7(x) + out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) + bottle = self.bottleneck(out) + return bottle diff --git a/uvr5_pack/lib_v5/layers_new.py b/uvr5_pack/lib_v5/layers_new.py new file mode 100644 index 0000000000000000000000000000000000000000..9c5ded32f8a94eb12b34b4ac713daf28a92ec696 --- /dev/null +++ b/uvr5_pack/lib_v5/layers_new.py @@ -0,0 +1,126 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import spec_utils + +class Conv2DBNActiv(nn.Module): + + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(Conv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, nout, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + bias=False), + nn.BatchNorm2d(nout), + activ() + ) + + def __call__(self, x): + return self.conv(x) + +class Encoder(nn.Module): + + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): + super(Encoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ) + self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) + + def __call__(self, x): + h = self.conv1(x) + h = self.conv2(h) + + return h + + +class Decoder(nn.Module): + + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): + super(Decoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + # self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) + self.dropout = nn.Dropout2d(0.1) if dropout else None + + def __call__(self, x, skip=None): + x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) + + if skip is not None: + skip = spec_utils.crop_center(skip, x) + x = torch.cat([x, skip], dim=1) + + h = self.conv1(x) + # h = self.conv2(h) + + if self.dropout is not None: + h = self.dropout(h) + + return h + + +class ASPPModule(nn.Module): + + def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False): + super(ASPPModule, self).__init__() + self.conv1 = nn.Sequential( + nn.AdaptiveAvgPool2d((1, None)), + Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ) + ) + self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ) + self.conv3 = Conv2DBNActiv( + nin, nout, 3, 1, dilations[0], dilations[0], activ=activ + ) + self.conv4 = Conv2DBNActiv( + nin, nout, 3, 1, dilations[1], dilations[1], activ=activ + ) + self.conv5 = Conv2DBNActiv( + nin, nout, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ) + self.dropout = nn.Dropout2d(0.1) if dropout else None + + def forward(self, x): + _, _, h, w = x.size() + feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True) + feat2 = self.conv2(x) + feat3 = self.conv3(x) + feat4 = self.conv4(x) + feat5 = self.conv5(x) + out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) + out = self.bottleneck(out) + + if self.dropout is not None: + out = self.dropout(out) + + return out + + +class LSTMModule(nn.Module): + + def __init__(self, nin_conv, nin_lstm, nout_lstm): + super(LSTMModule, self).__init__() + self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0) + self.lstm = nn.LSTM( + input_size=nin_lstm, + hidden_size=nout_lstm // 2, + bidirectional=True + ) + self.dense = nn.Sequential( + nn.Linear(nout_lstm, nin_lstm), + nn.BatchNorm1d(nin_lstm), + nn.ReLU() + ) + + def forward(self, x): + N, _, nbins, nframes = x.size() + h = self.conv(x)[:, 0] # N, nbins, nframes + h = h.permute(2, 0, 1) # nframes, N, nbins + h, _ = self.lstm(h) + h = self.dense(h.reshape(-1, h.size()[-1])) # nframes * N, nbins + h = h.reshape(nframes, N, 1, nbins) + h = h.permute(1, 2, 3, 0) + + return h diff --git a/uvr5_pack/lib_v5/model_param_init.py b/uvr5_pack/lib_v5/model_param_init.py new file mode 100644 index 0000000000000000000000000000000000000000..b995c0bfb1194746187692e2ab1c2a6dbaaaec6c --- /dev/null +++ b/uvr5_pack/lib_v5/model_param_init.py @@ -0,0 +1,69 @@ +import json +import os +import pathlib + +default_param = {} +default_param["bins"] = 768 +default_param["unstable_bins"] = 9 # training only +default_param["reduction_bins"] = 762 # training only +default_param["sr"] = 44100 +default_param["pre_filter_start"] = 757 +default_param["pre_filter_stop"] = 768 +default_param["band"] = {} + + +default_param["band"][1] = { + "sr": 11025, + "hl": 128, + "n_fft": 960, + "crop_start": 0, + "crop_stop": 245, + "lpf_start": 61, # inference only + "res_type": "polyphase", +} + +default_param["band"][2] = { + "sr": 44100, + "hl": 512, + "n_fft": 1536, + "crop_start": 24, + "crop_stop": 547, + "hpf_start": 81, # inference only + "res_type": "sinc_best", +} + + +def int_keys(d): + r = {} + for k, v in d: + if k.isdigit(): + k = int(k) + r[k] = v + return r + + +class ModelParameters(object): + def __init__(self, config_path=""): + if ".pth" == pathlib.Path(config_path).suffix: + import zipfile + + with zipfile.ZipFile(config_path, "r") as zip: + self.param = json.loads( + zip.read("param.json"), object_pairs_hook=int_keys + ) + elif ".json" == pathlib.Path(config_path).suffix: + with open(config_path, "r") as f: + self.param = json.loads(f.read(), object_pairs_hook=int_keys) + else: + self.param = default_param + + for k in [ + "mid_side", + "mid_side_b", + "mid_side_b2", + "stereo_w", + "stereo_n", + "reverse", + ]: + if not k in self.param: + self.param[k] = False diff --git a/uvr5_pack/lib_v5/modelparams/1band_sr16000_hl512.json b/uvr5_pack/lib_v5/modelparams/1band_sr16000_hl512.json new file mode 100644 index 0000000000000000000000000000000000000000..72cb4499867ad2827185e85687f06fb73d33eced --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/1band_sr16000_hl512.json @@ -0,0 +1,19 @@ +{ + "bins": 1024, + "unstable_bins": 0, + "reduction_bins": 0, + "band": { + "1": { + "sr": 16000, + "hl": 512, + "n_fft": 2048, + "crop_start": 0, + "crop_stop": 1024, + "hpf_start": -1, + "res_type": "sinc_best" + } + }, + "sr": 16000, + "pre_filter_start": 1023, + "pre_filter_stop": 1024 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json b/uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json new file mode 100644 index 0000000000000000000000000000000000000000..3c00ecf0a105e55a6a86a3c32db301a2635b5b41 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json @@ -0,0 +1,19 @@ +{ + "bins": 1024, + "unstable_bins": 0, + "reduction_bins": 0, + "band": { + "1": { + "sr": 32000, + "hl": 512, + "n_fft": 2048, + "crop_start": 0, + "crop_stop": 1024, + "hpf_start": -1, + "res_type": "kaiser_fast" + } + }, + "sr": 32000, + "pre_filter_start": 1000, + "pre_filter_stop": 1021 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json b/uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json new file mode 100644 index 0000000000000000000000000000000000000000..55666ac9a8d0547751fb4b4d3bffb1ee2c956913 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json @@ -0,0 +1,19 @@ +{ + "bins": 1024, + "unstable_bins": 0, + "reduction_bins": 0, + "band": { + "1": { + "sr": 33075, + "hl": 384, + "n_fft": 2048, + "crop_start": 0, + "crop_stop": 1024, + "hpf_start": -1, + "res_type": "sinc_best" + } + }, + "sr": 33075, + "pre_filter_start": 1000, + "pre_filter_stop": 1021 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl1024.json b/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl1024.json new file mode 100644 index 0000000000000000000000000000000000000000..665abe20eb3cc39fe0f8493dad8f25f6ef634a14 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl1024.json @@ -0,0 +1,19 @@ +{ + "bins": 1024, + "unstable_bins": 0, + "reduction_bins": 0, + "band": { + "1": { + "sr": 44100, + "hl": 1024, + "n_fft": 2048, + "crop_start": 0, + "crop_stop": 1024, + "hpf_start": -1, + "res_type": "sinc_best" + } + }, + "sr": 44100, + "pre_filter_start": 1023, + "pre_filter_stop": 1024 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl256.json b/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl256.json new file mode 100644 index 0000000000000000000000000000000000000000..0e8b16f89b0231d06eabe8d2f7c2670c7caa2272 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl256.json @@ -0,0 +1,19 @@ +{ + "bins": 256, + "unstable_bins": 0, + "reduction_bins": 0, + "band": { + "1": { + "sr": 44100, + "hl": 256, + "n_fft": 512, + "crop_start": 0, + "crop_stop": 256, + "hpf_start": -1, + "res_type": "sinc_best" + } + }, + "sr": 44100, + "pre_filter_start": 256, + "pre_filter_stop": 256 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json b/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json new file mode 100644 index 0000000000000000000000000000000000000000..3b38fcaf60ba204e03a47f5bd3f5bcfe75e1983a --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json @@ -0,0 +1,19 @@ +{ + "bins": 1024, + "unstable_bins": 0, + "reduction_bins": 0, + "band": { + "1": { + "sr": 44100, + "hl": 512, + "n_fft": 2048, + "crop_start": 0, + "crop_stop": 1024, + "hpf_start": -1, + "res_type": "sinc_best" + } + }, + "sr": 44100, + "pre_filter_start": 1023, + "pre_filter_stop": 1024 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512_cut.json b/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512_cut.json new file mode 100644 index 0000000000000000000000000000000000000000..630df3524e340f43a1ddb7b33ff02cc91fc1cb47 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512_cut.json @@ -0,0 +1,19 @@ +{ + "bins": 1024, + "unstable_bins": 0, + "reduction_bins": 0, + "band": { + "1": { + "sr": 44100, + "hl": 512, + "n_fft": 2048, + "crop_start": 0, + "crop_stop": 700, + "hpf_start": -1, + "res_type": "sinc_best" + } + }, + "sr": 44100, + "pre_filter_start": 1023, + "pre_filter_stop": 700 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/2band_32000.json b/uvr5_pack/lib_v5/modelparams/2band_32000.json