diff --git a/lollms/media.py b/lollms/media.py
index 7dda7b9..d2787ba 100644
--- a/lollms/media.py
+++ b/lollms/media.py
@@ -46,6 +46,12 @@ matplotlib.use('Agg')
 
 if not PackageManager.check_package_installed("whisper"):
     PackageManager.install_package("openai-whisper")
+    try:
+        import conda.cli
+        conda.cli.main("install", "conda-forge::ffmpeg", "-y")
+    except:
+        ASCIIColors.bright_red("Couldn't install ffmpeg. whisper won't work. Please install it manually")
+
 import whisper
 
 import socketio
@@ -90,6 +96,7 @@ class AudioRecorder:
         self.is_recording = False
         self.start_time = time.time()
         self.last_time = time.time()
+        self.whisper = whisper.load_model("base")
 
     def audio_callback(self, indata, frames, time_, status):
         volume_norm = np.linalg.norm(indata)*10
@@ -120,6 +127,11 @@ class AudioRecorder:
         write(self.filename, self.sample_rate, self.buffer)
         self.lollmsCom.info(f"Saved to {self.filename}")
         self.lollmsCom.info(f"Transcribing ... ")
+        result = self.whisper.transcribe(str(self.filename))
+        transcription_fn = str(self.filename)+".txt"
+        with open(transcription_fn, "w", encoding="utf-8") as f:
+            f.write(result["text"])
+        self.lollmsCom.info(f"File saved to {transcription_fn}")
 
     def update_spectrogram(self):
         f, t, Sxx = spectrogram(self.buffer[-30*self.sample_rate:], self.sample_rate)
diff --git a/lollms/personality.py b/lollms/personality.py
index 0ccce71..771a0a5 100644
--- a/lollms/personality.py
+++ b/lollms/personality.py
@@ -131,6 +131,9 @@ class AIPersonality:
 
         self.installation_option = installation_option
 
+        # Whisper to transcribe audio
+        self.whisper = None
+
         # First setup a default personality
         # Version
         self._version = pkg_resources.get_distribution('lollms').version
@@ -762,7 +765,30 @@ Date: {{date}}
         db_path = self.lollms_paths.personal_databases_path / "personalities" / self.name / "db.json"
         db_path.parent.mkdir(parents=True, exist_ok=True)
         path = Path(path)
-        if path.suffix in [".png",".jpg",".gif",".bmp",".webp"]:
+        if path.suffix in [".wav",".mp3"]:
+            if self.whisper is None:
+                if not PackageManager.check_package_installed("whisper"):
+                    PackageManager.install_package("openai-whisper")
+                    try:
+                        import conda.cli
+                        conda.cli.main("install", "conda-forge::ffmpeg", "-y")
+                    except:
+                        ASCIIColors.bright_red("Couldn't install ffmpeg. whisper won't work. Please install it manually")
+
+                import whisper
+                self.whisper = whisper.load_model("base")
+
+
+            self.info(f"Transcribing ... ")
+            self.step_start("Transcribing ... ")
+            result = self.whisper.transcribe(str(self.filename))
+            transcription_fn = str(path)+".txt"
+            with open(transcription_fn, "w", encoding="utf-8") as f:
+                f.write(result["text"])
+            self.info(f"File saved to {transcription_fn}")
+            self.full(result["text"])
+            self.step_end("Transcribing ... ")
+        elif path.suffix in [".png",".jpg",".gif",".bmp",".webp"]:
             if self.callback:
                 try:
                     pth = str(path).replace("\\","/").split('/')