fix the bug of the ctc

fairseq/criterions/label_smoothed_cross_entropy_with_ctc.py

@@ -188,7 +188,8 @@ class LabelSmoothedCrossEntropyCriterionWithCTC(
 
 
         return loss
         return loss
 
 
-    def reduce_metrics(self, logging_outputs) -> None:
+    @staticmethod
+    def reduce_metrics(logging_outputs) -> None:
         """Aggregate logging outputs from data parallel training."""
         """Aggregate logging outputs from data parallel training."""
         loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
         loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
         trans_loss_sum = utils.item(
         trans_loss_sum = utils.item(
@@ -197,10 +198,9 @@ class LabelSmoothedCrossEntropyCriterionWithCTC(
         nll_loss_sum = utils.item(
         nll_loss_sum = utils.item(
             sum(log.get("nll_loss", 0) for log in logging_outputs)
             sum(log.get("nll_loss", 0) for log in logging_outputs)
         )
         )
-        if self.ctc_weight > 0:
-            ctc_loss_sum = utils.item(
-                sum(log.get("ctc_loss", 0) for log in logging_outputs)
-            )
+        ctc_loss_sum = utils.item(
+            sum(log.get("ctc_loss", 0) for log in logging_outputs)
+        )
         ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
         ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
         sample_size = utils.item(
         sample_size = utils.item(
             sum(log.get("sample_size", 0) for log in logging_outputs)
             sum(log.get("sample_size", 0) for log in logging_outputs)
@@ -215,13 +215,12 @@ class LabelSmoothedCrossEntropyCriterionWithCTC(
         metrics.log_scalar(
         metrics.log_scalar(
             "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
             "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
         )
         )
-        if self.ctc_weight > 0:
-            metrics.log_scalar(
-                "ctc_loss",
-                ctc_loss_sum / sample_size / math.log(2),
-                sample_size,
-                round=3,
-            )
+        metrics.log_scalar(
+            "ctc_loss",
+            ctc_loss_sum / sample_size / math.log(2),
+            sample_size,
+            round=3,
+        )
         metrics.log_derived(
         metrics.log_derived(
             "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
             "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
         )
         )