diff --git a/vall_e/engines/base.py b/vall_e/engines/base.py
index 7545b3c..3a5c7d6 100755
--- a/vall_e/engines/base.py
+++ b/vall_e/engines/base.py
@@ -222,6 +222,7 @@ class Engines(dict[str, Engine]):
 		self._global_step = 0
 		self._micro_step = 0
 		self._batch_size = 0
+		self._global_samples = 0
 
 	@property
 	def global_step(self):
@@ -235,6 +236,10 @@ class Engines(dict[str, Engine]):
 	def batch_size(self):
 		return self._batch_size
 
+	@property
+	def global_samples(self):
+		return self._global_samples
+
 	def gather_attribute(self, *args, **kwargs):
 		ret = {}
 		for engine in self.values():
@@ -324,6 +329,7 @@ class Engines(dict[str, Engine]):
 			self._global_step = max(self._global_step, engine.global_step)
 			self._micro_step = max(self._micro_step, engine.micro_step)
 			self._batch_size = max(self._batch_size, engine.batch_size)
+			self._global_samples = max(self._global_samples, engine.global_samples)
 
 	def eval(self):
 		for engine in self.values():
diff --git a/vall_e/utils/trainer.py b/vall_e/utils/trainer.py
index 15d3828..cfb4c7c 100755
--- a/vall_e/utils/trainer.py
+++ b/vall_e/utils/trainer.py
@@ -251,9 +251,8 @@ def train(
 		#batch = to_device(batch, torch.cuda.current_device())
 		stats = engines.step(batch=batch, feeder=train_feeder)
 
-		iteration = stats['global_step'] # * cfg.hyperparameters.gradient_accumulation_steps
-		stats['it'] = iteration
-		stats['epoch'] = iteration * cfg.hyperparameters.gradient_accumulation_steps / len(train_dl)
+		stats['it'] = stats['global_step']
+		stats['epoch'] = engines.global_samples / len(train_dl.dataset.paths)
 
 		stats['batch'] = {
 			'size': len(batch['text']),