def __init__(self, params):
super().__init__()
self.samples = defaultdict(lambda: {
'mix': None,
'vocl': None,
'inst': []
})
path = params['path']
self.number_if_samples = 0
for a in iterate_files(os.path.join(path, 'Mixtures'), '.wav'):
key = os.path.basename(os.path.dirname(a))
self.samples[key]['mix'] = a
self.number_if_samples += 1
for a in iterate_files(os.path.join(path, 'Sources'), '.wav'):
key = os.path.basename(os.path.dirname(a))
if a.endswith('vocals.wav'):
self.samples[key]['vocl'] = a
self.add_frames_num('vocl', a)
else:
self.samples[key]['inst'].append(a)
self.add_frames_num('inst', a)
self.number_if_samples += 1
self.samples = dict(self.samples)
def pretrain():
model = self_supervized_model(400)
model.compile(loss='sparse_categorical_crossentropy',
optimizer=keras.optimizers.Adam(learning_rate=8e-5),
metrics='accuracy')
config = {
'batch_size': 128,
'maxlen': 80,
'units': 400,
'model': model,
}
run = wandb.init(project="dotter",
group="pretrain",
tags=[],
config=config)
wandb_callback = wandb.keras.WandbCallback(log_batch_frequency=50,
save_model=False,
log_weights=False)
with run:
for fname in utils.iterate_files(["../wikipedia/AA"]):
name = fname.split('/')[-1]
raw_y = load_plaintext(fname, 80)
utils.shuffle_in_unison(raw_y)
x, y = get_masked(raw_y, 0.3)
model.fit(x, y, batch_size=128, validation_split=0.1, callbacks=[wandb_callback])
model.save(f'{pretrain_path}/{name}.h5', save_format='tf')
model.save(model_name, save_format='tf')
return model
def diacritize_all(sysname):
for filename in utils.iterate_files([basepath]):
# if filename.endswith(r'\nrg\6.txt') or filename.endswith(r'president\6.txt'):
# continue
print(filename, end=' ' * 30 + '\r', flush=True)
actual = diacritize(sysname, filename)
outfile = filename.replace('expected', sysname)
Path(outfile).parent.mkdir(parents=True, exist_ok=True)
with open(outfile, 'w', encoding='utf8') as f:
f.write(actual)
def __init__(self, params):
super().__init__()
self.samples = defaultdict(lambda: {
'mix': None,
'vocl': None,
'inst': None
})
path = params['path']
for a in iterate_files(path, '.wav'):
key = os.path.basename(os.path.dirname(a))
if a.endswith('mix.wav'):
self.samples[key]['mix'] = a
elif a.endswith('source-01.wav'):
self.samples[key]['inst'] = a
self.add_frames_num('inst', a)
else:
self.samples[key]['vocl'] = a
self.add_frames_num('vocl', a)
self.samples = dict(self.samples)
def __init__(self, params):
super().__init__()
self.samples = defaultdict(lambda: {'mix': [], 'vocl': [], 'inst': []})
path = params['path']
self.net_vocals = 0
self.net_insts = 0
for a in iterate_files(os.path.join(path, 'separation'), '.wav'):
key = os.path.basename(os.path.dirname(os.path.dirname(a))).lower()
filename = os.path.basename(a).lower()
if self._is_vocal_name(filename):
self.samples[key]['vocl'].append(a)
# self.net_vocls += get_net_duration(a)
self.add_frames_num('vocl', a)
continue
if 'mix' in filename:
self.samples[key]['mix'].append(a)
continue
self.samples[key]['inst'].append(a)
self.add_frames_num('inst', a)
# self.net_insts += get_net_duration(a)
self.samples = dict(self.samples)
def __init__(self, params):
super().__init__()
path = params['path']
self.vocl = []
self.inst = []
reg = re.compile('\[(.*?)\]')
for a in iterate_files(path, '.wav'):
try:
txt_filename = a[:-4] + '.txt'
if 'voi' in map(str.strip, open(txt_filename)):
self.vocl.append(a)
self.add_frames_num('vocl', a)
else:
self.inst.append(a)
self.add_frames_num('inst', a)
except FileNotFoundError:
if 'voi' in reg.findall(a):
self.vocl.append(a)
self.add_frames_num('vocl', a)
else:
self.inst.append(a)
self.add_frames_num('inst', a)
def collect_tokens(paths: Iterable[str]):
return tokenize(itertools.chain.from_iterable(iterate_file(path) for path in utils.iterate_files(paths)))
def read_corpora(base_paths):
return [(filename, list(hebrew.iterate_file(filename)))
for filename in utils.iterate_files(base_paths)]