new file mode 100644 index 0000000000000000000000000000000000000000..ab9cf1150a818eb6252105408311be0a40d423b3 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/2band_32000.json @@ -0,0 +1,30 @@ +{ + "bins": 768, + "unstable_bins": 7, + "reduction_bins": 705, + "band": { + "1": { + "sr": 6000, + "hl": 66, + "n_fft": 512, + "crop_start": 0, + "crop_stop": 240, + "lpf_start": 60, + "lpf_stop": 118, + "res_type": "sinc_fastest" + }, + "2": { + "sr": 32000, + "hl": 352, + "n_fft": 1024, + "crop_start": 22, + "crop_stop": 505, + "hpf_start": 44, + "hpf_stop": 23, + "res_type": "sinc_medium" + } + }, + "sr": 32000, + "pre_filter_start": 710, + "pre_filter_stop": 731 +} diff --git a/uvr5_pack/lib_v5/modelparams/2band_44100_lofi.json b/uvr5_pack/lib_v5/modelparams/2band_44100_lofi.json new file mode 100644 index 0000000000000000000000000000000000000000..7faa216d7b49aeece24123dbdd868847a1dbc03c --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/2band_44100_lofi.json @@ -0,0 +1,30 @@ +{ + "bins": 512, + "unstable_bins": 7, + "reduction_bins": 510, + "band": { + "1": { + "sr": 11025, + "hl": 160, + "n_fft": 768, + "crop_start": 0, + "crop_stop": 192, + "lpf_start": 41, + "lpf_stop": 139, + "res_type": "sinc_fastest" + }, + "2": { + "sr": 44100, + "hl": 640, + "n_fft": 1024, + "crop_start": 10, + "crop_stop": 320, + "hpf_start": 47, + "hpf_stop": 15, + "res_type": "sinc_medium" + } + }, + "sr": 44100, + "pre_filter_start": 510, + "pre_filter_stop": 512 +} diff --git a/uvr5_pack/lib_v5/modelparams/2band_48000.json b/uvr5_pack/lib_v5/modelparams/2band_48000.json new file mode 100644 index 0000000000000000000000000000000000000000..7e78175052b09cb1a32345e54006475992712f9a --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/2band_48000.json @@ -0,0 +1,30 @@ +{ + "bins": 768, + "unstable_bins": 7, + "reduction_bins": 705, + "band": { + "1": { + "sr": 6000, + "hl": 66, + "n_fft": 512, + "crop_start": 0, + "crop_stop": 240, + "lpf_start": 60, + "lpf_stop": 240, + "res_type": "sinc_fastest" + }, + "2": { + "sr": 48000, + "hl": 528, + "n_fft": 1536, + "crop_start": 22, + "crop_stop": 505, + "hpf_start": 82, + "hpf_stop": 22, + "res_type": "sinc_medium" + } + }, + "sr": 48000, + "pre_filter_start": 710, + "pre_filter_stop": 731 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/3band_44100.json b/uvr5_pack/lib_v5/modelparams/3band_44100.json new file mode 100644 index 0000000000000000000000000000000000000000..d881d767ff83fbac0e18dfe2587ef16925b29b3c --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/3band_44100.json @@ -0,0 +1,42 @@ +{ + "bins": 768, + "unstable_bins": 5, + "reduction_bins": 733, + "band": { + "1": { + "sr": 11025, + "hl": 128, + "n_fft": 768, + "crop_start": 0, + "crop_stop": 278, + "lpf_start": 28, + "lpf_stop": 140, + "res_type": "polyphase" + }, + "2": { + "sr": 22050, + "hl": 256, + "n_fft": 768, + "crop_start": 14, + "crop_stop": 322, + "hpf_start": 70, + "hpf_stop": 14, + "lpf_start": 283, + "lpf_stop": 314, + "res_type": "polyphase" + }, + "3": { + "sr": 44100, + "hl": 512, + "n_fft": 768, + "crop_start": 131, + "crop_stop": 313, + "hpf_start": 154, + "hpf_stop": 141, + "res_type": "sinc_medium" + } + }, + "sr": 44100, + "pre_filter_start": 757, + "pre_filter_stop": 768 +} diff --git a/uvr5_pack/lib_v5/modelparams/3band_44100_mid.json b/uvr5_pack/lib_v5/modelparams/3band_44100_mid.json new file mode 100644 index 0000000000000000000000000000000000000000..77ec198573b19f36519a028a509767d30764c0e2 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/3band_44100_mid.json @@ -0,0 +1,43 @@ +{ + "mid_side": true, + "bins": 768, + "unstable_bins": 5, + "reduction_bins": 733, + "band": { + "1": { + "sr": 11025, + "hl": 128, + "n_fft": 768, + "crop_start": 0, + "crop_stop": 278, + "lpf_start": 28, + "lpf_stop": 140, + "res_type": "polyphase" + }, + "2": { + "sr": 22050, + "hl": 256, + "n_fft": 768, + "crop_start": 14, + "crop_stop": 322, + "hpf_start": 70, + "hpf_stop": 14, + "lpf_start": 283, + "lpf_stop": 314, + "res_type": "polyphase" + }, + "3": { + "sr": 44100, + "hl": 512, + "n_fft": 768, + "crop_start": 131, + "crop_stop": 313, + "hpf_start": 154, + "hpf_stop": 141, + "res_type": "sinc_medium" + } + }, + "sr": 44100, + "pre_filter_start": 757, + "pre_filter_stop": 768 +} diff --git a/uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json b/uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json new file mode 100644 index 0000000000000000000000000000000000000000..85ee8a7d44541c9176e85ea3dce8728d34990938 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json @@ -0,0 +1,43 @@ +{ + "mid_side_b2": true, + "bins": 640, + "unstable_bins": 7, + "reduction_bins": 565, + "band": { + "1": { + "sr": 11025, + "hl": 108, + "n_fft": 1024, + "crop_start": 0, + "crop_stop": 187, + "lpf_start": 92, + "lpf_stop": 186, + "res_type": "polyphase" + }, + "2": { + "sr": 22050, + "hl": 216, + "n_fft": 768, + "crop_start": 0, + "crop_stop": 212, + "hpf_start": 68, + "hpf_stop": 34, + "lpf_start": 174, + "lpf_stop": 209, + "res_type": "polyphase" + }, + "3": { + "sr": 44100, + "hl": 432, + "n_fft": 640, + "crop_start": 66, + "crop_stop": 307, + "hpf_start": 86, + "hpf_stop": 72, + "res_type": "kaiser_fast" + } + }, + "sr": 44100, + "pre_filter_start": 639, + "pre_filter_stop": 640 +} diff --git a/uvr5_pack/lib_v5/modelparams/4band_44100.json b/uvr5_pack/lib_v5/modelparams/4band_44100.json new file mode 100644 index 0000000000000000000000000000000000000000..df123754204372aa50d464fbe9102a401f48cc73 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/4band_44100.json @@ -0,0 +1,54 @@ +{ + "bins": 768, + "unstable_bins": 7, + "reduction_bins": 668, + "band": { + "1": { + "sr": 11025, + "hl": 128, + "n_fft": 1024, + "crop_start": 0, + "crop_stop": 186, + "lpf_start": 37, + "lpf_stop": 73, + "res_type": "polyphase" + }, + "2": { + "sr": 11025, + "hl": 128, + "n_fft": 512, + "crop_start": 4, + "crop_stop": 185, + "hpf_start": 36, + "hpf_stop": 18, + "lpf_start": 93, + "lpf_stop": 185, + "res_type": "polyphase" + }, + "3": { + "sr": 22050, + "hl": 256, + "n_fft": 512, + "crop_start": 46, + "crop_stop": 186, + "hpf_start": 93, + "hpf_stop": 46, + "lpf_start": 164, + "lpf_stop": 186, + "res_type": "polyphase" + }, + "4": { + "sr": 44100, + "hl": 512, + "n_fft": 768, + "crop_start": 121, + "crop_stop": 382, + "hpf_start": 138, + "hpf_stop": 123, + "res_type": "sinc_medium" + } + }, + "sr": 44100, + "pre_filter_start": 740, + "pre_filter_stop": 768 +} diff --git a/uvr5_pack/lib_v5/modelparams/4band_44100_mid.json b/uvr5_pack/lib_v5/modelparams/4band_44100_mid.json new file mode 100644 index 0000000000000000000000000000000000000000..e91b699eb63d3382c3b9e9edf46d40ed91d6122b --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/4band_44100_mid.json @@ -0,0 +1,55 @@ +{ + "bins": 768, + "unstable_bins": 7, + "mid_side": true, + "reduction_bins": 668, + "band": { + "1": { + "sr": 11025, + "hl": 128, + "n_fft": 1024, + "crop_start": 0, + "crop_stop": 186, + "lpf_start": 37, + "lpf_stop": 73, + "res_type": "polyphase" + }, + "2": { + "sr": 11025, + "hl": 128, + "n_fft": 512, + "crop_start": 4, + "crop_stop": 185, + "hpf_start": 36, + "hpf_stop": 18, + "lpf_start": 93, + "lpf_stop": 185, + "res_type": "polyphase" + }, + "3": { + "sr": 22050, + "hl": 256, + "n_fft": 512, + "crop_start": 46, + "crop_stop": 186, + "hpf_start": 93, + "hpf_stop": 46, + "lpf_start": 164, + "lpf_stop": 186, + "res_type": "polyphase" + }, + "4": { + "sr": 44100, + "hl": 512, + "n_fft": 768, + "crop_start": 121, + "crop_stop": 382, + "hpf_start": 138, + "hpf_stop": 123, + "res_type": "sinc_medium" + } + }, + "sr": 44100, + "pre_filter_start": 740, + "pre_filter_stop": 768 +} diff --git a/uvr5_pack/lib_v5/modelparams/4band_44100_msb.json b/uvr5_pack/lib_v5/modelparams/4band_44100_msb.json new file mode 100644 index 0000000000000000000000000000000000000000..f852f280ec9d98fc1b65cec688290eaafec61b84 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/4band_44100_msb.json @@ -0,0 +1,55 @@ +{ + "mid_side_b": true, + "bins": 768, + "unstable_bins": 7, + "reduction_bins": 668, + "band": { + "1": { + "sr": 11025, + "hl": 128, + "n_fft": 1024, + "crop_start": 0, + "crop_stop": 186, + "lpf_start": 37, + "lpf_stop": 73, + "res_type": "polyphase" + }, + "2": { + "sr": 11025, + "hl": 128, + "n_fft": 512, + "crop_start": 4, + "crop_stop": 185, + "hpf_start": 36, + "hpf_stop": 18, + "lpf_start": 93, + "lpf_stop": 185, + "res_type": "polyphase" + }, + "3": { + "sr": 22050, + "hl": 256, + "n_fft": 512, + "crop_start": 46, + "crop_stop": 186, + "hpf_start": 93, + "hpf_stop": 46, + "lpf_start": 164, + "lpf_stop": 186, + "res_type": "polyphase" + }, + "4": { + "sr": 44100, + "hl": 512, + "n_fft": 768, + "crop_start": 121, + "crop_stop": 382, + "hpf_start": 138, + "hpf_stop": 123, + "res_type": "sinc_medium" + } + }, + "sr": 44100, + "pre_filter_start": 740, + "pre_filter_stop": 768 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/4band_44100_msb2.json b/uvr5_pack/lib_v5/modelparams/4band_44100_msb2.json new file mode 100644 index 0000000000000000000000000000000000000000..f852f280ec9d98fc1b65cec688290eaafec61b84 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/4band_44100_msb2.json @@ -0,0 +1,55 @@ +{ + "mid_side_b": true, + "bins": 768, + "unstable_bins": 7, + "reduction_bins": 668, + "band": { + "1": { + "sr": 11025, + "hl": 128, + "n_fft": 1024, + "crop_start": 0, + "crop_stop": 186, + "lpf_start": 37, + "lpf_stop": 