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import argparse
import glob
import multiprocessing
import re
from functools import partial
from pathlib import Path
import librosa
import numpy
from become_yukarin import AcousticConverter
from become_yukarin.config.config import create_from_json as create_config
parser = argparse.ArgumentParser()
parser.add_argument('model_names', nargs='+')
parser.add_argument('-md', '--model_directory', type=Path, default=Path('/mnt/dwango/hiroshiba/become-yukarin/'))
parser.add_argument('-iwd', '--input_wave_directory', type=Path,
default=Path('/mnt/dwango/hiroshiba/become-yukarin/dataset/hiho-wave/hiho-pause-atr503-subset/'))
parser.add_argument('-it', '--iteration', type=int)
parser.add_argument('-g', '--gpu', type=int)
args = parser.parse_args()
model_directory = args.model_directory # type: Path
input_wave_directory = args.input_wave_directory # type: Path
it = args.iteration
gpu = args.gpu
paths_test = list(Path('./test_data/').glob('*.wav'))
def extract_number(f):
s = re.findall("\d+", str(f))
return int(s[-1]) if s else -1
def process(p: Path, acoustic_converter: AcousticConverter):
try:
if p.suffix in ['.npy', '.npz']:
fn = glob.glob(str(input_wave_directory / p.stem) + '.*')[0]
p = Path(fn)
wave = acoustic_converter(p)
librosa.output.write_wav(str(output / p.stem) + '.wav', wave.wave, wave.sampling_rate, norm=True)
except:
import traceback
print('error!', str(p))
traceback.format_exc()
for model_name in args.model_names:
base_model = model_directory / model_name
config = create_config(base_model / 'config.json')
input_paths = list(sorted([Path(p) for p in glob.glob(str(config.dataset.input_glob))]))
numpy.random.RandomState(config.dataset.seed).shuffle(input_paths)
path_train = input_paths[0]
path_test = input_paths[-1]
if it is not None:
model_path = base_model / 'predictor_{}.npz'.format(it)
else:
model_paths = base_model.glob('predictor_*.npz')
model_path = list(sorted(model_paths, key=extract_number))[-1]
print(model_path)
acoustic_converter = AcousticConverter(config, model_path, gpu=gpu)
output = Path('./output').absolute() / base_model.name
output.mkdir(exist_ok=True)
paths = [path_train, path_test] + paths_test
process_partial = partial(process, acoustic_converter=acoustic_converter)
if gpu is None:
pool = multiprocessing.Pool()
pool.map(process_partial, paths)
else:
list(map(process_partial, paths))
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