remove ignore generate eval flag

TTS/bin/compute_embeddings.py

@@ -32,8 +32,8 @@ args = parser.parse_args()
 
 c_dataset = load_config(args.config_dataset_path)
 
-train_files, dev_files = load_meta_data(c_dataset.datasets, eval_split=args.eval, ignore_generated_eval=True)
-wav_files = train_files + dev_files
+meta_data_train, meta_data_eval = load_meta_data(c_dataset.datasets, eval_split=args.eval)
+wav_files = meta_data_train + meta_data_eval
 
 speaker_manager = SpeakerManager(encoder_model_path=args.model_path, encoder_config_path=args.config_path, use_cuda=args.use_cuda)
 

TTS/bin/extract_tts_spectrograms.py

@@ -227,7 +227,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     ap = AudioProcessor(**c.audio)
 
     # load data instances
-    meta_data_train, meta_data_eval = load_meta_data(c.datasets, eval_split=args.eval, ignore_generated_eval=True)
+    meta_data_train, meta_data_eval = load_meta_data(c.datasets, eval_split=args.eval)
 
     # use eval and training partitions
     meta_data = meta_data_train + meta_data_eval

TTS/bin/find_unique_chars.py

@@ -24,7 +24,7 @@ def main():
     c = load_config(args.config_path)
 
     # load all datasets
-    train_items, eval_items = load_meta_data(c.datasets, eval_split=True, ignore_generated_eval=True)
+    train_items, eval_items = load_meta_data(c.datasets, eval_split=True)
     items = train_items + eval_items
 
     texts = "".join(item[0] for item in items)

TTS/tts/datasets/__init__.py

@@ -30,7 +30,7 @@ def split_dataset(items):
     return items[:eval_split_size], items[eval_split_size:]
 
 
-def load_meta_data(datasets, eval_split=True, ignore_generated_eval=False):
+def load_meta_data(datasets, eval_split=True):
     meta_data_train_all = []
     meta_data_eval_all = [] if eval_split else None
     for dataset in datasets:
@@ -47,11 +47,9 @@ def load_meta_data(datasets, eval_split=True, ignore_generated_eval=False):
         if eval_split:
             if meta_file_val:
                 meta_data_eval = preprocessor(root_path, meta_file_val)
-                meta_data_eval_all += meta_data_eval
-            elif not ignore_generated_eval:
+            else:
                 meta_data_eval, meta_data_train = split_dataset(meta_data_train)
-                meta_data_eval_all += meta_data_eval
-
+            meta_data_eval_all += meta_data_eval
         meta_data_train_all += meta_data_train
         # load attention masks for duration predictor training
         if dataset.meta_file_attn_mask: