Update app.py

app.py

@@ -128,32 +128,33 @@ def handle_feedback(feedback):
 
 def segment_background_audio(audio_path, output_path="background_segments.wav"):
     # Step 2: Initialize pyannote voice activity detection pipeline (you need Hugging Face token)
-    pipeline = Pipeline.from_pretrained(
-        "pyannote/voice-activity-detection",
-        use_auth_token=hf_api_key
-    )
-    # Step 3: Run VAD to get speech segments
-    vad_result = pipeline(audio_path)
-    print(f"Detected speech segments: {vad_result}")
-
-    # Step 4: Load full audio and subtract speech segments
-    full_audio = AudioSegment.from_wav(audio_path)
-    background_audio = AudioSegment.silent(duration=len(full_audio))
-
-    for segment in vad_result.itersegments():
-        start_ms = int(segment.start * 1000)
-        end_ms = int(segment.end * 1000)
-        # Remove speech by muting that portion
-        background_audio = background_audio.overlay(AudioSegment.silent(duration=end_ms - start_ms), position=start_ms)
-
-    # Step 5: Subtract background_audio from full_audio
-    result_audio = full_audio.overlay(background_audio)
-
-    # Step 6: Export non-speech segments
-    result_audio.export(output_path, format="wav")
-    print(f"Saved non-speech (background) audio to: {output_path}")
-
     return True
+    
+    # pipeline = Pipeline.from_pretrained(
+    #     "pyannote/voice-activity-detection",
+    #     use_auth_token=hf_api_key
+    # )
+    # # Step 3: Run VAD to get speech segments
+    # vad_result = pipeline(audio_path)
+    # print(f"Detected speech segments: {vad_result}")
+
+    # # Step 4: Load full audio and subtract speech segments
+    # full_audio = AudioSegment.from_wav(audio_path)
+    # background_audio = AudioSegment.silent(duration=len(full_audio))
+
+    # for segment in vad_result.itersegments():
+    #     start_ms = int(segment.start * 1000)
+    #     end_ms = int(segment.end * 1000)
+    #     # Remove speech by muting that portion
+    #     background_audio = background_audio.overlay(AudioSegment.silent(duration=end_ms - start_ms), position=start_ms)
+
+    # # Step 5: Subtract background_audio from full_audio
+    # result_audio = full_audio.overlay(background_audio)
+
+    # # Step 6: Export non-speech segments
+    # result_audio.export(output_path, format="wav")
+    # print(f"Saved non-speech (background) audio to: {output_path}")
+
 
 def transcribe_video_with_speakers(video_path):
     # Extract audio from video