diff --git a/models/transcription/transcription_transcriber.py b/models/transcription/transcription_transcriber.py
index c1856b34..a535cd8a 100644
--- a/models/transcription/transcription_transcriber.py
+++ b/models/transcription/transcription_transcriber.py
@@ -10,6 +10,7 @@ from .transcription_whisper import getWhisperModel, checkWhisperWeight
 
 import torch
 import numpy as np
+from pydub import AudioSegment
 
 PHRASE_TIMEOUT = 3
 MAX_PHRASES = 10
@@ -97,21 +98,21 @@ class AudioTranscriber:
         return audio_data
 
     def processSpeakerData(self):
-        original_channels = self.audio_sources["channels"]
-        if original_channels <= 2:
-            channels = original_channels
-            sample_rate = self.audio_sources["sample_rate"]
-        else:
-            channels = 2
-            sample_rate = self.audio_sources["sample_rate"]*original_channels/2
-
         temp_file = BytesIO()
         with wave.open(temp_file, 'wb') as wf:
-            wf.setnchannels(channels)
+            wf.setnchannels(self.audio_sources["channels"])
             wf.setsampwidth(get_sample_size(paInt16))
-            wf.setframerate(sample_rate)
+            wf.setframerate(self.audio_sources["sample_rate"])
             wf.writeframes(self.audio_sources["last_sample"])
         temp_file.seek(0)
+
+        if self.audio_sources["channels"] > 2:
+            audio = AudioSegment.from_file(temp_file, format="wav")
+            mono_audio = audio.set_channels(1)
+            temp_file = BytesIO()
+            mono_audio.export(temp_file, format="wav")
+            temp_file.seek(0)
+
         with AudioFile(temp_file) as source:
             audio = self.audio_recognizer.record(source)
         return audio