def _prepare_for_export(self, **kwargs):
m_count = 0
for m in self.modules():
if isinstance(m, MaskedConv1d):
m.use_mask = False
m_count += 1
Exportable._prepare_for_export(self, **kwargs)
logging.warning(f"Turned off {m_count} masked convolutions")
def _prepare_for_export(self, **kwargs):
m_count = 0
for m in self.modules():
if type(m).__name__ == "MaskedConv1d":
m.use_mask = False
m_count += 1
if m_count > 0:
logging.warning(f"Turned off {m_count} masked convolutions")
Exportable._prepare_for_export(self, **kwargs)
def _prepare_for_export(self, **kwargs):
# extend masks to configured maximum
max_len = self.pos_emb_max_len
if 'input_example' in kwargs:
m_len = kwargs['input_example'][0].size(-1)
if m_len > max_len:
max_len = m_len
logging.info(f"Extending input audio length to {max_len}")
self.set_max_audio_length(max_len)
Exportable._prepare_for_export(self, **kwargs)
def _prepare_for_export(self, **kwargs):
m_count = 0
for m in self.modules():
if isinstance(m, MaskedConv1d):
if self._rnnt_export:
pass
else:
m.use_mask = False
m_count += 1
if isinstance(m, SqueezeExcite):
m._se_pool_step = m._se_pool_step_export
Exportable._prepare_for_export(self, **kwargs)
logging.warning(f"Turned off {m_count} masked convolutions")
def _prepare_for_export(self, **kwargs):
m_count = 0
stride = 1
one_hour = 100 * 60 * 60 * 1 # 1 sec / 0.01 window stride = 100 frames / second * 60 sec * 60 min * 1 hour
for name, m in self.named_modules():
if isinstance(m, MaskedConv1d):
m.use_mask = False
m_count += 1
if isinstance(m, MaskedConv1d):
if m.conv.stride[0] > 1 and 'mconv' in name:
stride = stride * m.conv.stride[0]
if isinstance(m, SqueezeExcite):
m.set_max_len(int(one_hour // stride)) # One hour divided by current stride level
Exportable._prepare_for_export(self, **kwargs)
logging.warning(f"Turned off {m_count} masked convolutions")
def _prepare_for_export(self):
Exportable._prepare_for_export(self)