diff --git a/transcribe_demo.py b/transcribe_demo.py
index 6dd8972..31b2e5b 100644
--- a/transcribe_demo.py
+++ b/transcribe_demo.py
@@ -4,8 +4,6 @@
 import io
 import os
 import speech_recognition as sr
-import whisper
-import torch
 
 from datetime import datetime, timedelta
 from queue import Queue
@@ -20,6 +18,8 @@ def main():
                         choices=["tiny", "base", "small", "medium", "large"])
     parser.add_argument("--non_english", action='store_true',
                         help="Don't use the english model.")
+    parser.add_argument("--use_openai_api", action='store_true',
+                        help="Using OPENAI_API_KEY enviromental variable")
     parser.add_argument("--energy_threshold", default=1000,
                         help="Energy level for mic to detect.", type=int)
     parser.add_argument("--record_timeout", default=2,
@@ -62,16 +62,24 @@ def main():
     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"
-    audio_model = whisper.load_model(model)
+    if args.use_openai_api:
+        import openai
+        # Load your API key from an environment variable or secret management service
+        openai.api_key = os.getenv('OPENAI_API_KEY')
+    else:
+
+        import whisper
+        import torch
+        # Load / Download model
+        model = args.model
+        if args.model != "large" and not args.non_english:
+            model = model + ".en"
+        audio_model = whisper.load_model(model)
 
     record_timeout = args.record_timeout
     phrase_timeout = args.phrase_timeout
 
-    temp_file = NamedTemporaryFile().name
+    temp_file = NamedTemporaryFile(suffix='.wav').name
     transcription = ['']
     
     with source:
@@ -120,8 +128,12 @@ def record_callback(_, audio:sr.AudioData) -> None:
                 with open(temp_file, 'w+b') as f:
                     f.write(wav_data.read())
 
-                # Read the transcription.
-                result = audio_model.transcribe(temp_file, fp16=torch.cuda.is_available())
+                if args.use_openai_api:
+                    with open(temp_file, 'rb') as f:
+                        result = openai.Audio.transcribe("whisper-1", f)
+                else:
+                    # Read the transcription.
+                    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.
@@ -132,7 +144,7 @@ def record_callback(_, audio:sr.AudioData) -> None:
                     transcription[-1] = text
 
                 # Clear the console to reprint the updated transcription.
-                os.system('cls' if os.name=='nt' else 'clear')
+                os.system('cls' if os.name == 'nt' else 'clear')
                 for line in transcription:
                     print(line)
                 # Flush stdout.
@@ -149,4 +161,4 @@ def record_callback(_, audio:sr.AudioData) -> None:
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()