diff --git a/interviewkit/transcript.py b/interviewkit/transcript.py
index 6ee84b8..616b1cb 100644
--- a/interviewkit/transcript.py
+++ b/interviewkit/transcript.py
@@ -1,7 +1,43 @@
-from pydantic import BaseModel
+try: 
+    import whisper
+except ImportError:
+    print("Please install Whisper: pip install git+https://github.com/openai/whisper.git")
+    exit(1)
 
+from whisper.utils import get_writer
+from pydantic import BaseModel
 
 class Transcript(BaseModel):
     """The Transcript entity represents the transcript of an interview."""
 
     content: str
+
+def main() -> None:
+
+    model = whisper.load_model("base")
+
+    # Change this file path to be the location of the audio file, or connect it to the slicer.py file. The audio used can be a .mp3 or a .wav file
+    AUDIO_LOCATION = "/workspaces/HistoryAIToolkit/interviewkit/"
+    AUDIO_FILENAME = "sampled-2-Martine+Barrat_FINAL.mp3"
+
+    result = model.transcribe((AUDIO_LOCATION + AUDIO_FILENAME), fp16=False)
+
+    transcript_output_location = "./"
+
+    # Setting some initial options values for the .txt output file
+    txt_file_options = {
+        'max_line_width': 50, # the maximum number of characters in a line before breaking the line
+        'max_line_count': 1, # the maximum number of lines in a segment
+        'highlight_words': False # underline each word as it is spoken in srt and vtt
+    }
+
+    # Save as a .txt file without line breaks, name of the file could be changed with the slicer.py name of audio file
+    # with open((AUDIO_FILENAME + ".txt"), "w", encoding="utf-8") as txt:
+    #    txt.write(result["text"])   
+
+    # Save as a .txt file with hard breaks, added for readability to user
+    txt_writer = get_writer("txt", transcript_output_location)
+    txt_writer(result, AUDIO_FILENAME, txt_file_options)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file