diff --git a/config.json b/config.json
index a7ed04a3..a113836f 100644
--- a/config.json
+++ b/config.json
@@ -1,6 +1,6 @@
 {
     "model": "Tacotron2",          // one of the model in models/  
-    "run_name": "ljspeech-stf_params",
+    "run_name": "ljspeech-stft_params",
     "run_description": "tacotron2 cosntant stf parameters",
 
     // AUDIO PARAMETERS
@@ -36,12 +36,11 @@
     "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
 
     // TRAINING
-    "batch_size": 2,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+    "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
     "eval_batch_size":16,   
     "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.  
-    "gradual_training": [[0, 7, 64], [2000, 5, 64], [35000, 3, 32], [70000, 2, 32], [140000, 1, 32]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
+    "gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 32], [290000, 1, 32]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
     "loss_masking": true,         // enable / disable loss masking against the sequence padding.
-    "grad_accum": 2,        // if N > 1, enable gradient accumulation for N iterations. It is useful for low memory GPUs. 
 
     // VALIDATION
     "run_eval": true,
diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index a8de5bbb..d728eeb9 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -391,7 +391,9 @@ class KeepAverage():
             self.update_value(key, value)
 
 
-def _check_argument(name, c, enum_list=None, max_val=None, min_val=None, restricted=False, val_type=None):
+def _check_argument(name, c, enum_list=None, max_val=None, min_val=None, restricted=False, val_type=None, alternative=None):
+    if alternative in c.keys() and c[alternative] is not None:
+        return
     if restricted:
         assert name in c.keys(), f' [!] {name} not defined in config.json'
     if name in c.keys():
@@ -417,8 +419,8 @@ def check_config(c):
     _check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
     _check_argument('num_freq', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
     _check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
-    _check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000)
-    _check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000)
+    _check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length')
+    _check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length')
     _check_argument('preemphasis', c['audio'], restricted=True, val_type=float, min_val=0, max_val=1)
     _check_argument('min_level_db', c['audio'], restricted=True, val_type=int, min_val=-1000, max_val=10)
     _check_argument('ref_level_db', c['audio'], restricted=True, val_type=int, min_val=0, max_val=1000)