Delete uvr5_pack

uvr5_pack/lib_v5/dataset.py

@@ -1,170 +0,0 @@
-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)

uvr5_pack/lib_v5/layers.py

@@ -1,116 +0,0 @@
-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

uvr5_pack/lib_v5/layers_123812KB .py

@@ -1,116 +0,0 @@
-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

uvr5_pack/lib_v5/layers_123821KB.py

@@ -1,116 +0,0 @@
-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

uvr5_pack/lib_v5/layers_33966KB.py

@@ -1,122 +0,0 @@
-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

uvr5_pack/lib_v5/layers_537227KB.py

@@ -1,122 +0,0 @@
-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

uvr5_pack/lib_v5/layers_537238KB.py

@@ -1,122 +0,0 @@
-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

uvr5_pack/lib_v5/model_param_init.py

@@ -1,60 +0,0 @@
-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

uvr5_pack/lib_v5/modelparams/1band_sr16000_hl512.json

@@ -1,19 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json

@@ -1,19 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json

@@ -1,19 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/1band_sr44100_hl1024.json

@@ -1,19 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/1band_sr44100_hl256.json

@@ -1,19 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json

@@ -1,19 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512_cut.json

@@ -1,19 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/2band_32000.json

@@ -1,30 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/2band_44100_lofi.json

@@ -1,30 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/2band_48000.json

@@ -1,30 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/3band_44100.json

@@ -1,42 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/3band_44100_mid.json

@@ -1,43 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json

@@ -1,43 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/4band_44100.json

@@ -1,54 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/4band_44100_mid.json

@@ -1,55 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/4band_44100_msb.json

@@ -1,55 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/4band_44100_msb2.json

@@ -1,55 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/4band_44100_reverse.json

@@ -1,55 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/4band_44100_sw.json

@@ -1,55 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/4band_v2.json

@@ -1,54 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/4band_v2_sn.json

@@ -1,55 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/modelparams/ensemble.json

@@ -1,43 +0,0 @@
-{
-	"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
-}

uvr5_pack/lib_v5/nets.py

@@ -1,113 +0,0 @@
-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

uvr5_pack/lib_v5/nets_123812KB.py

@@ -1,112 +0,0 @@
-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

uvr5_pack/lib_v5/nets_123821KB.py

@@ -1,112 +0,0 @@
-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

uvr5_pack/lib_v5/nets_33966KB.py

@@ -1,112 +0,0 @@
-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

uvr5_pack/lib_v5/nets_537227KB.py

@@ -1,113 +0,0 @@
-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

uvr5_pack/lib_v5/nets_537238KB.py

@@ -1,113 +0,0 @@
-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

uvr5_pack/lib_v5/nets_61968KB.py

@@ -1,112 +0,0 @@
-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

uvr5_pack/lib_v5/spec_utils.py

@@ -1,485 +0,0 @@
-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] * .5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .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] * .5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .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.j * 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, .4 * wave_left), np.subtract(wave_left / 1.25, .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, .4 * wave_left), np.subtract(wave_left / 1.25, .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.j * 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.j * 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))

