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GPT_SoVITS/s2_train.py

@@ -251,8 +251,8 @@ def run(rank, n_gpus, hps):
 
     scaler = GradScaler(enabled=hps.train.fp16_run)
 
+    print("start training from epoch %s" % epoch_str)
     for epoch in range(epoch_str, hps.train.epochs + 1):
-        print("start training from epoch %s"%epoch)
         if rank == 0:
             train_and_evaluate(
                 rank,
@@ -282,7 +282,7 @@ def run(rank, n_gpus, hps):
             )
         scheduler_g.step()
         scheduler_d.step()
-        print("training done")
+    print("training done")
 
 
 def train_and_evaluate(

GPT_SoVITS/s2_train_v3.py

@@ -225,8 +225,8 @@ def run(rank, n_gpus, hps):
     scaler = GradScaler(enabled=hps.train.fp16_run)
 
     net_d=optim_d=scheduler_d=None
+    print("start training from epoch %s" % epoch_str)
     for epoch in range(epoch_str, hps.train.epochs + 1):
-        print("start training from epoch %s"%epoch)
         if rank == 0:
             train_and_evaluate(
                 rank,
@@ -256,7 +256,7 @@ def run(rank, n_gpus, hps):
             )
         scheduler_g.step()
         # scheduler_d.step()
-        print("training done")
+    print("training done")
 
 
 def train_and_evaluate(

GPT_SoVITS/s2_train_v3_lora.py

@@ -198,8 +198,8 @@ def run(rank, n_gpus, hps):
     scaler = GradScaler(enabled=hps.train.fp16_run)
 
     net_d=optim_d=scheduler_d=None
+    print("start training from epoch %s"%epoch_str)
     for epoch in range(epoch_str, hps.train.epochs + 1):
-        print("start training from epoch %s"%epoch)
         if rank == 0:
             train_and_evaluate(
                 rank,
@@ -228,7 +228,7 @@ def run(rank, n_gpus, hps):
                 None,
             )
         scheduler_g.step()
-        print("training done")
+    print("training done")
 
 def train_and_evaluate(
     rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers