def _adjust_time_resolution(self, batch, local_condition, max_time_steps):
'''Adjust time resolution between audio and local condition
'''
if local_condition:
new_batch = []
for b in batch:
x, c, g, l = b
self._assert_ready_for_upsample(x, c)
if max_time_steps is not None:
max_steps = _ensure_divisible(
max_time_steps, audio.get_hop_size(self._hparams),
True)
if len(x) > max_time_steps:
max_time_frames = max_steps // audio.get_hop_size(
self._hparams)
start = np.random.randint(0, len(c) - max_time_frames)
time_start = start * audio.get_hop_size(self._hparams)
x = x[time_start:time_start + max_time_frames *
audio.get_hop_size(self._hparams)]
c = c[start:start + max_time_frames, :]
self._assert_ready_for_upsample(x, c)
new_batch.append((x, c, g, l))
return new_batch
else:
new_batch = []
for b in batch:
x, c, g, l = b
x = audio.trim(x)
if max_time_steps is not None and len(x) > max_time_steps:
start = np.random.randint(0, len(c) - max_time_steps)
x = x[start:start + max_time_steps]
new_batch.append((x, c, g, l))
return new_batch
def _adjust_time_resolution(self, batch, local_condition, max_time_steps):
'''Adjust time resolution between audio and local condition
'''
if local_condition:
new_batch = []
for b in batch:
x, c, g, l = b
if len(x) < len(c) * audio.get_hop_size(self._hparams):
pad_length = audio.get_hop_size(
self._hparams) * len(c) - len(x)
if pad_length % 2 == 0:
x = np.pad(x, (pad_length // 2, pad_length // 2),
mode='constant',
constant_values=_pad)
else:
x = np.pad(x, (pad_length // 2, (pad_length + 1) // 2),
mode='constant',
constant_values=_pad)
else:
c = self._pad_specs(
c,
len(x) // audio.get_hop_size(self._hparams))
self._assert_ready_for_upsample(x, c)
if max_time_steps is not None:
max_steps = _ensure_divisible(
max_time_steps, audio.get_hop_size(self._hparams),
True)
if len(x) > max_time_steps:
max_time_frames = max_steps // audio.get_hop_size(
self._hparams)
start = np.random.randint(0, len(c) - max_time_frames)
time_start = start * audio.get_hop_size(self._hparams)
x = x[time_start:time_start + max_time_frames *
audio.get_hop_size(self._hparams)]
c = c[start:start + max_time_frames, :]
self._assert_ready_for_upsample(x, c)
new_batch.append((x, c, g, l))
return new_batch
else:
new_batch = []
for b in batch:
x, c, g, l = b
x = audio.trim(x)
if max_time_steps is not None and len(x) > max_time_steps:
start = np.random.randint(0, len(c) - max_time_steps)
x = x[start:start + max_time_steps]
new_batch.append((x, c, g, l))
return new_batch