diff --git a/whisperx/diarize.py b/whisperx/diarize.py
index 8b10530..3632cd8 100644
--- a/whisperx/diarize.py
+++ b/whisperx/diarize.py
@@ -30,16 +30,16 @@ class DiarizationPipeline:
     ) -> Union[tuple[pd.DataFrame, Optional[dict[str, list[float]]]], pd.DataFrame]:
         """
         Perform speaker diarization on audio.
-        
+
         Args:
             audio: Path to audio file or audio array
             num_speakers: Exact number of speakers (if known)
             min_speakers: Minimum number of speakers to detect
             max_speakers: Maximum number of speakers to detect
             return_embeddings: Whether to return speaker embeddings
-            
+
         Returns:
-            If return_embeddings is True: 
+            If return_embeddings is True:
                 Tuple of (diarization dataframe, speaker embeddings dictionary)
             Otherwise:
                 Just the diarization dataframe
@@ -53,18 +53,18 @@ class DiarizationPipeline:
 
         if return_embeddings:
             diarization, embeddings = self.model(
-                audio_data, 
+                audio_data,
                 num_speakers=num_speakers,
-                min_speakers=min_speakers, 
-                max_speakers=max_speakers, 
-                return_embeddings=True
+                min_speakers=min_speakers,
+                max_speakers=max_speakers,
+                return_embeddings=True,
             )
         else:
             diarization = self.model(
-                audio_data, 
+                audio_data,
                 num_speakers=num_speakers,
-                min_speakers=min_speakers, 
-                max_speakers=max_speakers
+                min_speakers=min_speakers,
+                max_speakers=max_speakers,
             )
             embeddings = None
 
@@ -91,13 +91,13 @@ def assign_word_speakers(
 ) -> Union[AlignedTranscriptionResult, TranscriptionResult]:
     """
     Assign speakers to words and segments in the transcript.
-    
+
     Args:
         diarize_df: Diarization dataframe from DiarizationPipeline
         transcript_result: Transcription result to augment with speaker labels
         speaker_embeddings: Optional dictionary mapping speaker IDs to embedding vectors
         fill_nearest: If True, assign speakers even when there's no direct time overlap
-        
+
     Returns:
         Updated transcript_result with speaker assignments and optionally embeddings
     """
@@ -131,12 +131,12 @@ def assign_word_speakers(
                         # sum over speakers
                         speaker = dia_tmp.groupby("speaker")["intersection"].sum().sort_values(ascending=False).index[0]
                         word["speaker"] = speaker
-    
+
     # Add speaker embeddings to the result if provided
     if speaker_embeddings is not None:
         transcript_result["speaker_embeddings"] = speaker_embeddings
-    
-    return transcript_result            
+
+    return transcript_result
 
 
 class Segment: