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)