fix logger

deepspeech/__init__.py

@@ -410,13 +410,11 @@ def ctc_loss(logits,
                                            input_lengths, label_lengths)
 
     loss_out = paddle.fluid.layers.squeeze(loss_out, [-1])
-    logger.debug(f"warpctc loss: {loss_out}/{loss_out.shape} ")
     assert reduction in ['mean', 'sum', 'none']
     if reduction == 'mean':
         loss_out = paddle.mean(loss_out / label_lengths)
     elif reduction == 'sum':
         loss_out = paddle.sum(loss_out)
-    logger.debug(f"ctc loss: {loss_out}")
     return loss_out
 
 

deepspeech/exps/u2/model.py

@@ -89,8 +89,9 @@ class U2Trainer(Trainer):
 
         if (batch_index + 1) % train_conf.accum_grad == 0:
             if dist.get_rank() == 0 and self.visualizer:
-                losses_np.update({"lr": self.lr_scheduler()})
+                losses_np_v = losses_np.copy()
-                self.visualizer.add_scalars("step", losses_np, self.iteration)
+                losses_np_v.update({"lr": self.lr_scheduler()})
+                self.visualizer.add_scalars("step", losses_np_v, self.iteration)
             self.optimizer.step()
             self.optimizer.clear_grad()
             self.lr_scheduler.step()
@@ -171,8 +172,9 @@ class U2Trainer(Trainer):
         logger.info(msg)
 
         if self.visualizer:
-            valid_losses.update({"lr": self.lr_scheduler()})
+            valid_losses_v = valid_losses.copy()
-            self.visualizer.add_scalars('epoch', valid_losses, self.epoch)
+            valid_losses_v.update({"lr": self.lr_scheduler()})
+            self.visualizer.add_scalars('epoch', valid_losses_v, self.epoch)
         return valid_losses
 
     def setup_dataloader(self):

deepspeech/io/dataset.py

@@ -297,13 +297,13 @@ class ManifestDataset(Dataset):
         else:
             speech_segment = SpeechSegment.from_file(audio_file, transcript)
         load_wav_time = time.time() - start_time
-        logger.debug(f"load wav time: {load_wav_time}")
+        #logger.debug(f"load wav time: {load_wav_time}")
 
         # audio augment
         start_time = time.time()
         self._augmentation_pipeline.transform_audio(speech_segment)
         audio_aug_time = time.time() - start_time
-        logger.debug(f"audio augmentation time: {audio_aug_time}")
+        #logger.debug(f"audio augmentation time: {audio_aug_time}")
 
         start_time = time.time()
         specgram, transcript_part = self._speech_featurizer.featurize(
@@ -311,13 +311,13 @@ class ManifestDataset(Dataset):
         if self._normalizer:
             specgram = self._normalizer.apply(specgram)
         feature_time = time.time() - start_time
-        logger.debug(f"audio & test feature time: {feature_time}")
+        #logger.debug(f"audio & test feature time: {feature_time}")
 
         # specgram augment
         start_time = time.time()
         specgram = self._augmentation_pipeline.transform_feature(specgram)
         feature_aug_time = time.time() - start_time
-        logger.debug(f"audio feature augmentation time: {feature_aug_time}")
+        #logger.debug(f"audio feature augmentation time: {feature_aug_time}")
         return specgram, transcript_part
 
     def _instance_reader_creator(self, manifest):

deepspeech/models/u2.py

@@ -159,7 +159,7 @@ class U2BaseModel(nn.Module):
         start = time.time()
         encoder_out, encoder_mask = self.encoder(speech, speech_lengths)
         encoder_time = time.time() - start
-        logger.debug(f"encoder time: {encoder_time}")
+        #logger.debug(f"encoder time: {encoder_time}")
         #TODO(Hui Zhang): sum not support bool type
         #encoder_out_lens = encoder_mask.squeeze(1).sum(1)  #[B, 1, T] -> [B]
         encoder_out_lens = encoder_mask.squeeze(1).cast(paddle.int64).sum(
@@ -172,7 +172,7 @@ class U2BaseModel(nn.Module):
             loss_att, acc_att = self._calc_att_loss(encoder_out, encoder_mask,
                                                     text, text_lengths)
             decoder_time = time.time() - start
-            logger.debug(f"decoder time: {decoder_time}")
+            #logger.debug(f"decoder time: {decoder_time}")
 
         # 2b. CTC branch
         loss_ctc = None
@@ -181,7 +181,7 @@ class U2BaseModel(nn.Module):
             loss_ctc = self.ctc(encoder_out, encoder_out_lens, text,
                                 text_lengths)
             ctc_time = time.time() - start
-            logger.debug(f"ctc time: {ctc_time}")
+            #logger.debug(f"ctc time: {ctc_time}")
 
         if loss_ctc is None:
             loss = loss_att