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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)
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