how set_transform affects batch size?
### Describe the bug

I am trying to fine-tune w2v-bert for ASR task. Since my dataset is so big, I preferred to use the on-the-fly method with set_transform. So i change the preprocessing function to this:
```
def prepare_dataset(batch):
    input_features = processor(batch["audio"], sampling_rate=16000).input_features[0]
    input_length = len(input_features)
    labels = processor.tokenizer(batch["text"], padding=False).input_ids

    batch = {
        "input_features": [input_features],
        "input_length": [input_length],
        "labels": [labels]
    }
    
    return batch


train_ds.set_transform(prepare_dataset)
val_ds.set_transform(prepare_dataset)
```

After this, I also had to change the DataCollatorCTCWithPadding class like this:
```
@dataclass
class DataCollatorCTCWithPadding:
    processor: Wav2Vec2BertProcessor
    padding: Union[bool, str] = True

    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        # Separate input_features and labels
        input_features = [{"input_features": feature["input_features"][0]} for feature in features]
        labels = [feature["labels"][0] for feature in features]

        # Pad input features
        batch = self.processor.pad(
            input_features,
            padding=self.padding,
            return_tensors="pt",
        )

        # Pad and process labels
        label_features = self.processor.tokenizer.pad(
            {"input_ids": labels},
            padding=self.padding,
            return_tensors="pt",
        )

        labels = label_features["input_ids"]
        attention_mask = label_features["attention_mask"]

        # Replace padding with -100 to ignore these tokens during loss calculation
        labels = labels.masked_fill(attention_mask.ne(1), -100)

        batch["labels"] = labels

        return batch
```
        
But now a strange thing is happening, no matter how much I increase the batch size, the amount of V-RAM GPU usage does not change, while the number of total steps in the progress-bar (logging) changes. Is this normal or have I made a mistake?

### Steps to reproduce the bug

i can share my code if needed

### Expected behavior

Equal to the batch size value, the set_transform function is applied to the dataset and given to the model as a batch.

### Environment info

all updated versions