uvr5_pack/utils.py

@@ -1,242 +0,0 @@
-import torch
-import numpy as np
-from tqdm import tqdm
-
-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==True):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.j * X_phase)
-    else:
-        return pred * coef, X_mag, np.exp(1.j * X_phase)
-            
-
-
-def  _get_name_params(model_path , model_hash):
-    ModelName = model_path
-    if model_hash == '47939caf0cfe52a0e81442b85b971dfd':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100.json')
-        param_name_auto=str('4band_44100')
-    if model_hash == '4e4ecb9764c50a8c414fee6e10395bbe':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_v2.json')
-        param_name_auto=str('4band_v2')
-    if model_hash == 'ca106edd563e034bde0bdec4bb7a4b36':
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_v2.json')
-        param_name_auto=str('4band_v2')
-    if model_hash == 'e60a1e84803ce4efc0a6551206cc4b71':
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100.json')
-        param_name_auto=str('4band_44100')
-    if model_hash == 'a82f14e75892e55e994376edbf0c8435':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100.json')
-        param_name_auto=str('4band_44100')
-    if model_hash == '6dd9eaa6f0420af9f1d403aaafa4cc06':   
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_v2_sn.json')
-        param_name_auto=str('4band_v2_sn')
-    if model_hash == '08611fb99bd59eaa79ad27c58d137727':
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_v2_sn.json')
-        param_name_auto=str('4band_v2_sn')
-    if model_hash == '5c7bbca45a187e81abbbd351606164e5':
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json')
-        param_name_auto=str('3band_44100_msb2')
-    if model_hash == 'd6b2cb685a058a091e5e7098192d3233':    
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json')
-        param_name_auto=str('3band_44100_msb2')
-    if model_hash == 'c1b9f38170a7c90e96f027992eb7c62b': 
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100.json')
-        param_name_auto=str('4band_44100')
-    if model_hash == 'c3448ec923fa0edf3d03a19e633faa53':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100.json')
-        param_name_auto=str('4band_44100')
-    if model_hash == '68aa2c8093d0080704b200d140f59e54':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/3band_44100.json')
-        param_name_auto=str('3band_44100.json')
-    if model_hash == 'fdc83be5b798e4bd29fe00fe6600e147':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/3band_44100_mid.json')
-        param_name_auto=str('3band_44100_mid.json')
-    if model_hash == '2ce34bc92fd57f55db16b7a4def3d745':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/3band_44100_mid.json')
-        param_name_auto=str('3band_44100_mid.json')
-    if model_hash == '52fdca89576f06cf4340b74a4730ee5f':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100.json')
-        param_name_auto=str('4band_44100.json')
-    if model_hash == '41191165b05d38fc77f072fa9e8e8a30':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100.json')
-        param_name_auto=str('4band_44100.json')
-    if model_hash == '89e83b511ad474592689e562d5b1f80e':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/2band_32000.json')
-        param_name_auto=str('2band_32000.json')
-    if model_hash == '0b954da81d453b716b114d6d7c95177f':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/2band_32000.json')
-        param_name_auto=str('2band_32000.json')
-
-    #v4 Models    
-    if model_hash == '6a00461c51c2920fd68937d4609ed6c8':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr16000_hl512.json')
-        param_name_auto=str('1band_sr16000_hl512')
-    if model_hash == '0ab504864d20f1bd378fe9c81ef37140':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json')
-        param_name_auto=str('1band_sr32000_hl512')
-    if model_hash == '7dd21065bf91c10f7fccb57d7d83b07f':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json')
-        param_name_auto=str('1band_sr32000_hl512')
-    if model_hash == '80ab74d65e515caa3622728d2de07d23':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json')
-        param_name_auto=str('1band_sr32000_hl512')
-    if model_hash == 'edc115e7fc523245062200c00caa847f':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json')
-        param_name_auto=str('1band_sr33075_hl384')
-    if model_hash == '28063e9f6ab5b341c5f6d3c67f2045b7':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json')
-        param_name_auto=str('1band_sr33075_hl384')
-    if model_hash == 'b58090534c52cbc3e9b5104bad666ef2':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json')
-        param_name_auto=str('1band_sr44100_hl512')
-    if model_hash == '0cdab9947f1b0928705f518f3c78ea8f':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json')
-        param_name_auto=str('1band_sr44100_hl512')
-    if model_hash == 'ae702fed0238afb5346db8356fe25f13':  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl1024.json')
-        param_name_auto=str('1band_sr44100_hl1024')                        
-    #User Models
-
-    #1 Band
-    if '1band_sr16000_hl512' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr16000_hl512.json')
-        param_name_auto=str('1band_sr16000_hl512')
-    if '1band_sr32000_hl512' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json')
-        param_name_auto=str('1band_sr32000_hl512')
-    if '1band_sr33075_hl384' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json')
-        param_name_auto=str('1band_sr33075_hl384')
-    if '1band_sr44100_hl256' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl256.json')
-        param_name_auto=str('1band_sr44100_hl256')
-    if '1band_sr44100_hl512' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json')
-        param_name_auto=str('1band_sr44100_hl512')
-    if '1band_sr44100_hl1024' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl1024.json')
-        param_name_auto=str('1band_sr44100_hl1024')
-        
-    #2 Band
-    if '2band_44100_lofi' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/2band_44100_lofi.json')
-        param_name_auto=str('2band_44100_lofi')
-    if '2band_32000' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/2band_32000.json')
-        param_name_auto=str('2band_32000')
-    if '2band_48000' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/2band_48000.json')
-        param_name_auto=str('2band_48000')
-        
-    #3 Band   
-    if '3band_44100' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/3band_44100.json')
-        param_name_auto=str('3band_44100')
-    if '3band_44100_mid' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/3band_44100_mid.json')
-        param_name_auto=str('3band_44100_mid')
-    if '3band_44100_msb2' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json')
-        param_name_auto=str('3band_44100_msb2')
-        
-    #4 Band    
-    if '4band_44100' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100.json')
-        param_name_auto=str('4band_44100')
-    if '4band_44100_mid' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100_mid.json')
-        param_name_auto=str('4band_44100_mid')
-    if '4band_44100_msb' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100_msb.json')
-        param_name_auto=str('4band_44100_msb')
-    if '4band_44100_msb2' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100_msb2.json')
-        param_name_auto=str('4band_44100_msb2')
-    if '4band_44100_reverse' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100_reverse.json')
-        param_name_auto=str('4band_44100_reverse')
-    if '4band_44100_sw' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_44100_sw.json') 
-        param_name_auto=str('4band_44100_sw')
-    if '4band_v2' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_v2.json')
-        param_name_auto=str('4band_v2')
-    if '4band_v2_sn' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/4band_v2_sn.json')
-        param_name_auto=str('4band_v2_sn')
-    if 'tmodelparam' in ModelName:  
-        model_params_auto=str('runtime/Lib/site-packages/uvr5_pack/lib_v5/modelparams/tmodelparam.json')
-        param_name_auto=str('User Model Param Set')
-    return param_name_auto , model_params_auto