using amp in training

TTS/bin/train_tts.py

@@ -506,7 +506,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     else:
         optimizer_st = None
 
-    if c.apex_amp_level:
+    if c.apex_amp_level == "O1":
         # pylint: disable=import-outside-toplevel
         from apex import amp
         model.cuda()
@@ -578,7 +578,7 @@ def main(args):  # pylint: disable=redefined-outer-name
             print("\n > Number of output frames:", model.decoder.r)
         train_avg_loss_dict, global_step = train(model, criterion, optimizer,
                                                  optimizer_st, scheduler, ap,
-                                                 global_step, epoch)
+                                                 global_step, epoch, amp)
         eval_avg_loss_dict = evaluate(model, criterion, ap, global_step, epoch)
         c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
         target_loss = train_avg_loss_dict['avg_postnet_loss']
@@ -637,7 +637,7 @@ if __name__ == '__main__':
     check_config(c)
     _ = os.path.dirname(os.path.realpath(__file__))
 
-    if c.apex_amp_level is 'O1':
+    if c.apex_amp_level == 'O1':
         print("   >  apex AMP level: ", c.apex_amp_level)
 
     OUT_PATH = args.continue_path

TTS/tts/configs/config.json

@@ -85,8 +85,8 @@
 
     // TACOTRON PRENET
     "memory_size": -1,             // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
-    "prenet_type": "bn",     // "original" or "bn".
-    "prenet_dropout": false,        // enable/disable dropout at prenet.
+    "prenet_type": "bn",           // "original" or "bn".
+    "prenet_dropout": false,       // enable/disable dropout at prenet.
 
     // TACOTRON ATTENTION
     "attention_type": "original",  // 'original' or 'graves'

TTS/tts/utils/generic_utils.py

@@ -178,6 +178,7 @@ def check_config(c):
     check_argument('r', c, restricted=True, val_type=int, min_val=1)
     check_argument('gradual_training', c, restricted=False, val_type=list)
     check_argument('loss_masking', c, restricted=True, val_type=bool)
+    check_argument('apex_amp_level', c, restricted=False, val_type=str)
     # check_argument('grad_accum', c, restricted=True, val_type=int, min_val=1, max_val=100)
 
     # validation parameters