73, + "res_type": "polyphase" + }, + "2": { + "sr": 11025, + "hl": 128, + "n_fft": 512, + "crop_start": 4, + "crop_stop": 185, + "hpf_start": 36, + "hpf_stop": 18, + "lpf_start": 93, + "lpf_stop": 185, + "res_type": "polyphase" + }, + "3": { + "sr": 22050, + "hl": 256, + "n_fft": 512, + "crop_start": 46, + "crop_stop": 186, + "hpf_start": 93, + "hpf_stop": 46, + "lpf_start": 164, + "lpf_stop": 186, + "res_type": "polyphase" + }, + "4": { + "sr": 44100, + "hl": 512, + "n_fft": 768, + "crop_start": 121, + "crop_stop": 382, + "hpf_start": 138, + "hpf_stop": 123, + "res_type": "sinc_medium" + } + }, + "sr": 44100, + "pre_filter_start": 740, + "pre_filter_stop": 768 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/4band_44100_reverse.json b/uvr5_pack/lib_v5/modelparams/4band_44100_reverse.json new file mode 100644 index 0000000000000000000000000000000000000000..7a07d5541bd83dc1caa20b531c3b43a2ffccac88 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/4band_44100_reverse.json @@ -0,0 +1,55 @@ +{ + "reverse": true, + "bins": 768, + "unstable_bins": 7, + "reduction_bins": 668, + "band": { + "1": { + "sr": 11025, + "hl": 128, + "n_fft": 1024, + "crop_start": 0, + "crop_stop": 186, + "lpf_start": 37, + "lpf_stop": 73, + "res_type": "polyphase" + }, + "2": { + "sr": 11025, + "hl": 128, + "n_fft": 512, + "crop_start": 4, + "crop_stop": 185, + "hpf_start": 36, + "hpf_stop": 18, + "lpf_start": 93, + "lpf_stop": 185, + "res_type": "polyphase" + }, + "3": { + "sr": 22050, + "hl": 256, + "n_fft": 512, + "crop_start": 46, + "crop_stop": 186, + "hpf_start": 93, + "hpf_stop": 46, + "lpf_start": 164, + "lpf_stop": 186, + "res_type": "polyphase" + }, + "4": { + "sr": 44100, + "hl": 512, + "n_fft": 768, + "crop_start": 121, + "crop_stop": 382, + "hpf_start": 138, + "hpf_stop": 123, + "res_type": "sinc_medium" + } + }, + "sr": 44100, + "pre_filter_start": 740, + "pre_filter_stop": 768 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/4band_44100_sw.json b/uvr5_pack/lib_v5/modelparams/4band_44100_sw.json new file mode 100644 index 0000000000000000000000000000000000000000..ba0cf342106de793e6ec3e876854c7fd451fbf76 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/4band_44100_sw.json @@ -0,0 +1,55 @@ +{ + "stereo_w": true, + "bins": 768, + "unstable_bins": 7, + "reduction_bins": 668, + "band": { + "1": { + "sr": 11025, + "hl": 128, + "n_fft": 1024, + "crop_start": 0, + "crop_stop": 186, + "lpf_start": 37, + "lpf_stop": 73, + "res_type": "polyphase" + }, + "2": { + "sr": 11025, + "hl": 128, + "n_fft": 512, + "crop_start": 4, + "crop_stop": 185, + "hpf_start": 36, + "hpf_stop": 18, + "lpf_start": 93, + "lpf_stop": 185, + "res_type": "polyphase" + }, + "3": { + "sr": 22050, + "hl": 256, + "n_fft": 512, + "crop_start": 46, + "crop_stop": 186, + "hpf_start": 93, + "hpf_stop": 46, + "lpf_start": 164, + "lpf_stop": 186, + "res_type": "polyphase" + }, + "4": { + "sr": 44100, + "hl": 512, + "n_fft": 768, + "crop_start": 121, + "crop_stop": 382, + "hpf_start": 138, + "hpf_stop": 123, + "res_type": "sinc_medium" + } + }, + "sr": 44100, + "pre_filter_start": 740, + "pre_filter_stop": 768 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/4band_v2.json b/uvr5_pack/lib_v5/modelparams/4band_v2.json new file mode 100644 index 0000000000000000000000000000000000000000..33281a0cf9916fc33558ddfda7a0287a2547faf4 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/4band_v2.json @@ -0,0 +1,54 @@ +{ + "bins": 672, + "unstable_bins": 8, + "reduction_bins": 637, + "band": { + "1": { + "sr": 7350, + "hl": 80, + "n_fft": 640, + "crop_start": 0, + "crop_stop": 85, + "lpf_start": 25, + "lpf_stop": 53, + "res_type": "polyphase" + }, + "2": { + "sr": 7350, + "hl": 80, + "n_fft": 320, + "crop_start": 4, + "crop_stop": 87, + "hpf_start": 25, + "hpf_stop": 12, + "lpf_start": 31, + "lpf_stop": 62, + "res_type": "polyphase" + }, + "3": { + "sr": 14700, + "hl": 160, + "n_fft": 512, + "crop_start": 17, + "crop_stop": 216, + "hpf_start": 48, + "hpf_stop": 24, + "lpf_start": 139, + "lpf_stop": 210, + "res_type": "polyphase" + }, + "4": { + "sr": 44100, + "hl": 480, + "n_fft": 960, + "crop_start": 78, + "crop_stop": 383, + "hpf_start": 130, + "hpf_stop": 86, + "res_type": "kaiser_fast" + } + }, + "sr": 44100, + "pre_filter_start": 668, + "pre_filter_stop": 672 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/4band_v2_sn.json b/uvr5_pack/lib_v5/modelparams/4band_v2_sn.json new file mode 100644 index 0000000000000000000000000000000000000000..2e5c770fe188779bf6b0873190b7a324d6a867b2 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/4band_v2_sn.json @@ -0,0 +1,55 @@ +{ + "bins": 672, + "unstable_bins": 8, + "reduction_bins": 637, + "band": { + "1": { + "sr": 7350, + "hl": 80, + "n_fft": 640, + "crop_start": 0, + "crop_stop": 85, + "lpf_start": 25, + "lpf_stop": 53, + "res_type": "polyphase" + }, + "2": { + "sr": 7350, + "hl": 80, + "n_fft": 320, + "crop_start": 4, + "crop_stop": 87, + "hpf_start": 25, + "hpf_stop": 12, + "lpf_start": 31, + "lpf_stop": 62, + "res_type": "polyphase" + }, + "3": { + "sr": 14700, + "hl": 160, + "n_fft": 512, + "crop_start": 17, + "crop_stop": 216, + "hpf_start": 48, + "hpf_stop": 24, + "lpf_start": 139, + "lpf_stop": 210, + "res_type": "polyphase" + }, + "4": { + "sr": 44100, + "hl": 480, + "n_fft": 960, + "crop_start": 78, + "crop_stop": 383, + "hpf_start": 130, + "hpf_stop": 86, + "convert_channels": "stereo_n", + "res_type": "kaiser_fast" + } + }, + "sr": 44100, + "pre_filter_start": 668, + "pre_filter_stop": 672 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/4band_v3.json b/uvr5_pack/lib_v5/modelparams/4band_v3.json new file mode 100644 index 0000000000000000000000000000000000000000..edb908b8853c6359d1e98ae381888d1a9906ca0f --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/4band_v3.json @@ -0,0 +1,54 @@ +{ + "bins": 672, + "unstable_bins": 8, + "reduction_bins": 530, + "band": { + "1": { + "sr": 7350, + "hl": 80, + "n_fft": 640, + "crop_start": 0, + "crop_stop": 85, + "lpf_start": 25, + "lpf_stop": 53, + "res_type": "polyphase" + }, + "2": { + "sr": 7350, + "hl": 80, + "n_fft": 320, + "crop_start": 4, + "crop_stop": 87, + "hpf_start": 25, + "hpf_stop": 12, + "lpf_start": 31, + "lpf_stop": 62, + "res_type": "polyphase" + }, + "3": { + "sr": 14700, + "hl": 160, + "n_fft": 512, + "crop_start": 17, + "crop_stop": 216, + "hpf_start": 48, + "hpf_stop": 24, + "lpf_start": 139, + "lpf_stop": 210, + "res_type": "polyphase" + }, + "4": { + "sr": 44100, + "hl": 480, + "n_fft": 960, + "crop_start": 78, + "crop_stop": 383, + "hpf_start": 130, + "hpf_stop": 86, + "res_type": "kaiser_fast" + } + }, + "sr": 44100, + "pre_filter_start": 668, + "pre_filter_stop": 672 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/modelparams/ensemble.json b/uvr5_pack/lib_v5/modelparams/ensemble.json new file mode 100644 index 0000000000000000000000000000000000000000..ee69beb46fc82f34619c5e48761e329fcabbbd00 --- /dev/null +++ b/uvr5_pack/lib_v5/modelparams/ensemble.json @@ -0,0 +1,43 @@ +{ + "mid_side_b2": true, + "bins": 1280, + "unstable_bins": 7, + "reduction_bins": 565, + "band": { + "1": { + "sr": 11025, + "hl": 108, + "n_fft": 2048, + "crop_start": 0, + "crop_stop": 374, + "lpf_start": 92, + "lpf_stop": 186, + "res_type": "polyphase" + }, + "2": { + "sr": 22050, + "hl": 216, + "n_fft": 1536, + "crop_start": 0, + "crop_stop": 424, + "hpf_start": 68, + "hpf_stop": 34, + "lpf_start": 348, + "lpf_stop": 418, + "res_type": "polyphase" + }, + "3": { + "sr": 44100, + "hl": 432, + "n_fft": 1280, + "crop_start": 132, + "crop_stop": 614, + "hpf_start": 172, + "hpf_stop": 144, + "res_type": "polyphase" + } + }, + "sr": 44100, + "pre_filter_start": 1280, + "pre_filter_stop": 1280 +} \ No newline at end of file diff --git a/uvr5_pack/lib_v5/nets.py b/uvr5_pack/lib_v5/nets.py new file mode 100644 index 0000000000000000000000000000000000000000..d4c376ed8715f9e85d71609e348add0a6550a4ba --- /dev/null +++ b/uvr5_pack/lib_v5/nets.py @@ -0,0 +1,123 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import layers +from uvr5_pack.lib_v5 import spec_utils + + +class BaseASPPNet(nn.Module): + def __init__(self, nin, ch, dilations=(4, 8, 16)): + super(BaseASPPNet, self).__init__() + self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) + self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) + self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) + self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) + + self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) + + self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) + self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) + self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) + self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) + + def __call__(self, x): + h, e1 = self.enc1(x) + h, e2 = self.enc2(h) + h, e3 = self.enc3(h) + h, e4 = self.enc4(h) + + h = self.aspp(h) + + h = self.dec4(h, e4) + h = self.dec3(h, e3) + h = self.dec2(h, e2) + h = self.dec1(h, e1) + + return h + + +class CascadedASPPNet(nn.Module): + def __init__(self, n_fft): + super(CascadedASPPNet, self).__init__() + self.stg1_low_band_net = BaseASPPNet(2, 16) + self.stg1_high_band_net = BaseASPPNet(2, 16) + + self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0) + self.stg2_full_band_net = BaseASPPNet(8, 16) + + self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) + self.stg3_full_band_net = BaseASPPNet(16, 32) + + self.out = nn.Conv2d(32, 2, 1, bias=False) + self.aux1_out = nn.Conv2d(16, 2, 1, bias=False) + self.aux2_out = nn.Conv2d(16, 2, 1, bias=False) + + self.max_bin = n_fft // 2 + self.output_bin = n_fft // 2 + 1 + + self.offset = 128 + + def forward(self, x, aggressiveness=None): + mix = x.detach() + x = x.clone() + + x = x[:, :, : self.max_bin] + + bandw = x.size()[2] // 2 + aux1 = torch.cat( + [ + self.stg1_low_band_net(x[:, :, :bandw]), + self.stg1_high_band_net(x[:, :, bandw:]), + ], + dim=2, + ) + + h = torch.cat([x, aux1], dim=1) + aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) + + h = torch.cat([x, aux1, aux2], dim=1) + h = self.stg3_full_band_net(self.stg3_bridge(h)) + + mask = torch.sigmoid(self.out(h)) + mask = F.pad( + input=mask, + pad=(0, 0, 0, self.output_bin - mask.size()[2]), + mode="replicate", + ) + + if self.training: + aux1 = torch.sigmoid(self.aux1_out(aux1)) + aux1 = F.pad( + input=aux1, + pad=(0, 0, 0, self.output_bin - aux1.size()[2]), + mode="replicate", + ) + aux2 = torch.sigmoid(self.aux2_out(aux2)) + aux2 = F.pad( + input=aux2, + pad=(0, 0, 0, self.output_bin - aux2.size()[2]), + mode="replicate", + ) + return mask * mix, aux1 * mix, aux2 * mix + else: + if aggressiveness: + mask[:, :, : aggressiveness["split_bin"]] = torch.pow( + mask[:, :, : aggressiveness["split_bin"]], + 1 + aggressiveness["value"] / 3, + ) + mask[:, :, aggressiveness["split_bin"] :] = torch.pow( + mask[:, :, aggressiveness["split_bin"] :], + 1 + aggressiveness["value"], + ) + + return mask * mix + + def predict(self, x_mag, aggressiveness=None): + h = self.forward(x_mag, aggressiveness) + + if self.offset > 0: + h = h[:, :, :, self.offset : -self.offset] + assert h.size()[3] > 0 + + return h diff --git a/uvr5_pack/lib_v5/nets_123812KB.py b/uvr5_pack/lib_v5/nets_123812KB.py new file mode 100644 index 0000000000000000000000000000000000000000..ea6c45c968d66c75e577e8a0fcca9bf800eb4ed6 --- /dev/null +++ b/uvr5_pack/lib_v5/nets_123812KB.py @@ -0,0 +1,122 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import layers_123821KB as layers + + +class BaseASPPNet(nn.Module): + def __init__(self, nin, ch, dilations=(4, 8, 16)): + super(BaseASPPNet, self).__init__() + self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) + self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) + self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) + self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) + + self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) + + self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) + self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) + self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) + self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) + + def __call__(self, x): + h, e1 = self.enc1(x) + h, e2 = self.enc2(h) + h, e3 = self.enc3(h) + h, e4 = self.enc4(h) + + h = self.aspp(h) + + h = self.dec4(h, e4) + h = self.dec3(h, e3) + h = self.dec2(h, e2) + h = self.dec1(h, e1) + + return h + + +class CascadedASPPNet(nn.Module): + def __init__(self, n_fft): + super(CascadedASPPNet, self).__init__() + self.stg1_low_band_net = BaseASPPNet(2, 32) + self.stg1_high_band_net = BaseASPPNet(2, 32) + + self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) + self.stg2_full_band_net = BaseASPPNet(16, 32) + + self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) + self.stg3_full_band_net = BaseASPPNet(32, 64) + + self.out = nn.Conv2d(64, 2, 1, bias=False) + self.aux1_out = nn.Conv2d(32, 2, 1, bias=False) + self.aux2_out = nn.Conv2d(32, 2, 1, bias=False) + + self.max_bin = n_fft // 2 + self.output_bin = n_fft // 2 + 1 + + self.offset = 128 + + def forward(self, x, aggressiveness=None): + mix = x.detach() + x = x.clone() + + x = x[:, :, : self.max_bin] + + bandw = x.size()[2] // 2 + aux1 = torch.cat( + [ + self.stg1_low_band_net(x[:, :, :bandw]), + self.stg1_high_band_net(x[:, :, bandw:]), + ], + dim=2, + ) + + h = torch.cat([x, aux1], dim=1) + aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) + + h = torch.cat([x, aux1, aux2], dim=1) + h = self.stg3_full_band_net(self.stg3_bridge(h)) + + mask = torch.sigmoid(self.out(h)) + mask = F.pad( + input=mask, + pad=(0, 0, 0, self.output_bin - mask.size()[2]), + mode="replicate", + ) + + if self.training: + aux1 = torch.sigmoid(self.aux1_out(aux1)) + aux1 = F.pad( + input=aux1, + pad=(0, 0, 0, self.output_bin - aux1.size()[2]), + mode="replicate", + ) + aux2 = torch.sigmoid(self.aux2_out(aux2)) + aux2 = F.pad( + input=aux2, + pad=(0, 0, 0, self.output_bin - aux2.size()[2]), + mode="replicate", + ) + return mask * mix, aux1 * mix, aux2 * mix + else: + if aggressiveness: + mask[:, :, : aggressiveness["split_bin"]] = torch.pow( + mask[:, :, : aggressiveness["split_bin"]], + 1 + aggressiveness["value"] / 3, + ) + mask[:, :, aggressiveness["split_bin"] :] = torch.pow( + mask[:, :, aggressiveness["split_bin"] :], + 1 + aggressiveness["value"], + ) + + return mask * mix + + def predict(self, x_mag, aggressiveness=None): + h = self.forward(x_mag, aggressiveness) + + if self.offset > 0: + h = h[:, :, :, self.offset : -self.offset] + assert h.size()[3] > 0 + + return h diff --git a/uvr5_pack/lib_v5/nets_123821KB.py b/uvr5_pack/lib_v5/nets_123821KB.py new file mode 100644 index 0000000000000000000000000000000000000000..ea6c45c968d66c75e577e8a0fcca9bf800eb4ed6 --- /dev/null +++ b/uvr5_pack/lib_v5/nets_123821KB.py @@ -0,0 +1,122 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import layers_123821KB as layers + + +class BaseASPPNet(nn.Module): + def __init__(self, nin, ch, dilations=(4, 8, 16)): + super(BaseASPPNet, self).__init__() + self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) + self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) + self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) + self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) + + self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) + + self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) + self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) + self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) + self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) + + def __call__(self, x): + h, e1 = self.enc1(x) + h, e2 = self.enc2(h) + h, e3 = self.enc3(h) + h, e4 = self.enc4(h) + + h = self.aspp(h) + + h = self.dec4(h, e4) + h = self.dec3(h, e3) + h = self.dec2(h, e2) + h = self.dec1(h, e1) + + return h + + +class CascadedASPPNet(nn.Module): + def __init__(self, n_fft): + super(CascadedASPPNet, self).__init__() + self.stg1_low_band_net = BaseASPPNet(2, 32) + self.stg1_high_band_net = BaseASPPNet(2, 32) + + self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) + self.stg2_full_band_net = BaseASPPNet(16, 32) + + self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) + self.stg3_full_band_net = BaseASPPNet(32, 64) + + self.out = nn.Conv2d(64, 2, 1, bias=False) + self.aux1_out = nn.Conv2d(32, 2, 1, bias=False) + self.aux2_out = nn.Conv2d(32, 2, 1, bias=False) + + self.max_bin = n_fft // 2 + self.output_bin = n_fft // 2 + 1 + + self.offset = 128 + + def forward(self, x, aggressiveness=None): + mix = x.detach() + x = x.clone() + + x = x[:, :, : self.max_bin] + + bandw = x.size()[2] // 2 + aux1 = torch.cat( + [ + self.stg1_low_band_net(x[:, :, :bandw]), + self.stg1_high_band_net(x[:, :, bandw:]), + ], + dim=2, + ) + + h = torch.cat([x, aux1], dim=1) + aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) + + h = torch.cat([x, aux1, aux2], dim=1) + h = self.stg3_full_band_net(self.stg3_bridge(h)) + + mask = torch.sigmoid(self.out(h)) + mask = F.pad( + input=mask, + pad=(0, 0, 0, self.output_bin - mask.size()[2]), + mode="replicate", + ) + + if self.training: + aux1 = torch.sigmoid(self.aux1_out(aux1)) + aux1 = F.pad( + input=aux1, + pad=(0, 0, 0, self.output_bin - aux1.size()[2]), + mode="replicate", + ) + aux2 = torch.sigmoid(self.aux2_out(aux2)) + aux2 = F.pad( + input=aux2, + pad=(0, 0, 0, self.output_bin - aux2.size()[2]), + mode="replicate", + ) + return mask * mix, aux1 * mix, aux2 * mix + else: + if aggressiveness: + mask[:, :, : aggressiveness["split_bin"]] = torch.pow( + mask[:, :, : aggressiveness["split_bin"]], + 1 + aggressiveness["value"] / 3, + ) + mask[:, :, aggressiveness["split_bin"] :] = torch.pow( + mask[:, :, aggressiveness["split_bin"] :], + 1 + aggressiveness["value"], + ) + + return mask * mix + + def predict(self, x_mag, aggressiveness=None): + h = self.forward(x_mag, aggressiveness) + + if self.offset > 0: + h = h[:, :, :, self.offset : -self.offset] + assert h.size()[3] > 0 + + return h diff --git a/uvr5_pack/lib_v5/nets_33966KB.py b/uvr5_pack/lib_v5/nets_33966KB.py new file mode 100644 index 0000000000000000000000000000000000000000..d2bddb14716202fee3f3b0b461ff335741d04ba8 --- /dev/null +++ b/uvr5_pack/lib_v5/nets_33966KB.py @@ -0,0 +1,122 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import layers_33966KB as layers + + +class BaseASPPNet(nn.Module): + def __init__(self, nin, ch, dilations=(4, 8, 16, 32)): + super(BaseASPPNet, self).__init__() + self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) + self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) + self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) + self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) + + self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) + + self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) + self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) + self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) + self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) + + def __call__(self, x): + h, e1 = self.enc1(x) + h, e2 = self.enc2(h) + h, e3 = self.enc3(h) + h, e4 = self.enc4(h) + + h = self.aspp(h) + + h = self.dec4(h, e4) + h = self.dec3(h, e3) + h = self.dec2(h, e2) + h = self.dec1(h, e1) + + return h + + +class CascadedASPPNet(nn.Module): + def __init__(self, n_fft): + super(CascadedASPPNet, self).__init__() + self.stg1_low_band_net = BaseASPPNet(2, 16) + self.stg1_high_band_net = BaseASPPNet(2, 16) + + self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0) + self.stg2_full_band_net = BaseASPPNet(8, 16) + + self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) + self.stg3_full_band_net = BaseASPPNet(16, 32) + + self.out = nn.Conv2d(32, 2, 1, bias=False) + self.aux1_out = nn.Conv2d(16, 2, 1, bias=False) + self.aux2_out = nn.Conv2d(16, 2, 1, bias=False) + + self.max_bin = n_fft // 2 + self.output_bin = n_fft // 2 + 1 + + self.offset = 128 + + def forward(self, x, aggressiveness=None): + mix = x.detach() + x = x.clone() + + x = x[:, :, : self.max_bin] + + bandw = x.size()[2] // 2 + aux1 = torch.cat( + [ + self.stg1_low_band_net(x[:, :, :bandw]), + self.stg1_high_band_net(x[:, :, bandw:]), + ], + dim=2, + ) + + h = torch.cat([x, aux1], dim=1) + aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) + + h = torch.cat([x, aux1, aux2], dim=1) + h = self.stg3_full_band_net(self.stg3_bridge(h)) + + mask = torch.sigmoid(self.out(h)) + mask = F.pad( + input=mask, + pad=(0, 0, 0, self.output_bin - mask.size()[2]), + mode="replicate", + ) + + if self.training: + aux1 = torch.sigmoid(self.aux1_out(aux1)) + aux1 = F.pad( + input=aux1, + pad=(0, 0, 0, self.output_bin - aux1.size()[2]), + mode="replicate", + ) + aux2 = torch.sigmoid(self.aux2_out(aux2)) + aux2 = F.pad( + input=aux2, + pad=(0, 0, 0, self.output_bin - aux2.size()[2]), + mode="replicate", + ) + return mask * mix, aux1 * mix, aux2 * mix + else: + if aggressiveness: + mask[:, :, : aggressiveness["split_bin"]] = torch.pow( + mask[:, :, : aggressiveness["split_bin"]], + 1 + aggressiveness["value"] / 3, + ) + mask[:, :, aggressiveness["split_bin"] :] = torch.pow( + mask[:, :, aggressiveness["split_bin"] :], + 1 + aggressiveness["value"], + ) + + return mask * mix + + def predict(self, x_mag, aggressiveness=None): + h = self.forward(x_mag, aggressiveness) + + if self.offset > 0: + h = h[:, :, :, self.offset : -self.offset] + assert h.size()[3] > 0 + + return h diff --git a/uvr5_pack/lib_v5/nets_537227KB.py b/uvr5_pack/lib_v5/nets_537227KB.py new file mode 100644 index 0000000000000000000000000000000000000000..1ceac4a470ca311d594818d52e5f96919cfddb26 --- /dev/null +++ b/uvr5_pack/lib_v5/nets_537227KB.py @@ -0,0 +1,123 @@ +import torch +import numpy as np +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import layers_537238KB as layers + + +class BaseASPPNet(nn.Module): + def __init__(self, nin, ch, dilations=(4, 8, 16)): + super(BaseASPPNet, self).__init__() + self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) + self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) + self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) + self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) + + self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) + + self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) + self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) + self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) + self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) + + def __call__(self, x): + h, e1 = self.enc1(x) + h, e2 = self.enc2(h) + h, e3 = self.enc3(h) + h, e4 = self.enc4(h) + + h = self.aspp(h) + + h = self.dec4(h, e4) + h = self.dec3(h, e3) + h = self.dec2(h, e2) + h = self.dec1(h, e1) + + return h + + +class CascadedASPPNet(nn.Module): + def __init__(self, n_fft): + super(CascadedASPPNet, self).__init__() + self.stg1_low_band_net = BaseASPPNet(2, 64) + self.stg1_high_band_net = BaseASPPNet(2, 64) + + self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) + self.stg2_full_band_net = BaseASPPNet(32, 64) + + self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0) + self.stg3_full_band_net = BaseASPPNet(64, 128) + + self.out = nn.Conv2d(128, 2, 1, bias=False) + self.aux1_out = nn.Conv2d(64, 2, 1, bias=False) + self.aux2_out = nn.Conv2d(64, 2, 1, bias=False) + + self.max_bin = n_fft // 2 + self.output_bin = n_fft // 2 + 1 + + self.offset = 128 + + def forward(self, x, aggressiveness=None): + mix = x.detach() + x = x.clone() + + x = x[:, :, : self.max_bin] + + bandw = x.size()[2] // 2 + aux1 = torch.cat( + [ + self.stg1_low_band_net(x[:, :, :bandw]), + self.stg1_high_band_net(x[:, :, bandw:]), + ], + dim=2, + ) + + h = torch.cat([x, aux1], dim=1) + aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) + + h = torch.cat([x, aux1, aux2], dim=1) + h = self.stg3_full_band_net(self.stg3_bridge(h)) + + mask = torch.sigmoid(self.out(h)) + mask = F.pad( + input=mask, + pad=(0, 0, 0, self.output_bin - mask.size()[2]), + mode="replicate", + ) + + if self.training: + aux1 = torch.sigmoid(self.aux1_out(aux1)) + aux1 = F.pad( + input=aux1, + pad=(0, 0, 0, self.output_bin - aux1.size()[2]), + mode="replicate", + ) + aux2 = torch.sigmoid(self.aux2_out(aux2)) + aux2 = F.pad( + input=aux2, + pad=(0, 0, 0, self.output_bin - aux2.size()[2]), + mode="replicate", + ) + return mask * mix, aux1 * mix, aux2 * mix + else: + if aggressiveness: + mask[:, :, : aggressiveness["split_bin"]] = torch.pow( + mask[:, :, : aggressiveness["split_bin"]], + 1 + aggressiveness["value"] / 3, + ) + mask[:, :, aggressiveness["split_bin"] :] = torch.pow( + mask[:, :, aggressiveness["split_bin"] :], + 1 + aggressiveness["value"], + ) + + return mask * mix + + def predict(self, x_mag, aggressiveness=None): + h = self.forward(x_mag, aggressiveness) + + if self.offset > 0: + h = h[:, :, :, self.offset : -self.offset] + assert h.size()[3] > 0 + + return h diff --git a/uvr5_pack/lib_v5/nets_537238KB.py b/uvr5_pack/lib_v5/nets_537238KB.py new file mode 100644 index 0000000000000000000000000000000000000000..1ceac4a470ca311d594818d52e5f96919cfddb26 --- /dev/null +++ b/uvr5_pack/lib_v5/nets_537238KB.py @@ -0,0 +1,123 @@ +import torch +import numpy as np +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import layers_537238KB as layers + + +class BaseASPPNet(nn.Module): + def __init__(self, nin, ch, dilations=(4, 8, 16)): + super(BaseASPPNet, self).__init__() + self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) + self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) + self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) + self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) + + self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) + + self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) + self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) + self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) + self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) + + def __call__(self, x): + h, e1 = self.enc1(x) + h, e2 = self.enc2(h) + h, e3 = self.enc3(h) + h, e4 = self.enc4(h) + + h = self.aspp(h) + + h = self.dec4(h, e4) + h = self.dec3(h, e3) + h = self.dec2(h, e2) + h = self.dec1(h, e1) + + return h + + +class CascadedASPPNet(nn.Module): + def __init__(self, n_fft): + super(CascadedASPPNet, self).__init__() + self.stg1_low_band_net = BaseASPPNet(2, 64) + self.stg1_high_band_net = BaseASPPNet(2, 64) + + self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) + self.stg2_full_band_net = BaseASPPNet(32, 64) + + self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0) + self.stg3_full_band_net = BaseASPPNet(64, 128) + + self.out = nn.Conv2d(128, 2, 1, bias=False) + self.aux1_out = nn.Conv2d(64, 2, 1, bias=False) + self.aux2_out = nn.Conv2d(64, 2, 1, bias=False) + + self.max_bin = n_fft // 2 + self.output_bin = n_fft // 2 + 1 + + self.offset = 128 + + def forward(self, x, aggressiveness=None): + mix = x.detach() + x = x.clone() + + x = x[:, :, : self.max_bin] + + bandw = x.size()[2] // 2 + aux1 = torch.cat( + [ + self.stg1_low_band_net(x[:, :, :bandw]), + self.stg1_high_band_net(x[:, :, bandw:]), + ], + dim=2, + ) + + h = torch.cat([x, aux1], dim=1) + aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) + + h = torch.cat([x, aux1, aux2], dim=1) + h = self.stg3_full_band_net(self.stg3_bridge(h)) + + mask = torch.sigmoid(self.out(h)) + mask = F.pad( + input=mask, + pad=(0, 0, 0, self.output_bin - mask.size()[2]), + mode="replicate", + ) + + if self.training: + aux1 = torch.sigmoid(self.aux1_out(aux1)) + aux1 = F.pad( + input=aux1, + pad=(0, 0, 0, self.output_bin - aux1.size()[2]), + mode="replicate", + ) + aux2 = torch.sigmoid(self.aux2_out(aux2)) + aux2 = F.pad( + input=aux2, + pad=(0, 0, 0, self.output_bin - aux2.size()[2]), + mode="replicate", + ) + return mask * mix, aux1 * mix, aux2 * mix + else: + if aggressiveness: + mask[:, :, : aggressiveness["split_bin"]] = torch.pow( + mask[:, :, : aggressiveness["split_bin"]], + 1 + aggressiveness["value"] / 3, + ) + mask[:, :, aggressiveness["split_bin"] :] = torch.pow( + mask[:, :, aggressiveness["split_bin"] :], + 1 + aggressiveness["value"], + ) + + return mask * mix + + def predict(self, x_mag, aggressiveness=None): + h = self.forward(x_mag, aggressiveness) + + if self.offset > 0: + h = h[:, :, :, self.offset : -self.offset] + assert h.size()[3] > 0 + + return h diff --git a/uvr5_pack/lib_v5/nets_61968KB.py b/uvr5_pack/lib_v5/nets_61968KB.py new file mode 100644 index 0000000000000000000000000000000000000000..ea6c45c968d66c75e577e8a0fcca9bf800eb4ed6 --- /dev/null +++ b/uvr5_pack/lib_v5/nets_61968KB.py @@ -0,0 +1,122 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from uvr5_pack.lib_v5 import layers_123821KB as layers + + +class BaseASPPNet(nn.Module): + def __init__(self, nin, ch, dilations=(4, 8, 16)): + super(BaseASPPNet, self).