diff --git a/audio-summarize.py b/audio-summarize.py
index f88d272..9c49ab6 100755
--- a/audio-summarize.py
+++ b/audio-summarize.py
@@ -2,9 +2,12 @@
 
 # Copyright (c) 2024 Julian Müller (ChaoticByte)
 
+# Disable FutureWarnings
 import warnings
 warnings.simplefilter(action='ignore', category=FutureWarning)
 
+# Imports
+
 from argparse import ArgumentParser
 from pathlib import Path
 from subprocess import check_call, DEVNULL
@@ -15,15 +18,16 @@ from semantic_text_splitter import TextSplitter
 from tokenizers import Tokenizer
 from transformers import pipeline
 
+# Some constant variables
 
 NLP_MODEL = "facebook/bart-large-cnn"
 root_dir = Path(__file__).parent
 whisper_cpp_binary = (root_dir / "vendor" / "whisper.cpp" / "main").__str__()
 
-
-# tasks
+# Steps
 
 def convert_audio(media_file: str, output_file: str):
+    '''Convert media to mono 16kHz pcm_s16le wav using ffmpeg'''
     check_call([
         "ffmpeg",
         "-hide_banner",
@@ -35,6 +39,7 @@ def convert_audio(media_file: str, output_file: str):
         output_file])
 
 def transcribe(model_file: str, audio_file: str, output_file: str):
+    '''Transcribe audio file using whisper.cpp'''
     check_call([
         whisper_cpp_binary,
         "-m", model_file,
@@ -53,6 +58,7 @@ def cleanup_text(t: str) -> str:
     return t
 
 def split_text(t: str, max_tokens: int) -> List[str]:
+    '''Split text into semantic segments'''
     tokenizer = Tokenizer.from_pretrained(NLP_MODEL)
     splitter = TextSplitter.from_huggingface_tokenizer(
         tokenizer, (int(max_tokens*0.8), max_tokens))
@@ -60,6 +66,7 @@ def split_text(t: str, max_tokens: int) -> List[str]:
     return chunks
 
 def summarize(chunks: List[str], summary_min: int, summary_max: int) -> str:
+    '''Summarize all segments (chunks) using a language model'''
     chunks_summarized = []
     summ = pipeline("summarization", model=NLP_MODEL)
     for c in chunks:
@@ -67,9 +74,10 @@ def summarize(chunks: List[str], summary_min: int, summary_max: int) -> str:
             summ(c, max_length=summary_max, min_length=summary_min, do_sample=False)[0]['summary_text'].strip())
     return "\n".join(chunks_summarized)
 
-#
+# Main
 
 if __name__ == "__main__":
+    # parse commandline arguments
     argp = ArgumentParser()
     argp.add_argument("--summin", metavar="n", type=int, default=10, help="The minimum lenght of a segment summary [10, min: 5]")
     argp.add_argument("--summax", metavar="n", type=int, default=90, help="The maximum lenght of a segment summary [90, min: 5]")
@@ -78,6 +86,7 @@ if __name__ == "__main__":
     argp.add_argument("-i", required=True, metavar="filepath", type=Path, help="The path to the media file")
     argp.add_argument("-o", required=True, metavar="filepath", type=Path, help="Where to save the output text to")
     args = argp.parse_args()
+    # Clamp values
     args.summin = max(5, args.summin)
     args.summax = max(5, args.summax)
     args.segmax = max(5, min(args.segmax, 500))
@@ -100,5 +109,5 @@ if __name__ == "__main__":
     summary = summarize(chunks, args.summin, args.summax)
     print(f"\n{summary}\n")
     print(f"* Saving summary to {args.o.__str__()}")
-    with args.o.open("w+") as f:
+    with args.o.open("w+") as f: # overwrites
         f.write(summary)