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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 SuperResolution
from become_yukarin.config.sr_config import create_from_json as create_config
from become_yukarin.dataset.dataset import AcousticFeatureProcess
from become_yukarin.dataset.dataset import WaveFileLoadProcess
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/yukari-wave/yukari-news/'))
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
gpu = args.gpu
paths_test = list(Path('./test_data_sr/').glob('*.wav'))
def extract_number(f):
s = re.findall("\d+", str(f))
return int(s[-1]) if s else -1
def process(p: Path, super_resolution: SuperResolution):
param = config.dataset.param
wave_process = WaveFileLoadProcess(
sample_rate=param.voice_param.sample_rate,
top_db=None,
)
acoustic_feature_process = AcousticFeatureProcess(
frame_period=param.acoustic_feature_param.frame_period,
order=param.acoustic_feature_param.order,
alpha=param.acoustic_feature_param.alpha,
f0_estimating_method=param.acoustic_feature_param.f0_estimating_method,
)
try:
if p.suffix in ['.npy', '.npz']:
p = glob.glob(str(input_wave_directory / p.stem) + '.*')[0]
p = Path(p)
input = acoustic_feature_process(wave_process(str(p)))
wave = super_resolution(input.spectrogram, acoustic_feature=input, sampling_rate=param.voice_param.sample_rate)
librosa.output.write_wav(str(output / p.stem) + '.wav', wave.wave, wave.sampling_rate, norm=True)
except:
import traceback
print('error!', str(p))
traceback.print_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]
model_paths = base_model.glob('predictor*.npz')
model_path = list(sorted(model_paths, key=extract_number))[-1]
print(model_path)
super_resolution = SuperResolution(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, super_resolution=super_resolution)
if gpu is None:
pool = multiprocessing.Pool()
pool.map(process_partial, paths)
else:
list(map(process_partial, paths))
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