diff --git a/autotranscript/autotranscript.py b/autotranscript/autotranscript.py
index 0a29528..2097f2f 100644
--- a/autotranscript/autotranscript.py
+++ b/autotranscript/autotranscript.py
@@ -38,8 +38,7 @@ class AutoTranscribe:
         """
         
         if whisper_model is None:
-            self.transcriber = Transcriber.load_model("medium", local=True)
-            
+            self.transcriber = Transcriber.load_model("medium", local=True)    
         elif isinstance(whisper_model, str):
             self.transcriber = Transcriber.load_model(whisper_model, **whisper_kwargs)
         else:
@@ -170,6 +169,7 @@ def cli():
     from whisper.utils import get_writer
     from whisper.tokenizer import LANGUAGES , TO_LANGUAGE_CODE
     from .transcriber import WHISPER_DEFAULT_PATH
+    from .diarisation import PYANNOTE_DEFAULT_PATH
     def str2bool(string):
         str2val = {"True": True, "False": False}
         if string in str2val:
@@ -190,6 +190,10 @@ def cli():
     parser.add_argument("--wmodel_dir", type=str, default= WHISPER_DEFAULT_PATH,
                         help="the path to save model files; uses ./models/whisper by default")
     
+    parser.add_argument("--dia_model", type=str, default = PYANNOTE_DEFAULT_PATH)
+    
+    parser.add_argument("--allow_download", type= bool, default=True,
+                        help="whether to allow model download if model is not found locally")
     parser.add_argument("--device", 
                         default="cuda" if torch.cuda.is_available() else "cpu",
                         help="device to use for PyTorch inference")
@@ -219,6 +223,7 @@ def cli():
     model_dir: str = args.pop("wmodel_dir")
     output_dir: str = args.pop("output_dir")
     output_format: str = args.pop("output_format")
+    local :str = args.pop("allow_download")
     task = args.pop("task")
     device: str = args.pop("device")
     os.makedirs(output_dir, exist_ok=True)
@@ -227,14 +232,17 @@ def cli():
         torch.set_num_threads(threads)
 
     wkwargs = {"download_root": model_dir,
-               "device": device,
-               "language" : args.pop("language")}
-    
-    model = AutoTranscribe(whisper_model= model_name, whisper_kwargs= wkwargs)
+               "local": local,
+               "device": device}
+    diarisation_kwargs = {"local": local}    
+    model = AutoTranscribe(whisper_model= model_name,
+                           whisper_kwargs= wkwargs,
+                           dia_model= args.pop("dia_model"),
+                           dia_kwargs_kwargs= diarisation_kwargs,)
     
     if task == "transcribe":
         for audio in args.pop("audio"):
-            out  = model.transcribe(audio)
+            out  = model.transcribe(audio, language = args.pop("language"))
             basename = audio.split("/")[-1].split(".")[0]
             spath = f"{output_dir}/{basename}.{output_format}"
             out.save(spath)
@@ -257,7 +265,7 @@ def cli():
              "It is recommendet to use the whisper cli directly",
              RuntimeWarning)
         for audio in args.pop("audio"):
-            out = model.transcriber.transcribe(audio, diarisation=True)
+            out = model.transcriber.transcribe(audio, language = args.pop("language"))
             basename = audio.split("/")[-1].split(".")[0]
             writer(out, audio)