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Delete lib_v2 directory
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Anjok07
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commit
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import os
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import numpy as np
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import torch
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from tqdm import tqdm
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from lib_v2 import spec_utils
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class VocalRemoverValidationSet(torch.utils.data.Dataset):
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def __init__(self, filelist):
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self.filelist = filelist
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def __len__(self):
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return len(self.filelist)
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def __getitem__(self, idx):
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path = self.filelist[idx]
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data = np.load(path)
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return data['X'], data['y']
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def mixup_generator(X, y, rate, alpha):
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perm = np.random.permutation(len(X))[:int(len(X) * rate)]
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for i in range(len(perm) - 1):
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lam = np.random.beta(alpha, alpha)
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X[perm[i]] = lam * X[perm[i]] + (1 - lam) * X[perm[i + 1]]
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y[perm[i]] = lam * y[perm[i]] + (1 - lam) * y[perm[i + 1]]
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return X, y
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def get_oracle_data(X, y, instance_loss, oracle_rate, oracle_drop_rate):
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k = int(len(X) * oracle_rate * (1 / (1 - oracle_drop_rate)))
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n = int(len(X) * oracle_rate)
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idx = np.argsort(instance_loss)[::-1][:k]
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idx = np.random.choice(idx, n, replace=False)
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oracle_X = X[idx].copy()
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oracle_y = y[idx].copy()
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return oracle_X, oracle_y, idx
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def make_padding(width, cropsize, offset):
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left = offset
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roi_size = cropsize - left * 2
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if roi_size == 0:
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roi_size = cropsize
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right = roi_size - (width % roi_size) + left
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return left, right, roi_size
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def make_training_set(filelist, cropsize, patches, sr, hop_length, offset):
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len_dataset = patches * len(filelist)
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X_dataset = np.zeros(
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(len_dataset, 2, hop_length, cropsize), dtype=np.float32)
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y_dataset = np.zeros(
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(len_dataset, 2, hop_length, cropsize), dtype=np.float32)
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for i, (X_path, y_path) in enumerate(tqdm(filelist)):
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p = np.random.uniform()
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if p < 0.1:
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X_path.replace(os.path.splitext(X_path)[1], '_pitch-1.wav')
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y_path.replace(os.path.splitext(y_path)[1], '_pitch-1.wav')
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elif p < 0.2:
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X_path.replace(os.path.splitext(X_path)[1], '_pitch1.wav')
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y_path.replace(os.path.splitext(y_path)[1], '_pitch1.wav')
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X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length)
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coeff = np.max([X.max(), y.max()])
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X, y = X / coeff, y / coeff
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l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
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X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant')
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y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode='constant')
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starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches)
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ends = starts + cropsize
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for j in range(patches):
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idx = i * patches + j
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X_dataset[idx] = X_pad[:, :, starts[j]:ends[j]]
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y_dataset[idx] = y_pad[:, :, starts[j]:ends[j]]
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if np.random.uniform() < 0.5:
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# swap channel
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X_dataset[idx] = X_dataset[idx, ::-1]
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y_dataset[idx] = y_dataset[idx, ::-1]
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return X_dataset, y_dataset
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def make_validation_set(filelist, cropsize, sr, hop_length, offset):
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patch_list = []
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outdir = 'cs{}_sr{}_hl{}_of{}'.format(cropsize, sr, hop_length, offset)
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os.makedirs(outdir, exist_ok=True)
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for i, (X_path, y_path) in enumerate(tqdm(filelist)):
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basename = os.path.splitext(os.path.basename(X_path))[0]
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X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length)
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coeff = np.max([X.max(), y.max()])
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X, y = X / coeff, y / coeff
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l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
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X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant')
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y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode='constant')
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len_dataset = int(np.ceil(X.shape[2] / roi_size))
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for j in range(len_dataset):
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outpath = os.path.join(outdir, '{}_p{}.npz'.format(basename, j))
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start = j * roi_size
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if not os.path.exists(outpath):
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np.savez(
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outpath,
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X=X_pad[:, :, start:start + cropsize],
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y=y_pad[:, :, start:start + cropsize])
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patch_list.append(outpath)
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return VocalRemoverValidationSet(patch_list)
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117
lib_v2/layers.py
117
lib_v2/layers.py
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import torch
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from torch import nn
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import torch.nn.functional as F
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from lib_v2 import spec_utils
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class Conv2DBNActiv(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
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super(Conv2DBNActiv, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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nin, nout,
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kernel_size=ksize,
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stride=stride,
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padding=pad,
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dilation=dilation,
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bias=False),
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nn.BatchNorm2d(nout),
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activ()
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)
