get_vad=2,
get_qspec=True,
pitch_threshold=0.8,
cqt_bins=96,
vad_smooth=3,
vad_minlen=0.1,
pca=True,
pca_whiten=False,
center=True,
save_stats=True,
substitute_nan=None,
dtype='float16',
datatype='memmap',
ncache=0.12,
ncpu=8)
with utils.UnitTimer():
feat.run()
shutil.copy(os.path.join(datapath, 'README.md'),
os.path.join(output_path, 'README.md'))
# ====== check the preprocessed dataset ====== #
ds = F.Dataset(output_path, read_only=True)
print('Output path:', output_path)
print(ds)
for n in ds.keys():
if '_pca' in n:
pca = ds[n]
if pca.components_ is None:
print(n, 'components is None !')
elif np.any(np.isnan(pca.components_)):
print(n, 'contains NaN !')
import numba as nb
from odin import utils
X = np.random.rand(50000, 123)
def normal(x):
idx = list(range(0, x.shape[0], 5))
_ = [x[i:i + 21].ravel() for i in idx if (i + 21) <= x.shape[0]]
x = np.asarray(_) if len(_) > 1 else _[0]
# np.random.shuffle(x)
return x
with utils.UnitTimer(12):
for i in range(12):
x1 = normal(X)
print(x1.shape)
tmp = np.ones((20000, 2583))
@nb.jit('f8[:,:](f8[:,:], f8[:,:])',
locals={},
nopython=True,
nogil=True,
cache=True)
def fast(x, tmp):
idx = list(range(0, x.shape[0], 5))
count = 0