Update train_nsf_sim_cache_sid_load_pretrain.py

train_nsf_sim_cache_sid_load_pretrain.py

@@ -289,8 +289,8 @@ def run(rank, n_gpus, hps):
             #
             Lowest_lg = f"{min(Loss_Gen_Per_Epoch):.5f}, epoch: {right_index(Loss_Gen_Per_Epoch,min(Loss_Gen_Per_Epoch))+1}"
             Lowest_ld = f"{min(Loss_Disc_Per_Epoch):.5f}, epoch: {right_index(Loss_Disc_Per_Epoch,min(Loss_Disc_Per_Epoch))+1}"
-            print(f"{hps.name}_e{epoch}_s{global_step} | Loss gen total: {Loss_Gen_Per_Epoch[-1]:.8f} | Lowest loss G: {Lowest_lg}\n Loss disc: {Loss_Disc_Per_Epoch[-1]:.8f} | Lowest loss D: {Lowest_ld}")
-            print(f"Specific Value: loss gen={loss_gen:.3f}, loss fm={loss_fm:.3f}, loss mel={loss_mel:.3f}, loss kl={loss_kl:.3f}")
+            print(f"{hps.name}_e{epoch}_s{global_step} | Loss gen total: {Loss_Gen_Per_Epoch[-1]:.8f} | Lowest loss G: {Lowest_lg}\n- Loss disc: {Loss_Disc_Per_Epoch[-1]:.8f} | Lowest loss D: {Lowest_ld}")
+            print(f"> Specific Value: loss gen={loss_gen:.3f}, loss fm={loss_fm:.3f}, loss mel={loss_mel:.3f}, loss kl={loss_kl:.3f}")
             #
             if len(Loss_Gen_Per_Epoch) > Min_for_Single_epoch and epoch % hps.save_every_epoch != 0:
                 if min(Loss_Gen_Per_Epoch[Min_for_Single_epoch::1]) == Loss_Gen_Per_Epoch[-1]:
@@ -322,7 +322,7 @@ def run(rank, n_gpus, hps):
             time_left = elapsed_time_avg*(hps.total_epoch-epoch)
             hour, minute, second, millisec = Calculate_format_elapsed_time(elapsed_time)
             hour_left, minute_left, second_left, millisec_left = Calculate_format_elapsed_time(time_left)
-            print(f"Time Elapsed: {hour}h:{formating_time(minute)}m:{formating_time(second)}s:{millisec}ms  ||  Time left: {hour_left}h:{formating_time(minute_left)}m:{formating_time(second_left)}s:{millisec_left}ms\n")
+            print(f"-Time Elapsed: {hour}h:{formating_time(minute)}m:{formating_time(second)}s:{millisec}ms  ||  Time left: {hour_left}h:{formating_time(minute_left)}m:{formating_time(second_left)}s:{millisec_left}ms-\n")
             #
             if ((len(Loss_Gen_Per_Epoch) - right_index(Loss_Gen_Per_Epoch,min(Loss_Gen_Per_Epoch)) + 1) > overtrain and overtrain != -1):
                 logger.info("Over Train threshold reached. Training is done.")