f

main.py

@@ -80,11 +80,21 @@ def extract_features(audio):
 
 async def process_audio_data(audio_data):
     try:
-        # Convert audio data from webm/ogg to wav format using pydub
+        logger.info(f"Audio data type: {type(audio_data)}")
+        # Attempt to convert audio data from webm/ogg to wav format using pydub
         audio_segment = AudioSegment.from_file(io.BytesIO(audio_data), format="webm")
+    except Exception as e:
+        logger.error(f"Failed to convert audio data using pydub: {e}")
+        return
+
+    try:
+        # Export the audio segment to wav format
         wav_io = io.BytesIO()
         audio_segment.export(wav_io, format="wav")
+        logger.info(f"Audio data type: {type(audio_segment)}")
         wav_io.seek(0)
+
+        # Read the audio data
         audio, sr = sf.read(wav_io, dtype='float32')
     except Exception as e:
         logger.error(f"Failed to read audio data: {e}")
@@ -92,14 +102,20 @@ async def process_audio_data(audio_data):
 
     if audio.ndim > 1:  # If audio has more than one channel, average them
         audio = np.mean(audio, axis=1)
-    logger.info("Here!!!")
+    
+    logger.info(f"The len of audio: {len(audio)}")
+    logger.info("Extracting features")
     features = extract_features(audio)
     features = features.reshape(1, -1)
-    prediction = model.predict(features)
-    is_fake = prediction[0]
 
-    result = 'fake' if is_fake else 'real'
-    
+    try:
+        prediction = model.predict(features)
+        is_fake = prediction[0]
+        result = 'fake' if is_fake else 'real'
+    except Exception as e:
+        logger.error(f"Model prediction failed: {e}")
+        return
+
     await manager.send_message(result)
 
 @app.post("/start_detection")