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Delete lib_v4 directory
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Anjok07
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commit
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import os
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import random
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import numpy as np
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import torch
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import torch.utils.data
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from tqdm import tqdm
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from lib_v4 import spec_utils
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class VocalRemoverValidationSet(torch.utils.data.Dataset):
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def __init__(self, patch_list):
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self.patch_list = patch_list
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def __len__(self):
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return len(self.patch_list)
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def __getitem__(self, idx):
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path = self.patch_list[idx]
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data = np.load(path)
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X, y = data['X'], data['y']
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X_mag = np.abs(X)
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y_mag = np.abs(y)
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return X_mag, y_mag
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def make_pair(mix_dir, inst_dir):
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input_exts = ['.wav', '.m4a', '.mp3', '.mp4', '.flac']
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X_list = sorted([
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os.path.join(mix_dir, fname)
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for fname in os.listdir(mix_dir)
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if os.path.splitext(fname)[1] in input_exts])
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y_list = sorted([
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os.path.join(inst_dir, fname)
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for fname in os.listdir(inst_dir)
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if os.path.splitext(fname)[1] in input_exts])
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filelist = list(zip(X_list, y_list))
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return filelist
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def train_val_split(dataset_dir, split_mode, val_rate, val_filelist):
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if split_mode == 'random':
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filelist = make_pair(
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os.path.join(dataset_dir, 'mixtures'),
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os.path.join(dataset_dir, 'instruments'))
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random.shuffle(filelist)
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if len(val_filelist) == 0:
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val_size = int(len(filelist) * val_rate)
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train_filelist = filelist[:-val_size]
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val_filelist = filelist[-val_size:]
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else:
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train_filelist = [
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pair for pair in filelist
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if list(pair) not in val_filelist]
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elif split_mode == 'subdirs':
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if len(val_filelist) != 0:
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raise ValueError('The `val_filelist` option is not available in `subdirs` mode')
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train_filelist = make_pair(
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os.path.join(dataset_dir, 'training/mixtures'),
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os.path.join(dataset_dir, 'training/instruments'))
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val_filelist = make_pair(
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os.path.join(dataset_dir, 'validation/mixtures'),
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os.path.join(dataset_dir, 'validation/instruments'))
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return train_filelist, val_filelist
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def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha):
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perm = np.random.permutation(len(X))
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for i, idx in enumerate(tqdm(perm)):
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if np.random.uniform() < reduction_rate:
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y[idx] = spec_utils.reduce_vocal_aggressively(X[idx], y[idx], reduction_mask)
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if np.random.uniform() < 0.5:
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# swap channel
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X[idx] = X[idx, ::-1]
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y[idx] = y[idx, ::-1]
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if np.random.uniform() < 0.02:
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# mono
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X[idx] = X[idx].mean(axis=0, keepdims=True)
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y[idx] = y[idx].mean(axis=0, keepdims=True)
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if np.random.uniform() < 0.02:
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# inst
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X[idx] = y[idx]
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if np.random.uniform() < mixup_rate and i < len(perm) - 1:
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lam = np.random.beta(mixup_alpha, mixup_alpha)
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X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]]
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y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]]
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return X, y
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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, n_fft, 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, n_fft // 2 + 1, cropsize), dtype=np.complex64)
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y_dataset = np.zeros(
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(len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
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for i, (X_path, y_path) in enumerate(tqdm(filelist)):
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X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
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coef = np.max([np.abs(X).max(), np.abs(y).max()])
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X, y = X / coef, y / coef
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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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return X_dataset, y_dataset
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def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset):
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patch_list = []
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patch_dir = 'cs{}_sr{}_hl{}_nf{}_of{}'.format(cropsize, sr, hop_length, n_fft, offset)
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os.makedirs(patch_dir, 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, n_fft)
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coef = np.max([np.abs(X).max(), np.abs(y).max()])
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X, y = X / coef, y / coef
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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(patch_dir, '{}_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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116
lib_v4/layers.py
116
lib_v4/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_v4 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(nin, nout, ksize, 1, pad, activ=activ)
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self.conv2 = Conv2DBNActiv(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, activ=nn.ReLU, dropout=False):
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super(Decoder, self).__init__()
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self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
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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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skip = spec_utils.crop_center(skip, x)
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x = torch.cat([x, skip], dim=1)
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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), activ=nn.ReLU):
