diff --git a/ultravox/data/dataset_config.py b/ultravox/data/dataset_config.py
index 3d6e3422..ea1f0a60 100644
--- a/ultravox/data/dataset_config.py
+++ b/ultravox/data/dataset_config.py
@@ -11,7 +11,7 @@ class DataDictConfig(BaseModel):
     name: Optional[str] = None
     splits: List[str] = dataclasses.field(default_factory=list)
     num_samples: Optional[int] = None
-    total_samples: int
+    total_samples: int = 1
     weight: float = 1.0
     streaming: bool = True
     user_template: str = "<|audio|>"
diff --git a/ultravox/data/datasets.py b/ultravox/data/datasets.py
index b136f839..aa3fb1b9 100644
--- a/ultravox/data/datasets.py
+++ b/ultravox/data/datasets.py
@@ -298,7 +298,7 @@ def __init__(self, args: VoiceDatasetArgs) -> None:
         self._rng = np.random.default_rng(self._args.shuffle_seed)
         self._weight = 1.0  # the default weight for the dataset
 
-    def _init_dataset(self, dataset: data.Dataset, estimated_length: int = 0) -> None:
+    def _init_dataset(self, dataset: data.Dataset, estimated_length: int = 1) -> None:
         self._dataset = dataset
         # Only required when using epochs when training dataset.
         self._estimated_length = estimated_length
@@ -363,12 +363,12 @@ def __iter__(self):
             actual_length += 1
             # If len(dataset) == 0 most likely the dataset is a validation dataset,
             # or the training is using max_steps instead of num_epochs.
-            if actual_length > len(self) and len(self) > 0:
+            if actual_length > len(self) and len(self) > 1:
                 warnings.warn(
                     f"The estimated length {self._estimated_length} has been exceeded for type {type(self._dataset)}. Make sure to update."
                 )
 
-        if actual_length != len(self) and len(self) > 0:
+        if actual_length != len(self) and len(self) > 1:
             warnings.warn(
                 f"Mismatch between estimated length ({self._estimated_length}) and actual length ({actual_length}) for dataset of type {type(self._dataset)}. Make sure to update."
             )
@@ -484,7 +484,7 @@ def _get_sample(self, row: transformers.BatchFeature) -> VoiceSample:
 
 # Making EmptyDataset a SizedIterableDataset to be compatible with using epochs during training.
 class EmptyDataset(SizedIterableDataset):
-    def __init__(self, estimated_length: int = 0) -> None:
+    def __init__(self, estimated_length: int = 1) -> None:
         self._estimated_length = estimated_length
 
     def __iter__(self):
diff --git a/ultravox/training/train.py b/ultravox/training/train.py
index 3884658a..05cea992 100644
--- a/ultravox/training/train.py
+++ b/ultravox/training/train.py
@@ -40,6 +40,7 @@ def fix_hyphens(arg: str):
 
 
 def prepare_dataset(
+    train_args: config_base.TrainConfig,
     dataset_names: List[str],
     data_args: datasets.VoiceDatasetArgs,
     processor: ultravox_processing.UltravoxProcessor,
@@ -48,8 +49,14 @@ def prepare_dataset(
     num_samples: Optional[int] = None,
     include_alt_fields: bool = False,  # whether to generate tensors for text-only input (e.g., used for KD training)
 ) -> datasets.SizedIterableDataset:
-
     data_sets = [datasets.create_dataset(ds, data_args) for ds in dataset_names]
+    # If we're using epochs to train, validate the dataset length is appropriate.
+    if train_args.max_steps == 0:
+        for ds in data_sets:
+            assert (
+                len(ds) > 1
+            ), f"Dataset {ds} has length {len(ds)} which is too short for epoch training"
+
     interleave = datasets.InterleaveDataset(data_sets, stop_strategy=stop_strategy)
     ds_with_proc = data_processing.UltravoxDataproc(
         interleave,
@@ -196,6 +203,7 @@ def train(args: config_base.TrainConfig):
         + [(f"text_{x}", [x]) for x in args.val_sets]
     )
     train_dataset = prepare_dataset(
+        train_args=args,
         dataset_names=args.data_sets,
         train_on_inputs=args.train_on_inputs,
         stop_strategy=args.stop_strategy,
@@ -226,6 +234,7 @@ def train(args: config_base.TrainConfig):
         val_ds_args_text.include_audio = False
         val_datasets = {
             k: prepare_dataset(
+                train_args=args,
                 dataset_names=val_sets[k],
                 train_on_inputs=args.train_on_inputs,
                 stop_strategy=args.stop_strategy,