Fixup overflow (#2218)

* Update overflow config

* Pulling shuffle and drop_last  from config

* Print training stats for overflow

TTS/tts/configs/overflow_config.py

@@ -169,8 +169,9 @@ class OverflowConfig(BaseTTSConfig):  # The classname has to be camel case
     lr_scheduler: str = None
 
     # overrides
-    min_seq_len: int = 3
-    max_seq_len: int = 500
+    min_text_len: int = 10
+    max_text_len: int = 500
+    min_audio_len: int = 512
 
     # testing
     test_sentences: List[str] = field(

TTS/tts/configs/shared_configs.py

@@ -230,6 +230,13 @@ class BaseTTSConfig(BaseTrainingConfig):
             If True, the data loader will start loading the longest batch first. It is useful for checking OOM issues.
             Defaults to False.
 
+        shuffle (bool):
+            If True, the data loader will shuffle the dataset when there is not sampler defined. Defaults to True.
+
+        drop_last (bool):
+            If True, the data loader will drop the last batch if it is not complete. It helps to prevent
+            issues that emerge from the partial batch statistics. Defaults to True.
+
         add_blank (bool):
             Add blank characters between each other two characters. It improves performance for some models at expense
             of slower run-time due to the longer input sequence.
@@ -309,6 +316,8 @@ class BaseTTSConfig(BaseTrainingConfig):
     precompute_num_workers: int = 0
     use_noise_augment: bool = False
     start_by_longest: bool = False
+    shuffle: bool = False
+    drop_last: bool = False
     # dataset
     datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])
     # optimizer

TTS/tts/models/base_tts.py

@@ -345,9 +345,9 @@ class BaseTTS(BaseTrainerModel):
             loader = DataLoader(
                 dataset,
                 batch_size=config.eval_batch_size if is_eval else config.batch_size,
-                shuffle=True,  # if there is no other sampler
+                shuffle=config.shuffle if sampler is not None else False,  # if there is no other sampler
                 collate_fn=dataset.collate_fn,
-                drop_last=False,  # setting this False might cause issues in AMP training.
+                drop_last=config.drop_last,  # setting this False might cause issues in AMP training.
                 sampler=sampler,
                 num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
                 pin_memory=False,

TTS/tts/models/overflow.py

@@ -159,6 +159,15 @@ class Overflow(BaseTTS):
 
         return outputs
 
+    @staticmethod
+    def _training_stats(batch):
+        stats = {}
+        stats["avg_text_length"] = batch["text_lengths"].float().mean()
+        stats["avg_spec_length"] = batch["mel_lengths"].float().mean()
+        stats["avg_text_batch_occupancy"] = (batch["text_lengths"].float() / batch["text_lengths"].float().max()).mean()
+        stats["avg_spec_batch_occupancy"] = (batch["mel_lengths"].float() / batch["mel_lengths"].float().max()).mean()
+        return stats
+
     def train_step(self, batch: dict, criterion: nn.Module):
         text_input = batch["text_input"]
         text_lengths = batch["text_lengths"]
@@ -171,9 +180,10 @@ class Overflow(BaseTTS):
             mels=mel_input,
             mel_len=mel_lengths,
         )
+        loss_dict = criterion(outputs["log_probs"] / (mel_lengths.sum() + text_lengths.sum()))
 
-        loss_dict = criterion(outputs["log_probs"])
-
+        # for printing useful statistics on terminal
+        loss_dict.update(self._training_stats(batch))
         return outputs, loss_dict
 
     def eval_step(self, batch: Dict, criterion: nn.Module):