Update app.py

app.py

@@ -125,14 +125,14 @@ def handle_feedback(feedback):
             conn.commit()
         return "Thank you for your feedback!", None
 
-def segment_background_audio(audio_path, output_path="background_segments.wav"):
+def segment_background_audio(audio_path, segment_audio_path="background_segments.wav"):
     
     """
     Detects and extracts non-speech (background) segments from audio using pyannote VAD.
 
     Parameters:
     - audio_path (str): Path to input audio (.wav).
-    - output_path (str): Path to save the output non-speech audio.
+    - segment_audio_path (str): Path to save the output non-speech audio.
     - hf_token (str): Hugging Face auth token for pyannote.
 
     Returns:
@@ -171,8 +171,8 @@ def segment_background_audio(audio_path, output_path="background_segments.wav"):
         non_speech_audio += segment
 
     # Step 6: Export the non-speech audio
-    non_speech_audio.export(output_path, format="wav")
-    print(f"🎵 Non-speech audio saved to: {output_path}")
+    non_speech_audio.export(segment_audio_path, format="wav")
+    print(f"🎵 Non-speech audio saved to: {segment_audio_path}")
 
     return background_segments
 
@@ -433,7 +433,7 @@ def process_entry(entry, i, tts_model, video_width, video_height, process_mode,
             else:
                 generate_voiceover_OpenAI(entry['translated'], target_language, desired_speed, segment_audio_path)
 
-            if not output_path or not os.path.exists(segment_audio_path):
+            if not segment_audio_path or not os.path.exists(segment_audio_path):
                 raise FileNotFoundError(f"Voiceover file not generated at: {segment_audio_path}")
 
             audio_clip = AudioFileClip(segment_audio_path)