Add argumnet to disable storage

TTS/bin/eval_encoder.py

@@ -83,7 +83,7 @@ for idx, wav_file in enumerate(tqdm(wav_files)):
 acc_avg = 0
 for key in class_acc_dict:
     acc = sum(class_acc_dict[key])/len(class_acc_dict[key])
-    print("Class", key, "ACC:", acc)
+    print("Class", key, "Accuracy:", acc)
     acc_avg += acc
 
-print("Average Acc:", acc_avg/len(class_acc_dict))
+print("Average Accuracy:", acc_avg/len(class_acc_dict))

TTS/bin/train_encoder.py

@@ -41,11 +41,12 @@ def setup_loader(ap: AudioProcessor, is_val: bool = False, verbose: bool = False
             voice_len=c.voice_len,
             num_utter_per_class=c.num_utter_per_class,
             num_classes_in_batch=c.num_classes_in_batch,
+            use_storage=c.use_storage,
             skip_classes=c.skip_classes,
             storage_size=c.storage["storage_size"],
             sample_from_storage_p=c.storage["sample_from_storage_p"],
             verbose=verbose,
-            augmentation_config=c.audio_augmentation,
+            augmentation_config=c.audio_augmentation if not is_val else None,
             use_torch_spec=c.model_params.get("use_torch_spec", False),
         )
 

TTS/encoder/configs/base_encoder_config.py

@@ -27,6 +27,7 @@ class BaseEncoderConfig(BaseTrainingConfig):
 
     audio_augmentation: Dict = field(default_factory=lambda: {})
 
+    use_storage: bool = False
     storage: Dict = field(
         default_factory=lambda: {
             "sample_from_storage_p": 0.66,  # the probability with which we'll sample from the DataSet in-memory storage

TTS/encoder/dataset.py

@@ -14,6 +14,7 @@ class EncoderDataset(Dataset):
         meta_data,
         voice_len=1.6,
         num_classes_in_batch=64,
+        use_storage=False,
         storage_size=1,
         sample_from_storage_p=0.5,
         num_utter_per_class=10,
@@ -36,16 +37,21 @@ class EncoderDataset(Dataset):
         self.num_classes_in_batch = num_classes_in_batch
         self.num_utter_per_class = num_utter_per_class
         self.skip_classes = skip_classes
+        self.use_storage = use_storage
         self.ap = ap
         self.verbose = verbose
         self.use_torch_spec = use_torch_spec
         self.__parse_items()
 
         storage_max_size = storage_size * num_classes_in_batch
-        self.storage = Storage(
-            maxsize=storage_max_size, storage_batchs=storage_size, num_classes_in_batch=num_classes_in_batch
-        )
-        self.sample_from_storage_p = float(sample_from_storage_p)
+        if self.use_storage:
+            self.storage = Storage(
+                maxsize=storage_max_size, storage_batchs=storage_size, num_classes_in_batch=num_classes_in_batch
+            )
+            self.sample_from_storage_p = float(sample_from_storage_p)
+        else:
+            self.storage = None
+            self.sample_from_storage_p = None
 
         classes_aux = list(self.classes)
         classes_aux.sort()
@@ -163,7 +169,8 @@ class EncoderDataset(Dataset):
         # don't sample from storage, but from HDD
         wavs_, labels_ = self.__sample_class_utterances(class_name)
         # put the newly loaded item into storage
-        self.storage.append((wavs_, labels_))
+        if self.use_storage:
+            self.storage.append((wavs_, labels_))
         return wavs_, labels_
 
     def collate_fn(self, batch):
@@ -189,7 +196,7 @@ class EncoderDataset(Dataset):
                 class_id = self.classname_to_classid[class_name]
                 classes_id_in_batch.add(class_id)
 
-            if random.random() < self.sample_from_storage_p and self.storage.full():
+            if self.use_storage and random.random() < self.sample_from_storage_p and self.storage.full():
                 # sample from storage (if full)
                 wavs_, labels_ = self.storage.get_random_sample_fast()