diff --git a/vall_e/config.py b/vall_e/config.py
index 7139e73..bfa2546 100755
--- a/vall_e/config.py
+++ b/vall_e/config.py
@@ -129,6 +129,8 @@ class Dataset:
 	max_prompts: int = 3
 	prompt_duration: float = 3.0
 
+	sample_type: str = "path" # path | speaker
+
 @dataclass()
 class Model:
 	name: str = ""
diff --git a/vall_e/data.py b/vall_e/data.py
index 8e476c1..f3f371b 100755
--- a/vall_e/data.py
+++ b/vall_e/data.py
@@ -119,6 +119,7 @@ class Dataset(_Dataset):
 		max_duration=cfg.dataset.duration_range[1],
 		training=False,
 		extra_paths_by_spkr_name: dict[str, list] = {},
+		sample_type=cfg.dataset.sample_type # path | speaker
 	):
 		super().__init__()
 		self._head = None
@@ -126,6 +127,7 @@ class Dataset(_Dataset):
 		self.max_phones = max_phones
 		self.min_duration = min_duration
 		self.max_duration = max_duration
+		self.sample_type = sample_type
 
 		if cfg.dataset.validate:
 			self.paths = [
@@ -167,7 +169,7 @@ class Dataset(_Dataset):
 				else:
 					self.durations[spkr_id] += duration
 
-		if training and not cfg.distributed:
+		if training and not cfg.distributed and self.sample_type == "path":
 			self.sampler = Sampler(self.paths, [cfg.get_spkr])
 		else:
 			self.sampler = None
@@ -200,10 +202,14 @@ class Dataset(_Dataset):
 		choices = set(self.paths_by_spkr_name[spkr_name]) - {ignore}
 		choices = [*choices]
 
+		# no other utterances, it'd make more sense to prune speakers with only one utterance in the validatoin step
 		if len(choices) == 0:
+			choices = [*set(self.paths_by_spkr_name[spkr_name])]
+			"""
 			raise ValueError(
 				f"Failed to find another different utterance for {spkr_name}."
 			)
+			"""
 
 		# shuffle it up a bit
 		offset = random.randint(-16, 16)
@@ -248,13 +254,17 @@ class Dataset(_Dataset):
 		return prom
 
 	def __getitem__(self, index):
-		if self.training and self.sampler is not None:
-			path = self.sampler.sample()
+		if hasattr(self, "sample_type") and self.sample_type == "speaker":
+			spkr_name = self.spkrs[index]
+			spkr_id = self.spkr_symmap[spkr_name]
+			path = random.choice([*set(self.paths_by_spkr_name[spkr_name])])
 		else:
-			path = self.paths[index]
-
-		spkr_name = cfg.get_spkr(path)
-		spkr_id = self.spkr_symmap[spkr_name]
+			if self.training and self.sampler is not None:
+				path = self.sampler.sample()
+			else:
+				path = self.paths[index]
+			spkr_name = cfg.get_spkr(path)
+			spkr_id = self.spkr_symmap[spkr_name]
 
 		if cfg.dataset.use_hdf5:
 			key = _get_hdf5_path(path)
@@ -287,6 +297,8 @@ class Dataset(_Dataset):
 		self.paths = [*_interleaved_reorder(self.paths, fn)]
 
 	def __len__(self):
+		if hasattr(self, "sample_type") and self.sample_type == "speaker":
+			return min(len(self.spkrs), self._head or len(self.spkrs))
 		return min(len(self.paths), self._head or len(self.paths))
 
 	def pin_memory(self):
@@ -444,6 +456,7 @@ def create_datasets():
 
 def create_train_val_dataloader():
 	train_dataset, val_dataset = create_datasets()
+	#train_dataset.sample_type = "speaker"
 
 	subtrain_dataset = copy.deepcopy(train_dataset)
 	subtrain_dataset.head_(cfg.evaluation.size)