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def __call__(self, x):
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return self.conv(x)
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class SeperableConv2DBNActiv(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
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super(SeperableConv2DBNActiv, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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nin, nin,
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kernel_size=ksize,
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stride=stride,
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padding=pad,
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dilation=dilation,
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groups=nin,
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bias=False),
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nn.Conv2d(
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nin, nout,
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kernel_size=1,
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bias=False),
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nn.BatchNorm2d(nout),
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activ()
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)
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def __call__(self, x):
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return self.conv(x)
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class Encoder(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
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super(Encoder, self).__init__()
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self.conv1 = Conv2DBNActiv(
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nin, nout, ksize, 1, pad, activ=activ)
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self.conv2 = Conv2DBNActiv(
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nout, nout, ksize, stride, pad, activ=activ)
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def __call__(self, x):
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skip = self.conv1(x)
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h = self.conv2(skip)
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return h, skip
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class Decoder(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dropout=False):
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super(Decoder, self).__init__()
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self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad)
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self.dropout = nn.Dropout2d(0.1) if dropout else None
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def __call__(self, x, skip=None):
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x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
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if skip is not None:
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x = spec_utils.crop_center(x, skip)
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h = self.conv(x)
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if self.dropout is not None:
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h = self.dropout(h)
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return h
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class ASPPModule(nn.Module):
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def __init__(self, nin, nout, dilations=(4, 8, 16)):
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super(ASPPModule, self).__init__()
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self.conv1 = nn.Sequential(
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nn.AdaptiveAvgPool2d((1, None)),
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Conv2DBNActiv(nin, nin, 1, 1, 0)
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)
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self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0)
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self.conv3 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[0], dilations[0])
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self.conv4 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[1], dilations[1])
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self.conv5 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[2], dilations[2])
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self.bottleneck = nn.Sequential(
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Conv2DBNActiv(nin * 5, nout, 1, 1, 0),
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nn.Dropout2d(0.1)
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)
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def forward(self, x):
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_, _, h, w = x.size()
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feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
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feat2 = self.conv2(x)
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feat3 = self.conv3(x)
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feat4 = self.conv4(x)
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feat5 = self.conv5(x)
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out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
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bottle = self.bottleneck(out)
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return bottle
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import torch
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from torch import nn
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from lib_v2 import layers
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class BaseASPPNet(nn.Module):
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def __init__(self, nin, ch, dilations=(4, 8, 16)):
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super(BaseASPPNet, self).__init__()
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self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
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self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
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self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
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self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
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self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
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self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
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self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
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self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
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self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
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def __call__(self, x):
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h, e1 = self.enc1(x)
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h, e2 = self.enc2(h)
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h, e3 = self.enc3(h)
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h, e4 = self.enc4(h)
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h = self.aspp(h)
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h = self.dec4(h, e4)
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h = self.dec3(h, e3)
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h = self.dec2(h, e2)
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h = self.dec1(h, e1)
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return h
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class CascadedASPPNet(nn.Module):
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def __init__(self):
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super(CascadedASPPNet, self).__init__()
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self.low_band_net = BaseASPPNet(2, 32, ((2, 4), (4, 8), (8, 16)))
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self.high_band_net = BaseASPPNet(2, 32, ((2, 4), (4, 8), (8, 16)))
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self.bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
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self.full_band_net = BaseASPPNet(16, 32)
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self.out = nn.Sequential(
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layers.Conv2DBNActiv(32, 16, 3, 1, 1),
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nn.Conv2d(16, 2, 1, bias=False))
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self.aux_out = nn.Conv2d(32, 2, 1, bias=False)
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self.offset = 128
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def __call__(self, x):
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bandw = x.size()[2] // 2
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aux = torch.cat([
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self.low_band_net(x[:, :, :bandw]),
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self.high_band_net(x[:, :, bandw:])
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], dim=2)
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h = torch.cat([x, aux], dim=1)
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h = self.full_band_net(self.bridge(h))
