diff --git a/train_ms.py b/train_ms.py
index 34115dc..81048e3 100644
--- a/train_ms.py
+++ b/train_ms.py
@@ -38,7 +38,6 @@ from text.symbols import symbols
 
 torch.backends.cudnn.benchmark = True
 torch.backends.cuda.matmul.allow_tf32 = True
-# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
 torch.backends.cudnn.allow_tf32 = True
 torch.set_float32_matmul_precision('medium')
 global_step = 0
@@ -62,9 +61,7 @@ def run(rank, n_gpus, hps):
         logger = utils.get_logger(hps.model_dir)
         logger.info(hps)
         utils.check_git_hash(hps.model_dir)
-        #wandb.init(project="Bert-VITS2", config=hps, sync_tensorboard=True)
         writer = SummaryWriter(log_dir=hps.model_dir)
-        #writer = wandb.summary.create_file_writer()
         writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
 
     dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
@@ -307,15 +304,9 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                                "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
                 scalar_dict.update(
                     {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
-                #if net_dur_disc is not None:
-                    #scalar_dict.update({"loss/dur_disc/total": loss_dur_disc_all, "grad_norm_dur_disc": grad_norm_dur_disc})
                 scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
                 scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
                 scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-                #if net_dur_disc is not None:
-                    #scalar_dict.update({"loss/dur_disc_r" : f"{losses_dur_disc_r}"})
-                    #scalar_dict.update({"loss/dur_disc_g" : f"{losses_dur_disc_g}"})
-                    #scalar_dict.update({"loss/dur_gen" : f"{loss_dur_gen}"})
           
                 image_dict = {
                     "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),