cmd interface: train command should not fail if data folder contains more than train files
Currently, the command line interface fails to train the `nlu` or `core` model, if the data folder contains more than the expected training file.

Example:
Data folder contains `stories.md` and `nlu.md` and you execute the command `rasa nlu train`. The execution will fail with the following error:
```
ValueError: Unknown data format for file data/stories.md
```
If you run `rasa core train`, the execution fails with
```
TypeError: expected str, bytes or os.PathLike object, not NoneType
```

Current solution:
The data folder should contain only the training file you need to train your model. E.g. if you want to run `rasa train nlu`, the data folder should just contain `nlu.md`.

What needs to be done:
Ignore files in the data folder if they are not in the expected format, so that you don't have to move files before training any model.
diff --git a/rasa/train.py b/rasa/train.py
index 0a735277074..5478b3a10f1 100644
--- a/rasa/train.py
+++ b/rasa/train.py
@@ -149,7 +149,7 @@ async def train_core_async(
     # normal (not compare) training
     core_model = await rasa.core.train(
         domain_file=domain,
-        stories_file=stories,
+        stories_file=data.get_core_directory(stories),
         output_path=os.path.join(_train_path, "core"),
         policy_config=config,
         kwargs=kwargs,
@@ -189,7 +189,11 @@ def train_nlu(
 
     _train_path = train_path or tempfile.mkdtemp()
     _, nlu_model, _ = rasa.nlu.train(
-        config, nlu_data, _train_path, project="", fixed_model_name="nlu"
+        config,
+        data.get_nlu_directory(nlu_data),
+        _train_path,
+        project="",
+        fixed_model_name="nlu",
     )
 
     if not train_path:
['rasa/train.py']