fix train valid log

data_utils/featurizer/audio_featurizer.py

@@ -114,7 +114,6 @@ class AudioFeaturizer(object):
         else:
             raise ValueError("Unknown specgram_type %s. "
                              "Supported values: linear." % self._specgram_type)
-        print('feat_dim:', feat_dim)
         return feat_dim
 
     def _compute_specgram(self, samples, sample_rate):

model_utils/model.py

@@ -150,8 +150,8 @@ class DeepSpeech2Trainer(Trainer):
         self.logger.info("Setup model/optimizer/criterion!")
 
     def compute_losses(self, inputs, outputs):
-        _, texts, _, texts_len = inputs
-        logits, logits_len = outputs
+        del inputs
+        logits, texts, logits_len, texts_len = outputs
         loss = self.criterion(logits, texts, logits_len, texts_len)
         return loss
 
@@ -169,8 +169,8 @@ class DeepSpeech2Trainer(Trainer):
         self.optimizer.step()
         iteration_time = time.time() - start
 
-        losses_np = {'loss': float(loss)}
-        msg = "Rank: {}, ".format(dist.get_rank())
+        losses_np = {'train_loss': float(loss)}
+        msg = "Train: Rank: {}, ".format(dist.get_rank())
         msg += "epoch: {}, ".format(self.epoch)
         msg += "step: {}, ".format(self.iteration)
 
@@ -185,6 +185,9 @@ class DeepSpeech2Trainer(Trainer):
                 self.visualizer.add_scalar("train/{}".format(k), v,
                                            self.iteration)
 
+    def compute_metrics(self, inputs, outputs):
+        pass
+
     @mp_tools.rank_zero_only
     @paddle.no_grad()
     def valid(self):
@@ -192,15 +195,17 @@ class DeepSpeech2Trainer(Trainer):
         for i, batch in enumerate(self.valid_loader):
             audio, text, audio_len, text_len = batch
             outputs = self.model(audio, text, audio_len, text_len)
-            losses = self.compute_losses(batch, outputs)
+            loss = self.compute_losses(batch, outputs)
+            metrics = self.compute_metrics(batch, outputs)
 
-            valid_losses['val_loss'].append(float(v))
+            valid_losses['val_loss'].append(float(loss))
 
         # write visual log
         valid_losses = {k: np.mean(v) for k, v in valid_losses.items()}
 
         # logging
         msg = "Valid: "
+        msg += "epoch: {}, ".format(self.epoch)
         msg += "step: {}, ".format(self.iteration)
         msg += ', '.join('{}: {:>.6f}'.format(k, v)
                          for k, v in valid_losses.items())

model_utils/network.py

@@ -532,7 +532,7 @@ class DeepSpeech2(nn.Layer):
         text_len: shape [B]
         """
         logits, _, audio_len = self.predict(audio, audio_len)
-        return logits, audio_len
+        return logits, text, audio_len, text_len
 
 
 class DeepSpeech2Loss(nn.Layer):