diff --git a/src/stt/transcriber.py b/src/stt/transcriber.py
index bfabd95..543f65b 100644
--- a/src/stt/transcriber.py
+++ b/src/stt/transcriber.py
@@ -1,9 +1,7 @@
 import logging
-import os
 
 import numpy as np
 import whisperx
-from whisperx.diarize import DiarizationPipeline
 
 # Do not call basicConfig here, as it's called in the orchestrator
 logger = logging.getLogger(__name__)
@@ -11,14 +9,14 @@ logger = logging.getLogger(__name__)
 
 class Transcriber:
     """
-    Converts audio chunks (numpy arrays) into text and identifies speakers using WhisperX.
+    Converts audio chunks (numpy arrays) into text using WhisperX.
     """
 
     def __init__(
         self, model_size="base", device="cpu", compute_type="int8", language="en"
     ):
         """
-        Initializes the WhisperX model and diarization pipeline.
+        Initializes the WhisperX model.
 
         Args:
             model_size (str): The size of the model to use (e.g., "tiny", "base", "small").
@@ -39,28 +37,20 @@ class Transcriber:
                 model_size, device=device, compute_type=compute_type
             )
 
-            # Load alignment model (required for accurate speaker assignment)
-            # model_dir=None allows automatic model selection based on the language
-            self.align_model, self.align_metadata = whisperx.load_align_model(
-                device=device, model_dir=None, language_code=self.language
-            )
-
-            self.diarize_model = DiarizationPipeline()
-
-            logger.info("WhisperX and Diarization models loaded successfully.")
+            logger.info("WhisperX model loaded successfully.")
         except Exception as e:
             logger.error(f"Failed to load WhisperX models: {e}")
             raise
 
     def transcribe(self, audio_chunk):
         """
-        Transcribes an audio chunk and performs speaker diarization.
+        Transcribes an audio chunk.
 
         Args:
             audio_chunk (np.ndarray): The audio data as a numpy array.
 
         Returns:
-            list: A list of tuples (speaker_id, text).
+            list: A list of tuples (speaker_id, text, start, end).
         """
         if audio_chunk is None:
             return []
@@ -73,35 +63,18 @@ class Transcriber:
             # batch_size is set to 16 for efficiency; can be adjusted based on VRAM
             result = self.model.transcribe(audio, batch_size=16)
 
-            # 2. Perform alignment
-            # Alignment is necessary for the assign_words_to_speakers step
-            result_a = whisperx.align(
-                result["segments"],
-                self.align_model,
-                self.align_metadata,
-                audio,
-                self.device,
-            )
-
-            # 3. Perform diarization
-            diarize_segments = self.diarize_model(audio)
-
-            # 4. Align transcription segments with speakers
-            result_final = whisperx.assign_word_speakers(diarize_segments, result_a)
-
-            # Extract (speaker_id, text, start, end) tuples from the final result
+            # Extract ("Unknown", text, start, end) tuples from the transcription result
             output = []
-            for segment in result_final.get("segments", []):
-                speaker = segment.get("speaker", "Unknown")
+            for segment in result.get("segments", []):
                 text = segment.get("text", "").strip()
                 start = segment.get("start", 0.0)
                 end = segment.get("end", 0.0)
                 if text:
-                    output.append((speaker, text, start, end))
+                    output.append(("Unknown", text, start, end))
 
             return output
         except Exception as e:
-            logger.error(f"Transcription/Diarization error: {e}")
+            logger.error(f"Transcription error: {e}")
             return []
 
     def close(self):