diff --git a/transcribe_demo.py b/transcribe_demo.py
index a8f6721..6dd8972 100644
--- a/transcribe_demo.py
+++ b/transcribe_demo.py
@@ -5,11 +5,13 @@ import io
 import os
 import speech_recognition as sr
 import whisper
+import torch
 
 from datetime import datetime, timedelta
 from queue import Queue
 from tempfile import NamedTemporaryFile
 from time import sleep
+from sys import platform
 
 
 def main():
@@ -24,9 +26,43 @@ def main():
                         help="How real time the recording is in seconds.", type=float)
     parser.add_argument("--phrase_timeout", default=3,
                         help="How much empty space between recordings before we "
-                             "consider it a new line in the transcription.", type=float)
+                             "consider it a new line in the transcription.", type=float)  
+    if 'linux' in platform:
+        parser.add_argument("--default_microphone", default='pulse',
+                            help="Default microphone name for SpeechRecognition. "
+                                 "Run this with 'list' to view available Microphones.", type=str)
     args = parser.parse_args()
-
+    
+    # The last time a recording was retreived from the queue.
+    phrase_time = None
+    # Current raw audio bytes.
+    last_sample = bytes()
+    # Thread safe Queue for passing data from the threaded recording callback.
+    data_queue = Queue()
+    # We use SpeechRecognizer to record our audio because it has a nice feauture where it can detect when speech ends.
+    recorder = sr.Recognizer()
+    recorder.energy_threshold = args.energy_threshold
+    # Definitely do this, dynamic energy compensation lowers the energy threshold dramtically to a point where the SpeechRecognizer never stops recording.
+    recorder.dynamic_energy_threshold = False
+    
+    # Important for linux users. 
+    # Prevents permanent application hang and crash by using the wrong Microphone
+    if 'linux' in platform:
+        mic_name = args.default_microphone
+        if not mic_name or mic_name == 'list':
+            print("Available microphone devices are: ")
+            for index, name in enumerate(sr.Microphone.list_microphone_names()):
+                print(f"Microphone with name \"{name}\" found")   
+            return
+        else:
+            for index, name in enumerate(sr.Microphone.list_microphone_names()):
+                if mic_name in name:
+                    source = sr.Microphone(sample_rate=16000, device_index=index)
+                    break
+    else:
+        source = sr.Microphone(sample_rate=16000)
+        
+    # Load / Download model
     model = args.model
     if args.model != "large" and not args.non_english:
         model = model + ".en"
@@ -37,21 +73,7 @@ def main():
 
     temp_file = NamedTemporaryFile().name
     transcription = ['']
-
-    # The last time a recording was retreived from the queue.
-    phrase_time = None
-    # Current raw audio bytes.
-    last_sample = bytes()
-    # Thread safe Queue for passing data from the threaded recording callback.
-    data_queue = Queue()
-
-    # We use SpeechRecognizer to record our audio because it has a nice feauture where it can detect when speech ends.
-    recorder = sr.Recognizer()
-    recorder.energy_threshold = args.energy_threshold
-    # Definitely do this, dynamic energy compensation lowers the energy threshold dramtically to a point where the SpeechRecognizer never stops recording.
-    recorder.dynamic_energy_threshold = False
-
-    source = sr.Microphone(sample_rate=16000)
+    
     with source:
         recorder.adjust_for_ambient_noise(source)
 
@@ -99,7 +121,7 @@ def main():
                     f.write(wav_data.read())
 
                 # Read the transcription.
-                result = audio_model.transcribe(temp_file)
+                result = audio_model.transcribe(temp_file, fp16=torch.cuda.is_available())
                 text = result['text'].strip()
 
                 # If we detected a pause between recordings, add a new item to our transcripion.