funasr/models/e2e_asr_contextual_paraformer.py
@@ -125,6 +125,7 @@ if self.crit_attn_weight > 0: self.attn_loss = torch.nn.L1Loss() self.crit_attn_smooth = crit_attn_smooth self.length_normalized_loss = length_normalized_loss def forward( self, @@ -231,6 +232,8 @@ stats["loss"] = torch.clone(loss.detach()) # force_gatherable: to-device and to-tensor if scalar for DataParallel if self.length_normalized_loss: batch_size = int((text_lengths + self.predictor_bias).sum()) loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) return loss, stats, weight
