Cool_Tools/ultimatevocalremovergui
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Anjok07 2020-11-09 04:27:44 -06:00 committed by GitHub
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lib/dataset.py
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import os
import numpy as np
import torch
from tqdm import tqdm
from lib import spec_utils
class VocalRemoverValidationSet(torch.utils.data.Dataset):
def __init__(self, filelist):
self.filelist = filelist
def __len__(self):
return len(self.filelist)
def __getitem__(self, idx):
path = self.filelist[idx]
data = np.load(path)
return data['X'], data['y']
def mixup_generator(X, y, rate, alpha):
perm = np.random.permutation(len(X))[:int(len(X) * rate)]
for i in range(len(perm) - 1):
lam = np.random.beta(alpha, alpha)
X[perm[i]] = lam * X[perm[i]] + (1 - lam) * X[perm[i + 1]]
y[perm[i]] = lam * y[perm[i]] + (1 - lam) * y[perm[i + 1]]
return X, y
def get_oracle_data(X, y, instance_loss, oracle_rate, oracle_drop_rate):
k = int(len(X) * oracle_rate * (1 / (1 - oracle_drop_rate)))
n = int(len(X) * oracle_rate)
idx = np.argsort(instance_loss)[::-1][:k]
idx = np.random.choice(idx, n, replace=False)
oracle_X = X[idx].copy()
oracle_y = y[idx].copy()
return oracle_X, oracle_y, idx
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, offset):
len_dataset = patches * len(filelist)
X_dataset = np.zeros(
(len_dataset, 2, hop_length, cropsize), dtype=np.float32)
y_dataset = np.zeros(
(len_dataset, 2, hop_length, cropsize), dtype=np.float32)
for i, (X_path, y_path) in enumerate(tqdm(filelist)):
p = np.random.uniform()
if p < 0.1:
X_path.replace(os.path.splitext(X_path)[1], '_pitch-1.wav')
y_path.replace(os.path.splitext(y_path)[1], '_pitch-1.wav')
elif p < 0.2:
X_path.replace(os.path.splitext(X_path)[1], '_pitch1.wav')
y_path.replace(os.path.splitext(y_path)[1], '_pitch1.wav')
X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length)
coeff = np.max([X.max(), y.max()])
X, y = X / coeff, y / coeff
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]]
if np.random.uniform() < 0.5:
# swap channel
X_dataset[idx] = X_dataset[idx, ::-1]
y_dataset[idx] = y_dataset[idx, ::-1]
return X_dataset, y_dataset
def make_validation_set(filelist, cropsize, sr, hop_length, offset):
patch_list = []
outdir = 'cs{}_sr{}_hl{}_of{}'.format(cropsize, sr, hop_length, offset)
os.makedirs(outdir, 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)
coeff = np.max([X.max(), y.max()])
X, y = X / coeff, y / coeff
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(outdir, '{}_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)