def clean():
import src.utils.path as pth
import src.utils.logger as log
import src.parser.toml as tml
from shutil import rmtree
log.info("Cleaning project")
pth.__remove_file(tml.value('json', section='data', subkey='fname'))
pth.__remove_file(tml.value('numpy', section='data', subkey='fname'))
dnames = tml.value('dnames', section='demo')
if pth.__exists(dnames['input']):
rmtree(dnames['input'])
if pth.__exists(dnames['output']):
rmtree(dnames['output'])
pth.__remove_file(tml.value('fx_name', section='demo'))
dnames = tml.value('dnames', section='neuralnet')
if pth.__exists(dnames['predicted_labels']):
rmtree(dnames['predicted_labels'])
if pth.__exists(dnames['expected_labels']):
rmtree(dnames['expected_labels'])
if pth.__exists(dnames['original_data']):
rmtree(dnames['original_data'])
def demo():
import src.utils.logger as log
import src.utils.path as pth
import src.parser.toml as tml
from src.utils.tools import download, extract
# Downloading data from URLs and extracting downloaded files
dry_url = tml.value('urls', section='demo', subkey='dry')
fx_url = tml.value('urls', section='demo', subkey='fx')
dry_dpath = tml.value('dnames', section='demo', subkey='input')
fx_fname = tml.value('fx_name', section='demo')
log.info("Downloading and extracting dataset and fx")
fx_fpath = download(fx_url)
pth.__rename_file(fx_fpath, fx_fname)
if not pth.__exists(dry_dpath):
dry_fpath = download(dry_url)
extract(dry_fpath, dry_dpath)
else:
log.warning("\"{0}\" already exist, skipping dataset downloading".format(dry_dpath))
run(dry_dpath, fx_fname, tml.value('dnames', section='demo', subkey='output'))
def rfname(path='.', prefix=''):
"""Return path to random file name at <path> prefixed by <prefix>."""
fpath = pth.__join_path(path, ''.join([prefix, rstr()]))
while (pth.__exists(fpath)):
fpath = pth.__join_path(path, ''.join([prefix, rstr()]))
return fpath
def generate_dataset(dry_dpath, fx_fpath, output_dpath=None, func=None):
"""Generate dataset of wet samples.
Fill a JSON file wich matches dry and wet samples.
"""
if not output_dpath:
output_dpath = mkrdir()
elif not pth.__exists(output_dpath):
pth.__make_dir(output_dpath)
fx = _read(fx_fpath)
jsn.init()
info, save_steps = dict(), tml.value('json',
section='data',
subkey='save_steps')
for idx, dryfpath in enumerate(__list_audio_files(dry_dpath)):
wet_signal = apply_fx(_read(dryfpath), fx, func)
dpath = pth.__with_extension(
rfname(path=output_dpath, prefix='{0}_'.format(idx)), '.wav')
_save(wet_signal, dpath)
info[dryfpath] = dpath
if (idx + 1) % save_steps == 0:
log.debug("{0} samples processed".format(idx + 1))
jsn.dump(info)
jsn.dump(info)
def mkrdir(path='.', prefix=''):
"""Create a random directory at <path>, prefixed by <prefix>.
Return path to created directory.
"""
dpath = pth.__join_path(path, ''.join([prefix, rstr()]))
while (pth.__exists(dpath)):
dpath = pth.__join_path(path, ''.join([prefix, rstr()]))
pth.__make_dir(dpath)
return dpath
def _save(npy_array, fpath, override=True):
"""Save a numpy array as a wave file at <fpath>."""
if not override and pth.__exists(fpath):
return
while pth.__file_name(fpath).endswith('.'):
fpath = pth.__with_name(fpath, pth.__file_name(fpath)[:-1])
if pth.__file_extension(fpath) != '.wav':
fpath = pth.__with_extension(fpath, '.wav')
write(fpath, tml.value('s_rate', section='audio'), npy_array)
def _export(npy_arrays, outdpath=None, override=True):
"""Save a list of numpy arrays as wave files at <outdpath>.
If <outdpath> is None, create a random directory.
If <oudtpath> doesn't exist, it creates it.
"""
if outdpath is None:
outdpath = mkrdir()
elif pth.__is_file(outdpath):
log.critical(
"Can't export songs in {0} since it is a file".format(outdpath))
elif not pth.__exists(outdpath):
pth.__make_dir(outdpath)
for idx, npy_array in enumerate(npy_arrays):
_save(npy_array, rfname(path=outdpath, prefix='{0}_'.format(idx)),
override)