Update handler.py

handler.py

@@ -33,34 +33,36 @@ class EndpointHandler():
         return np.expand_dims(features, axis=0)
     
     def preprocess_audio_data(self, base64_string, duration=2.5, desired_sr=22050*2, offset=0.5):
-        # audio_base64 = base64_string.replace("data:audio/webm;codecs=opus;base64,", "")
+        try:
+        # Decode the base64 string
+            audio_bytes = base64.b64decode(base64_string)
+            audio_io = io.BytesIO(audio_bytes)
 
-        audio_base64 = base64_string.split(',')[-1] if ',' in base64_string else base64_string
-        audio_bytes = base64.b64decode(audio_base64)
-        audio_io = io.BytesIO(audio_bytes)
-        y, sr = librosa.load(audio_io, sr=desired_sr, duration=duration, offset=offset)
-        y = librosa.util.normalize(y)
-        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=30)
-        return mfcc
-        # audio_bytes = base64.b64decode(base64_string)
-        # audio_io = io.BytesIO(audio_bytes)
-        # audio = AudioSegment.from_file(audio_io, format="webm")
-        
-        # byte_io = io.BytesIO()
-        # audio.export(byte_io, format="wav")
-        # byte_io.seek(0)
+        # Try to load with librosa first
+            try:
+                y, sr = librosa.load(audio_io, sr=desired_sr, duration=duration, offset=offset)
+            except:
+            # If librosa fails, try using pydub
+                audio_io.seek(0)  # Reset file pointer
+                audio = AudioSegment.from_file(audio_io)
+                audio = audio.set_channels(1)  # Convert to mono
+                audio = audio.set_frame_rate(desired_sr)
+            
+                samples = np.array(audio.get_array_of_samples())
+                y = samples.astype(np.float32) / 32768.0  # Normalize
+                sr = desired_sr
 
-        # sample_rate, audio_array = wavfile.read(byte_io)
+        # Normalize the audio
+            y = librosa.util.normalize(y)
 
-        # audio_array = librosa.resample(audio_array.astype(float), orig_sr=sample_rate, target_sr=desired_sr)
-        # start_sample = int(offset * desired_sr)
-        # end_sample = start_sample + int(duration * desired_sr)
-        # audio_array = audio_array[start_sample:end_sample]
+        # Extract MFCC features
+            mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=30)
 
-        
-        # # X, sample_rate = librosa.load(audio_io, duration=duration, sr=desired_sr, offset=offset)
-        # X = librosa.util.normalize(audio_array)
-        # return librosa.feature.mfcc(y=X, sr=desired_sr, n_mfcc=30)
+            return mfcc
+
+        except Exception as e:
+            print(f"Error in preprocess_audio_data: {str(e)}")
+            raise
     
     def perform_emotion_analysis(self, features, emotion_padding=216, depression_padding=2584):
         emotion_features = self.get_mfcc_features(features, emotion_padding)