diff --git a/autotranscript/misc.py b/autotranscript/misc.py
new file mode 100644
index 0000000..91008fd
--- /dev/null
+++ b/autotranscript/misc.py
@@ -0,0 +1,88 @@
+
+from pyannote.audio import Pipeline
+from whisper import Whisper, load_model
+import os
+import glob
+
+def get_whisper_default_path() -> str:
+    """
+    Get default path for whisper models
+
+    Returns
+    -------
+    str
+        path
+    """
+    _path = os.path.dirname(os.path.dirname(__file__))
+    return os.path.join(_path, "models", "whisper")
+
+WHISPER_DEFAULT_PATH = get_whisper_default_path()
+
+def load_whisper_model(model: str ="medium", local : bool = False, download_root: str = WHISPER_DEFAULT_PATH) -> Whisper:
+    """
+    Load modules from whisper
+
+    Parameters
+    ----------
+    whisper : str
+        whisper model
+        available models:
+
+            - 'tiny.en'
+            - 'tiny'
+            - 'base.en'
+            - 'base'
+            - 'small.en'
+            - 'small'
+            - 'medium.en'
+            - 'medium'
+            - 'large-v1'
+            - 'large-v2'
+            - 'large' 
+
+    local : bool
+        If true, load from local cache
+
+    download_root : str
+        Path to download the model
+
+        default: /models/whisper
+    
+    Returns
+    -------
+    Whisper Object
+    """
+    
+    if local:
+        available_models = [os.path.basename(x) for x in glob.glob(os.path.join(WHISPER_DEFAULT_PATH, "*"))]
+        
+        for i, module in enumerate(available_models):
+            available_models[i] = module.split(".")[0]
+        
+        if model not in available_models:
+            raise RuntimeError("Model not found. Consider downloading the model first. By deactivating the local flag, the model will be downloaded automatically.")
+
+    return load_model(model, download_root=WHISPER_DEFAULT_PATH)
+
+def load_pyannote_model(model: str, token: str = "", local : bool = True) -> Pipeline:
+    """
+    Load modules from pyannote
+
+    Parameters
+    ----------
+    model : str
+        pyannote model 
+    token : str
+        HUGGINGFACE_TOKEN
+    local : bool
+        If true, load from local cache
+    
+    Returns
+    -------
+    Pipeline Object
+    """
+
+    if local:
+        return Pipeline.from_pretrained(model)
+    else:
+        return Pipeline.from_pretrained(model, use_auth_token = token)