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h = torch.sigmoid(self.out(h))
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aux = torch.sigmoid(self.aux_out(aux))
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return h, aux
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def predict(self, x):
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bandw = x.size()[2] // 2
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aux = torch.cat([
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self.low_band_net(x[:, :, :bandw]),
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self.high_band_net(x[:, :, bandw:])
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], dim=2)
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h = torch.cat([x, aux], dim=1)
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h = self.full_band_net(self.bridge(h))
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h = torch.sigmoid(self.out(h))
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if self.offset > 0:
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h = h[:, :, :, self.offset:-self.offset]
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assert h.size()[3] > 0
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return h
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@ -1,136 +0,0 @@
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import os
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import librosa
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import numpy as np
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import soundfile as sf
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import torch
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def crop_center(h1, h2, concat=True):
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# s_freq = (h2.shape[2] - h1.shape[2]) // 2
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# e_freq = s_freq + h1.shape[2]
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h1_shape = h1.size()
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h2_shape = h2.size()
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if h2_shape[3] < h1_shape[3]:
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raise ValueError('h2_shape[3] must be greater than h1_shape[3]')
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s_time = (h2_shape[3] - h1_shape[3]) // 2
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e_time = s_time + h1_shape[3]
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h2 = h2[:, :, :, s_time:e_time]
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if concat:
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return torch.cat([h1, h2], dim=1)
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else:
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return h2
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def calc_spec(X, hop_length):
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n_fft = (hop_length - 1) * 2
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audio_left = np.asfortranarray(X[0])
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audio_right = np.asfortranarray(X[1])
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spec_left = librosa.stft(audio_left, n_fft, hop_length=hop_length)
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spec_right = librosa.stft(audio_right, n_fft, hop_length=hop_length)
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def mask_uninformative(mask, ref, thres=0.3, min_range=64, fade_area=32):
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if min_range < fade_area * 2:
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raise ValueError('min_range must be >= fade_area * 2')
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idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
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starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
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ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
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uninformative = np.where(ends - starts > min_range)[0]
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if len(uninformative) > 0:
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starts = starts[uninformative]
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ends = ends[uninformative]
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old_e = None
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for s, e in zip(starts, ends):
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if old_e is not None and s - old_e < fade_area:
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s = old_e - fade_area * 2
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elif s != 0:
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start_mask = mask[:, :, s:s + fade_area]
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np.clip(
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start_mask + np.linspace(0, 1, fade_area), 0, 1,
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out=start_mask)
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if e != mask.shape[2]:
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end_mask = mask[:, :, e - fade_area:e]
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np.clip(
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end_mask + np.linspace(1, 0, fade_area), 0, 1,
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out=end_mask)
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mask[:, :, s + fade_area:e - fade_area] = 1
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old_e = e
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return mask
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def align_wave_head_and_tail(a, b, sr):
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a_mono = a[:, :sr * 4].sum(axis=0)
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b_mono = b[:, :sr * 4].sum(axis=0)
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a_mono -= a_mono.mean()
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b_mono -= b_mono.mean()
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offset = len(a_mono) - 1
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delay = np.argmax(np.correlate(a_mono, b_mono, 'full')) - offset
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if delay > 0:
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a = a[:, delay:]
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else:
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b = b[:, np.abs(delay):]
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if a.shape[1] < b.shape[1]:
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b = b[:, :a.shape[1]]
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else:
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a = a[:, :b.shape[1]]
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return a, b
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def cache_or_load(mix_path, inst_path, sr, hop_length):
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_, mix_ext = os.path.splitext(mix_path)
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_, inst_ext = os.path.splitext(inst_path)
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spec_mix_path = mix_path.replace(mix_ext, '.npy')
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spec_inst_path = inst_path.replace(inst_ext, '.npy')
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if os.path.exists(spec_mix_path) and os.path.exists(spec_inst_path):
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X = np.load(spec_mix_path)
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y = np.load(spec_inst_path)
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else:
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X, _ = librosa.load(
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mix_path, sr, False, dtype=np.float32, res_type='kaiser_fast')
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y, _ = librosa.load(
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inst_path, sr, False, dtype=np.float32, res_type='kaiser_fast')
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||||
X, _ = librosa.effects.trim(X)
|
||||
y, _ = librosa.effects.trim(y)
|
||||
X, y = align_wave_head_and_tail(X, y, sr)
|
||||
|
||||
X = np.abs(calc_spec(X, hop_length))
|
||||
y = np.abs(calc_spec(y, hop_length))
|
||||
|
||||
_, ext = os.path.splitext(mix_path)
|
||||
np.save(spec_mix_path, X)
|
||||
np.save(spec_inst_path, y)
|
||||
|
||||
return X, y
|
||||
|
||||
|
||||
def spec_to_wav(mag, phase, hop_length):
|
||||
spec = mag * phase
|
||||
spec_left = np.asfortranarray(spec[0])
|
||||
spec_right = np.asfortranarray(spec[1])
|
||||
wav_left = librosa.istft(spec_left, hop_length=hop_length)
|
||||
wav_right = librosa.istft(spec_right, hop_length=hop_length)
|
||||
wav = np.asfortranarray([wav_left, wav_right])
|
||||
|
||||
return wav
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
X, _ = librosa.load(
|
||||
sys.argv[1], 44100, False, dtype=np.float32, res_type='kaiser_fast')
|
||||
y, _ = librosa.load(
|
||||
sys.argv[2], 44100, False, dtype=np.float32, res_type='kaiser_fast')
|
||||
X, _ = librosa.effects.trim(X)
|
||||
y, _ = librosa.effects.trim(y)
|
||||
X, y = align_wave_head_and_tail(X, y, 44100)
|
||||
sf.write('test_i.wav', y.T, 44100)
|
||||
sf.write('test_m.wav', X.T, 44100)
|
||||
sf.write('test_v.wav', (X - y).T, 44100)
|
||||
Loading…
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Reference in New Issue
Block a user