Inference

src/diffwave/learner.py

@@ -103,6 +103,8 @@ class DiffWaveLearner:
     while True:
       for features in tqdm(self.dataset, desc=f'Epoch {self.step // len(self.dataset)}') if self.is_master else self.dataset:
         if max_steps is not None and self.step >= max_steps:
+          # Save final checkpoint.
+          self.save_to_checkpoint()
           return
         features = _nested_map(features, lambda x: x.to(device) if isinstance(x, torch.Tensor) else x)
         loss = self.train_step(features)
@@ -145,11 +147,11 @@ class DiffWaveLearner:
 
   def _write_summary(self, step, features, loss):
     writer = self.summary_writer or SummaryWriter(self.model_dir, purge_step=step)
-    writer.add_audio('feature/audio', features['audio'][0], step, sample_rate=self.params.sample_rate)
+    # the following line will print a warning if the audio amplitude is out of range
+    # writer.add_audio('feature/audio', features['audio'][0], step, sample_rate=self.params.sample_rate)
     if not self.params.unconditional:
       writer.add_image('feature/spectrogram', torch.flip(features['spectrogram'][:1], [1]), step)
     writer.add_scalar('train/loss', loss, step)
-    # the following line will print a warning if the audio amplitude is out of range
     writer.add_scalar('train/grad_norm', self.grad_norm, step)
     writer.flush()
     self.summary_writer = writer