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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, activ=activ)
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)
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self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
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self.conv3 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)
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self.conv4 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)
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self.conv5 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
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self.bottleneck = nn.Sequential(
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Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ),
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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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108
lib_v4/nets.py
108
lib_v4/nets.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_v4 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, n_fft):
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super(CascadedASPPNet, self).__init__()
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self.stg1_low_band_net = BaseASPPNet(2, 16)
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self.stg1_high_band_net = BaseASPPNet(2, 16)
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self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
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self.stg2_full_band_net = BaseASPPNet(8, 16)
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self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
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self.stg3_full_band_net = BaseASPPNet(16, 32)
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self.out = nn.Conv2d(32, 2, 1, bias=False)
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self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
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self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
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self.max_bin = n_fft // 2
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self.output_bin = n_fft // 2 + 1
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self.offset = 128
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def forward(self, x):
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mix = x.detach()
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x = x.clone()
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x = x[:, :, :self.max_bin]
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bandw = x.size()[2] // 2
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aux1 = torch.cat([
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self.stg1_low_band_net(x[:, :, :bandw]),
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self.stg1_high_band_net(x[:, :, bandw:])
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], dim=2)
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h = torch.cat([x, aux1], dim=1)
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aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
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h = torch.cat([x, aux1, aux2], dim=1)
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h = self.stg3_full_band_net(self.stg3_bridge(h))
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mask = torch.sigmoid(self.out(h))
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mask = F.pad(
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input=mask,
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pad=(0, 0, 0, self.output_bin - mask.size()[2]),
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mode='replicate')
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if self.training:
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aux1 = torch.sigmoid(self.aux1_out(aux1))
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aux1 = F.pad(
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input=aux1,
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pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
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mode='replicate')
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aux2 = torch.sigmoid(self.aux2_out(aux2))
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aux2 = F.pad(
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input=aux2,
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pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
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mode='replicate')
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return mask * mix, aux1 * mix, aux2 * mix
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else:
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return mask * mix
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def predict(self, x_mag):
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h = self.forward(x_mag)
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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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@ -1,216 +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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def crop_center(h1, h2):
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h1_shape = h1.size()
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h2_shape = h2.size()
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if h1_shape[3] == h2_shape[3]:
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return h1
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elif h1_shape[3] < h2_shape[3]:
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raise ValueError('h1_shape[3] must be greater than h2_shape[3]')
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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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s_time = (h1_shape[3] - h2_shape[3]) // 2
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e_time = s_time + h2_shape[3]
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h1 = h1[:, :, :, s_time:e_time]
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return h1
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def wave_to_spectrogram(wave, hop_length, n_fft):
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
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spec_right = librosa.stft(wave_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 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, sr):
|
||||
a, _ = librosa.effects.trim(a)
|
||||
b, _ = librosa.effects.trim(b)
|
||||
|
||||
a_mono = a[:, :sr * 4].sum(axis=0)
|
||||
b_mono = b[:, :sr * 4].sum(axis=0)
|
||||
|
||||
a_mono -= a_mono.mean()
|
||||
b_mono -= b_mono.mean()
|
||||
|
||||
offset = len(a_mono) - 1
|
||||
delay = np.argmax(np.correlate(a_mono, b_mono, 'full')) - offset
|
||||
|
||||
if delay > 0:
|
||||
a = a[:, delay:]
|
||||
else:
|
||||
b = b[:, np.abs(delay):]
|
||||
|
||||
if a.shape[1] < b.shape[1]:
|
||||
b = b[:, :a.shape[1]]
|
||||
else:
|
||||
a = a[:, :b.shape[1]]
|
||||
|
||||
return a, b
|
||||
|
||||
|
||||
def cache_or_load(mix_path, inst_path, sr, hop_length, n_fft):
|
||||
mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
|
||||
inst_basename = os.path.splitext(os.path.basename(inst_path))[0]
|
||||
|
||||
cache_dir = 'sr{}_hl{}_nf{}'.format(sr, hop_length, n_fft)
|
||||
mix_cache_dir = os.path.join(os.path.dirname(mix_path), cache_dir)
|
||||
inst_cache_dir = os.path.join(os.path.dirname(inst_path), 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 = np.load(mix_cache_path)
|
||||
y = np.load(inst_cache_path)
|
||||
else:
|
||||
X, _ = librosa.load(
|
||||
mix_path, sr, False, dtype=np.float32, res_type='kaiser_fast')
|
||||
y, _ = librosa.load(
|
||||
inst_path, sr, False, dtype=np.float32, res_type='kaiser_fast')
|
||||
|
||||
X, y = align_wave_head_and_tail(X, y, sr)
|
||||
|
||||
X = wave_to_spectrogram(X, hop_length, n_fft)
|
||||
y = wave_to_spectrogram(y, hop_length, n_fft)
|
||||
|
||||
_, ext = os.path.splitext(mix_path)
|
||||
np.save(mix_cache_path, X)
|
||||
np.save(inst_cache_path, y)
|
||||
|
||||
return X, y
|
||||
|
||||
|
||||
def spectrogram_to_wave(spec, hop_length=1024):
|
||||
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)
|
||||
wave = np.asfortranarray([wave_left, wave_right])
|
||||
|
||||
return wave
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import cv2
|
||||
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, y = align_wave_head_and_tail(X, y, 44100)
|
||||
|
||||
X_spec = wave_to_spectrogram(X, 1024, 2048)
|
||||
y_spec = wave_to_spectrogram(y, 1024, 2048)
|
||||
|
||||
y_spec = reduce_vocal_aggressively(X_spec, y_spec, 0.2)
|
||||
v_spec = X_spec - y_spec
|
||||
|
||||
# v_mask = np.abs(v_spec) > np.abs(y_spec)
|
||||
# y_spec = X_spec - v_spec * v_mask
|
||||
# v_spec = X_spec - y_spec
|
||||
|
||||
X_mag = np.abs(X_spec)
|
||||
y_mag = np.abs(y_spec)
|
||||
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('test_X.jpg', X_image)
|
||||
cv2.imwrite('test_y.jpg', y_image)
|
||||
cv2.imwrite('test_v.jpg', v_image)
|
||||
|
||||
sf.write('test_X.wav', spectrogram_to_wave(X_spec).T, 44100)
|
||||
sf.write('test_y.wav', spectrogram_to_wave(y_spec).T, 44100)
|
||||
sf.write('test_v.wav', spectrogram_to_wave(v_spec).T, 44100)
|
||||
Loading…
Reference in New Issue
Block a user