__init__() + self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) + self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) + self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) + self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) + + self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) + + self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) + self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) + self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) + self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) + + def __call__(self, x): + h, e1 = self.enc1(x) + h, e2 = self.enc2(h) + h, e3 = self.enc3(h) + h, e4 = self.enc4(h) + + h = self.aspp(h) + + h = self.dec4(h, e4) + h = self.dec3(h, e3) + h = self.dec2(h, e2) + h = self.dec1(h, e1) + + return h + + +class CascadedASPPNet(nn.Module): + def __init__(self, n_fft): + super(CascadedASPPNet, self).__init__() + self.stg1_low_band_net = BaseASPPNet(2, 32) + self.stg1_high_band_net = BaseASPPNet(2, 32) + + self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) + self.stg2_full_band_net = BaseASPPNet(16, 32) + + self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) + self.stg3_full_band_net = BaseASPPNet(32, 64) + + self.out = nn.Conv2d(64, 2, 1, bias=False) + self.aux1_out = nn.Conv2d(32, 2, 1, bias=False) + self.aux2_out = nn.Conv2d(32, 2, 1, bias=False) + + self.max_bin = n_fft // 2 + self.output_bin = n_fft // 2 + 1 + + self.offset = 128 + + def forward(self, x, aggressiveness=None): + mix = x.detach() + x = x.clone() + + x = x[:, :, : self.max_bin] + + bandw = x.size()[2] // 2 + aux1 = torch.cat( + [ + self.stg1_low_band_net(x[:, :, :bandw]), + self.stg1_high_band_net(x[:, :, bandw:]), + ], + dim=2, + ) + + h = torch.cat([x, aux1], dim=1) + aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) + + h = torch.cat([x, aux1, aux2], dim=1) + h = self.stg3_full_band_net(self.stg3_bridge(h)) + + mask = torch.sigmoid(self.out(h)) + mask = F.pad( + input=mask, + pad=(0, 0, 0, self.output_bin - mask.size()[2]), + mode="replicate", + ) + + if self.training: + aux1 = torch.sigmoid(self.aux1_out(aux1)) + aux1 = F.pad( + input=aux1, + pad=(0, 0, 0, self.output_bin - aux1.size()[2]), + mode="replicate", + ) + aux2 = torch.sigmoid(self.aux2_out(aux2)) + aux2 = F.pad( + input=aux2, + pad=(0, 0, 0, self.output_bin - aux2.size()[2]), + mode="replicate", + ) + return mask * mix, aux1 * mix, aux2 * mix + else: + if aggressiveness: + mask[:, :, : aggressiveness["split_bin"]] = torch.pow( + mask[:, :, : aggressiveness["split_bin"]], + 1 + aggressiveness["value"] / 3, + ) + mask[:, :, aggressiveness["split_bin"] :] = torch.pow( + mask[:, :, aggressiveness["split_bin"] :], + 1 + aggressiveness["value"], + ) + + return mask * mix + + def predict(self, x_mag, aggressiveness=None): + h = self.forward(x_mag, aggressiveness) + + if self.offset > 0: + h = h[:, :, :, self.offset : -self.offset] + assert h.size()[3] > 0 + + return h diff --git a/uvr5_pack/lib_v5/nets_new.py b/uvr5_pack/lib_v5/nets_new.py new file mode 100644 index 0000000000000000000000000000000000000000..1c50bf895250170611a0df996a5034b8f90c2220 --- /dev/null +++ b/uvr5_pack/lib_v5/nets_new.py @@ -0,0 +1,124 @@ +import torch +from torch import nn +import torch.nn.functional as F +from uvr5_pack.lib_v5 import layers_new as layers + +class BaseNet(nn.Module): + + def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))): + super(BaseNet, self).__init__() + self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1) + self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1) + self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1) + self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1) + self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1) + + self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True) + + self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1) + self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1) + self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1) + self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm) + self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1) + + def __call__(self, x): + e1 = self.enc1(x) + e2 = self.enc2(e1) + e3 = self.enc3(e2) + e4 = self.enc4(e3) + e5 = self.enc5(e4) + + h = self.aspp(e5) + + h = self.dec4(h, e4) + h = self.dec3(h, e3) + h = self.dec2(h, e2) + h = torch.cat([h, self.lstm_dec2(h)], dim=1) + h = self.dec1(h, e1) + + return h + +class CascadedNet(nn.Module): + + def __init__(self, n_fft, nout=32, nout_lstm=128): + super(CascadedNet, self).__init__() + + self.max_bin = n_fft // 2 + self.output_bin = n_fft // 2 + 1 + self.nin_lstm = self.max_bin // 2 + self.offset = 64 + + self.stg1_low_band_net = nn.Sequential( + BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm), + layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0) + ) + + self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2) + + self.stg2_low_band_net = nn.Sequential( + BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm), + layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0) + ) + self.stg2_high_band_net = BaseNet(nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2) + + self.stg3_full_band_net = BaseNet(3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm) + + self.out = nn.Conv2d(nout, 2, 1, bias=False) + self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False) + + def forward(self, x): + x = x[:, :, :self.max_bin] + + bandw = x.size()[2] // 2 + l1_in = x[:, :, :bandw] + h1_in = x[:, :, bandw:] + l1 = self.stg1_low_band_net(l1_in) + h1 = self.stg1_high_band_net(h1_in) + aux1 = torch.cat([l1, h1], dim=2) + + l2_in = torch.cat([l1_in, l1], dim=1) + h2_in = torch.cat([h1_in, h1], dim=1) + l2 = self.stg2_low_band_net(l2_in) + h2 = self.stg2_high_band_net(h2_in) + aux2 = torch.cat([l2, h2], dim=2) + + f3_in = torch.cat([x, aux1, aux2], dim=1) + f3 = self.stg3_full_band_net(f3_in) + + mask = torch.sigmoid(self.out(f3)) + mask = F.pad( + input=mask, + pad=(0, 0, 0, self.output_bin - mask.size()[2]), + mode='replicate' + ) + + if self.training: + aux = torch.cat([aux1, aux2], dim=1) + aux = torch.sigmoid(self.aux_out(aux)) + aux = F.pad( + input=aux, + pad=(0, 0, 0, self.output_bin - aux.size()[2]), + mode='replicate' + ) + return mask, aux + else: + return mask + + def predict_mask(self, x): + mask = self.forward(x) + + if self.offset > 0: + mask = mask[:, :, :, self.offset:-self.offset] + assert mask.size()[3] > 0 + + return mask + + def predict(self, x,aggressiveness=None): + mask = self.forward(x) + pred_mag = x * mask + + if self.offset > 0: + pred_mag = pred_mag[:, :, :, self.offset:-self.offset] + assert pred_mag.size()[3] > 0 + + return pred_mag diff --git a/uvr5_pack/lib_v5/spec_utils.py b/uvr5_pack/lib_v5/spec_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a3fd46d333da7becc7f09f42c084ac7cde661035 --- /dev/null +++ b/uvr5_pack/lib_v5/spec_utils.py @@ -0,0 +1,667 @@ +import os, librosa +import numpy as np +import soundfile as sf +from tqdm import tqdm +import json, math, hashlib + + +def crop_center(h1, h2): + h1_shape = h1.size() + h2_shape = h2.size() + + if h1_shape[3] == h2_shape[3]: + return h1 + elif h1_shape[3] < h2_shape[3]: + raise ValueError("h1_shape[3] must be greater than h2_shape[3]") + + # s_freq = (h2_shape[2] - h1_shape[2]) // 2 + # e_freq = s_freq + h1_shape[2] + s_time = (h1_shape[3] - h2_shape[3]) // 2 + e_time = s_time + h2_shape[3] + h1 = h1[:, :, :, s_time:e_time] + + return h1 + + +def wave_to_spectrogram( + wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False +): + if reverse: + wave_left = np.flip(np.asfortranarray(wave[0])) + wave_right = np.flip(np.asfortranarray(wave[1])) + elif mid_side: + wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2) + wave_right = np.asfortranarray(np.subtract(wave[0], wave[1])) + elif mid_side_b2: + wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5)) + wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5)) + else: + wave_left = np.asfortranarray(wave[0]) + wave_right = np.asfortranarray(wave[1]) + + spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length) + spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length) + + spec = np.asfortranarray([spec_left, spec_right]) + + return spec + + +def wave_to_spectrogram_mt( + wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False +): + import threading + + if reverse: + wave_left = np.flip(np.asfortranarray(wave[0])) + wave_right = np.flip(np.asfortranarray(wave[1])) + elif mid_side: + wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2) + wave_right = np.asfortranarray(np.subtract(wave[0], wave[1])) + elif mid_side_b2: + wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5)) + wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5)) + else: + wave_left = np.asfortranarray(wave[0]) + wave_right = np.asfortranarray(wave[1]) + + def run_thread(**kwargs): + global spec_left + spec_left = librosa.stft(**kwargs) + + thread = threading.Thread( + target=run_thread, + kwargs={"y": wave_left, "n_fft": n_fft, "hop_length": hop_length}, + ) + thread.start() + spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length) + thread.join() + + spec = np.asfortranarray([spec_left, spec_right]) + + return spec + + +def combine_spectrograms(specs, mp): + l = min([specs[i].shape[2] for i in specs]) + spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64) + offset = 0 + bands_n = len(mp.param["band"]) + + for d in range(1, bands_n + 1): + h = mp.param["band"][d]["crop_stop"] - mp.param["band"][d]["crop_start"] + spec_c[:, offset : offset + h, :l] = specs[d][ + :, mp.param["band"][d]["crop_start"] : mp.param["band"][d]["crop_stop"], :l + ] + offset += h + + if offset > mp.param["bins"]: + raise ValueError("Too much bins") + + # lowpass fiter + if ( + mp.param["pre_filter_start"] > 0 + ): # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']: + if bands_n == 1: + spec_c = fft_lp_filter( + spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"] + ) + else: + gp = 1 + for b in range( + mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"] + ): + g = math.pow( + 10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0 + ) + gp = g + spec_c[:, b, :] *= g + + return np.asfortranarray(spec_c) + + +def spectrogram_to_image(spec, mode="magnitude"): + if mode == "magnitude": + if np.iscomplexobj(spec): + y = np.abs(spec) + else: + y = spec + y = np.log10(y**2 + 1e-8) + elif mode == "phase": + if np.iscomplexobj(spec): + y = np.angle(spec) + else: + y = spec + + y -= y.min() + y *= 255 / y.max() + img = np.uint8(y) + + if y.ndim == 3: + img = img.transpose(1, 2, 0) + img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2) + + return img + + +def reduce_vocal_aggressively(X, y, softmask): + v = X - y + y_mag_tmp = np.abs(y) + v_mag_tmp = np.abs(v) + + v_mask = v_mag_tmp > y_mag_tmp + y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf) + + return y_mag * np.exp(1.0j * np.angle(y)) + + +def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32): + if min_range < fade_size * 2: + raise ValueError("min_range must be >= fade_area * 2") + + mag = mag.copy() + + idx = np.where(ref.mean(axis=(0, 1)) < thres)[0] + starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0]) + ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1]) + uninformative = np.where(ends - starts > min_range)[0] + if len(uninformative) > 0: + starts = starts[uninformative] + ends = ends[uninformative] + old_e = None + for s, e in zip(starts, ends): + if old_e is not None and s - old_e < fade_size: + s = old_e - fade_size * 2 + + if s != 0: + weight = np.linspace(0, 1, fade_size) + mag[:, :, s : s + fade_size] += weight * ref[:, :, s : s + fade_size] + else: + s -= fade_size + + if e != mag.shape[2]: + weight = np.linspace(1, 0, fade_size) + mag[:, :, e - fade_size : e] += weight * ref[:, :, e - fade_size : e] + else: + e += fade_size + + mag[:, :, s + fade_size : e - fade_size] += ref[ + :, :, s + fade_size : e - fade_size + ] + old_e = e + + return mag + + +def align_wave_head_and_tail(a, b): + l = min([a[0].size, b[0].size]) + + return a[:l, :l], b[:l, :l] + + +def cache_or_load(mix_path, inst_path, mp): + mix_basename = os.path.splitext(os.path.basename(mix_path))[0] + inst_basename = os.path.splitext(os.path.basename(inst_path))[0] + + cache_dir = "mph{}".format( + hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode("utf-8")).hexdigest() + ) + mix_cache_dir = os.path.join("cache", cache_dir) + inst_cache_dir = os.path.join("cache", cache_dir) + + os.makedirs(mix_cache_dir, exist_ok=True) + os.makedirs(inst_cache_dir, exist_ok=True) + + mix_cache_path = os.path.join(mix_cache_dir, mix_basename + ".npy") + inst_cache_path = os.path.join(inst_cache_dir, inst_basename + ".npy") + + if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path): + X_spec_m = np.load(mix_cache_path) + y_spec_m = np.load(inst_cache_path) + else: + X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {} + + for d in range(len(mp.param["band"]), 0, -1): + bp = mp.param["band"][d] + + if d == len(mp.param["band"]): # high-end band + X_wave[d], _ = librosa.load( + mix_path, bp["sr"], False, dtype=np.float32, res_type=bp["res_type"] + ) + y_wave[d], _ = librosa.load( + inst_path, + bp["sr"], + False, + dtype=np.float32, + res_type=bp["res_type"], + ) + else: # lower bands + X_wave[d] = librosa.resample( + X_wave[d + 1], + mp.param["band"][d + 1]["sr"], + bp["sr"], + res_type=bp["res_type"], + ) + y_wave[d] = librosa.resample( + y_wave[d + 1], + mp.param["band"][d + 1]["sr"], + bp["sr"], + res_type=bp["res_type"], + ) + + X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d]) + + X_spec_s[d] = wave_to_spectrogram( + X_wave[d], + bp["hl"], + bp["n_fft"], + mp.param["mid_side"], + mp.param["mid_side_b2"], + mp.param["reverse"], + ) + y_spec_s[d] = wave_to_spectrogram( + y_wave[d], + bp["hl"], + bp["n_fft"], + mp.param["mid_side"], + mp.param["mid_side_b2"], + mp.param["reverse"], + ) + + del X_wave, y_wave + + X_spec_m = combine_spectrograms(X_spec_s, mp) + y_spec_m = combine_spectrograms(y_spec_s, mp) + + if X_spec_m.shape != y_spec_m.shape: + raise ValueError("The combined spectrograms are different: " + mix_path) + + _, ext = os.path.splitext(mix_path) + + np.save(mix_cache_path, X_spec_m) + np.save(inst_cache_path, y_spec_m) + + return X_spec_m, y_spec_m + + +def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse): + spec_left = np.asfortranarray(spec[0]) + spec_right = np.asfortranarray(spec[1]) + + wave_left = librosa.istft(spec_left, hop_length=hop_length) + wave_right = librosa.istft(spec_right, hop_length=hop_length) + + if reverse: + return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)]) + elif mid_side: + return np.asfortranarray( + [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)] + ) + elif mid_side_b2: + return np.asfortranarray( + [ + np.add(wave_right / 1.25, 0.4 * wave_left), + np.subtract(wave_left / 1.25, 0.4 * wave_right), + ] + ) + else: + return np.asfortranarray([wave_left, wave_right]) + + +def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2): + import threading + + spec_left = np.asfortranarray(spec[0]) + spec_right = np.asfortranarray(spec[1]) + + def run_thread(**kwargs): + global wave_left + wave_left = librosa.istft(**kwargs) + + thread = threading.Thread( + target=run_thread, kwargs={"stft_matrix": spec_left, "hop_length": hop_length} + ) + thread.start() + wave_right = librosa.istft(spec_right, hop_length=hop_length) + thread.join() + + if reverse: + return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)]) + elif mid_side: + return np.asfortranarray( + [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)] + ) + elif mid_side_b2: + return np.asfortranarray( + [ + np.add(wave_right / 1.25, 0.4 * wave_left), + np.subtract(wave_left / 1.25, 0.4 * wave_right), + ] + ) + else: + return np.asfortranarray([wave_left, wave_right]) + + +def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None): + wave_band = {} + bands_n = len(mp.param["band"]) + offset = 0 + + for d in range(1, bands_n + 1): + bp = mp.param["band"][d] + spec_s = np.ndarray( + shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex + ) + h = bp["crop_stop"] - bp["crop_start"] + spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[ + :, offset : offset + h, : + ] + + offset += h + if d == bands_n: # higher + if extra_bins_h: # if --high_end_process bypass + max_bin = bp["n_fft"] // 2 + spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[ + :, :extra_bins_h, : + ] + if bp["hpf_start"] > 0: + spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1) + if bands_n == 1: + wave = spectrogram_to_wave( + spec_s, + bp["hl"], + mp.param["mid_side"], + mp.param["mid_side_b2"], + mp.param["reverse"], + ) + else: + wave = np.add( + wave, + spectrogram_to_wave( + spec_s, + bp["hl"], + mp.param["mid_side"], + mp.param["mid_side_b2"], + mp.param["reverse"], + ), + ) + else: + sr = mp.param["band"][d + 1]["sr"] + if d == 1: # lower + spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"]) + 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="sinc_fastest", + ) + else: # mid + spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1) + spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"]) + wave2 = np.add( + wave, + spectrogram_to_wave( + spec_s, + bp["hl"], + mp.param["mid_side"], + mp.param["mid_side_b2"], + mp.param["reverse"], + ), + ) + # wave = librosa.core.resample(wave2, bp['sr'], sr, res_type="sinc_fastest") + wave = librosa.core.resample(wave2, bp["sr"], sr, res_type="scipy") + + return wave.T + + +def fft_lp_filter(spec, bin_start, bin_stop): + g = 1.0 + for b in range(bin_start, bin_stop): + g -= 1 / (bin_stop - bin_start) + spec[:, b, :] = g * spec[:, b, :] + + spec[:, bin_stop:, :] *= 0 + + return spec + + +def fft_hp_filter(spec, bin_start, bin_stop): + g = 1.0 + for b in range(bin_start, bin_stop, -1): + g -= 1 / (bin_start - bin_stop) + spec[:, b, :] = g * spec[:, b, :] + + spec[:, 0 : bin_stop + 1, :] *= 0 + + return spec + + +def mirroring(a, spec_m, input_high_end, mp): + if "mirroring" == a: + 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, + ) + mirror = mirror * np.exp(1.0j * np.angle(input_high_end)) + + return np.where( + np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror + ) + + if "mirroring2" == a: + 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, + ) + mi = np.multiply(mirror, input_high_end * 1.7) + + return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi) + + +def ensembling(a, specs): + for i in range(1, len(specs)): + if i == 1: + spec = specs[0] + + ln = min([spec.shape[2], specs[i].shape[2]]) + spec = spec[:, :, :ln] + specs[i] = specs[i][:, :, :ln] + + if "min_mag" == a: + spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec) + if "max_mag" == a: + spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec) + + return spec + + +def stft(wave, nfft, hl): + wave_left = np.asfortranarray(wave[0]) + wave_right = np.asfortranarray(wave[1]) + spec_left = librosa.stft(wave_left, nfft, hop_length=hl) + spec_right = librosa.stft(wave_right, nfft, hop_length=hl) + spec = np.asfortranarray([spec_left, spec_right]) + + return spec + + +def istft(spec, hl): + spec_left = np.asfortranarray(spec[0]) + spec_right = np.asfortranarray(spec[1]) + + wave_left = librosa.istft(spec_left, hop_length=hl) + wave_right = librosa.istft(spec_right, hop_length=hl) + wave = np.asfortranarray([wave_left, wave_right]) + + +if __name__ == "__main__": + import cv2 + import sys + import time + import argparse + from model_param_init import ModelParameters + + p = argparse.ArgumentParser() + p.add_argument( + "--algorithm", + "-a", + type=str, + choices=["invert", "invert_p", "min_mag", "max_mag", "deep", "align"], + default="min_mag", + ) + p.add_argument( + "--model_params", + "-m", + type=str, + default=os.path.join("modelparams", "1band_sr44100_hl512.json"), + ) + p.add_argument("--output_name", "-o", type=str, default="output") + p.add_argument("--vocals_only", "-v", action="store_true") + p.add_argument("input", nargs="+") + args = p.parse_args() + + start_time = time.time() + + if args.algorithm.startswith("invert") and len(args.input) != 2: + raise ValueError("There should be two input files.") + + if not args.algorithm.startswith("invert") and len(args.input) < 2: + raise ValueError("There must be at least two input files.") + + wave, specs = {}, {} + mp = ModelParameters(args.model_params) + + for i in range(len(args.input)): + spec = {} + + for d in range(len(mp.param["band"]), 0, -1): + bp = mp.param["band"][d] + + if d == len(mp.param["band"]): # high-end band + wave[d], _ = librosa.load( + args.input[i], + bp["sr"], + False, + dtype=np.float32, + res_type=bp["res_type"], + ) + + if len(wave[d].shape) == 1: # mono to stereo + wave[d] = np.array([wave[d], wave[d]]) + else: # lower bands + wave[d] = librosa.resample( + wave[d + 1], + mp.param["band"][d + 1]["sr"], + bp["sr"], + res_type=bp["res_type"], + ) + + spec[d] = wave_to_spectrogram( + wave[d], + bp["hl"], + bp["n_fft"], + mp.param["mid_side"], + mp.param["mid_side_b2"], + mp.param["reverse"], + ) + + specs[i] = combine_spectrograms(spec, mp) + + del wave + + if args.algorithm == "deep": + d_spec = np.where(np.abs(specs[0]) <= np.abs(spec[1]), specs[0], spec[1]) + v_spec = d_spec - specs[1] + sf.write( + os.path.join("{}.wav".format(args.output_name)), + cmb_spectrogram_to_wave(v_spec, mp), + mp.param["sr"], + ) + + if args.algorithm.startswith("invert"): + ln = min([specs[0].shape[2], specs[1].shape[2]]) + specs[0] = specs[0][:, :, :ln] + specs[1] = specs[1][:, :, :ln] + + if "invert_p" == args.algorithm: + X_mag = np.abs(specs[0]) + y_mag = np.abs(specs[1]) + max_mag = np.where(X_mag >= y_mag, X_mag, y_mag) + v_spec = specs[1] - max_mag * np.exp(1.0j * np.angle(specs[0])) + else: + specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2) + v_spec = specs[0] - specs[1] + + if not args.vocals_only: + X_mag = np.abs(specs[0]) + y_mag = np.abs(specs[1]) + v_mag = np.abs(v_spec) + + X_image = spectrogram_to_image(X_mag) + y_image = spectrogram_to_image(y_mag) + v_image = spectrogram_to_image(v_mag) + + cv2.imwrite("{}_X.png".format(args.output_name), X_image) + cv2.imwrite("{}_y.png".format(args.output_name), y_image) + cv2.imwrite("{}_v.png".format(args.output_name), v_image) + + sf.write( + "{}_X.wav".format(args.output_name), + cmb_spectrogram_to_wave(specs[0], mp), + mp.param["sr"], + ) + sf.write( + "{}_y.wav".format(args.output_name), + cmb_spectrogram_to_wave(specs[1], mp), + mp.param["sr"], + ) + + sf.write( + "{}_v.wav".format(args.output_name), + cmb_spectrogram_to_wave(v_spec, mp), + mp.param["sr"], + ) + else: + if not args.algorithm == "deep": + sf.write( + os.path.join("ensembled", "{}.wav".format(args.output_name)), + cmb_spectrogram_to_wave(ensembling(args.algorithm, specs), mp), + mp.param["sr"], + ) + + if args.algorithm == "align": + trackalignment = [ + { + "file1": '"{}"'.format(args.input[0]), + "file2": '"{}"'.format(args.input[1]), + } + ] + + for i, e in tqdm(enumerate(trackalignment), desc="Performing Alignment..."): + os.system(f"python lib/align_tracks.py {e['file1']} {e['file2']}") + + # print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1)) diff --git a/uvr5_pack/name_params.json b/uvr5_pack/name_params.json new file mode 100644 index 0000000000000000000000000000000000000000..cb6609134a3831799b9ed190fac09e1eef59f9cb --- /dev/null +++ b/uvr5_pack/name_params.json @@ -0,0 +1,263 @@ +{ + "equivalent" : [ + { + "model_hash_name" : [ + { + "hash_name": "47939caf0cfe52a0e81442b85b971dfd", + "model_params": "uvr5_pack/lib_v5/modelparams/4band_44100.json", + "param_name": "4band_44100" + }, + { + "hash_name": "4e4ecb9764c50a8c414fee6e10395bbe", + "model_params": "uvr5_pack/lib_v5/modelparams/4band_v2.json", + "param_name": "4band_v2" + }, + { + "hash_name": "ca106edd563e034bde0bdec4bb7a4b36", + "model_params": "uvr5_pack/lib_v5/modelparams/4band_v2.json", + "param_name": "4band_v2" + }, + { + "hash_name": "e60a1e84803ce4efc0a6551206cc4b71", + "model_params": "uvr5_pack/lib_v5/modelparams/4band_44100.json", + "param_name": "4band_44100" + }, + { + "hash_name": 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+ "hash_name": "tmodelparam", + "model_params": "uvr5_pack/lib_v5/modelparams/tmodelparam.json", + "param_name": "User Model Param Set" + } + ] + } + ] +} \ No newline at end of file diff --git a/uvr5_pack/utils.py b/uvr5_pack/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..dbea30293e9343ecf053d3d539f6c5b577f04f10 --- /dev/null +++ b/uvr5_pack/utils.py @@ -0,0 +1,119 @@ +import torch +import numpy as np +from tqdm import tqdm +import json +import pdb + +def load_data(file_name: str = "./uvr5_pack/name_params.json") -> dict: + with open(file_name, "r") as f: + data = json.load(f) + + return data + + +def make_padding(width, cropsize, offset): + left = offset + roi_size = cropsize - left * 2 + if roi_size == 0: + roi_size = cropsize + right = roi_size - (width % roi_size) + left + + return left, right, roi_size + + +def inference(X_spec, device, model, aggressiveness, data): + """ + data ： dic configs + """ + + def _execute( + X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True + ): + model.eval() + with torch.no_grad(): + preds = [] + + iterations = [n_window] + + total_iterations = sum(iterations) + for i in tqdm(range(n_window)): + start = i * roi_size + X_mag_window = X_mag_pad[ + None, :, :, start : start + data["window_size"] + ] + X_mag_window = torch.from_numpy(X_mag_window) + if is_half: + X_mag_window = X_mag_window.half() + X_mag_window = X_mag_window.to(device) + pred = model.predict(X_mag_window, aggressiveness) + + pred = pred.detach().cpu().numpy() + preds.append(pred[0]) + + pred = np.concatenate(preds, axis=2) + return pred + + def preprocess(X_spec): + X_mag = np.abs(X_spec) + X_phase = np.angle(X_spec) + + return X_mag, X_phase + + X_mag, X_phase = preprocess(X_spec) + + coef = X_mag.max() + X_mag_pre = X_mag / coef + + n_frame = X_mag_pre.shape[2] + pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset) + n_window = int(np.ceil(n_frame / roi_size)) + + X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant") + + if list(model.state_dict().values())[0].dtype == torch.float16: + is_half = True + else: + is_half = False + pred = _execute( + X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half + ) + pred = pred[:, :, :n_frame] + + if data["tta"]: + pad_l += roi_size // 2 + pad_r += roi_size // 2 + n_window += 1 + + X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant") + + pred_tta = _execute( + X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half + ) + pred_tta = pred_tta[:, :, roi_size // 2 :] + pred_tta = pred_tta[:, :, :n_frame] + + return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase) + else: + return pred * coef, X_mag, np.exp(1.0j * X_phase) + + +def _get_name_params(model_path, model_hash): + data = load_data() + flag = False + ModelName = model_path + for type in list(data): + for model in list(data[type][0]): + for i in range(len(data[type][0][model])): + if str(data[type][0][model][i]["hash_name"]) == model_hash: + flag = True + elif str(data[type][0][model][i]["hash_name"]) in ModelName: + flag = True + + if flag: + model_params_auto = data[type][0][model][i]["model_params"] + param_name_auto = data[type][0][model][i]["param_name"] + if type == "equivalent": + return param_name_auto, model_params_auto + else: + flag = False + return param_name_auto, model_params_auto diff --git "a/\344\272\272\345\243\260/.gitkeep" "b/\344\272\272\345\243\260/.gitkeep" new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git "a/\344\274\264\345\245\217/.gitkeep" "b/\344\274\264\345\245\217/.gitkeep" new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391