diff --git a/whisperx/asr.py b/whisperx/asr.py
index 88d5bf6..2fab8bc 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -214,7 +214,7 @@ class FasterWhisperPipeline(Pipeline):
         return final_iterator
 
     def transcribe(
-        self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0
+        self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0, language=None, task=None
     ) -> TranscriptionResult:
         if isinstance(audio, str):
             audio = load_audio(audio)
@@ -229,13 +229,12 @@ class FasterWhisperPipeline(Pipeline):
         vad_segments = self.vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE})
         vad_segments = merge_chunks(vad_segments, 30)
 
-        del_tokenizer = False
-        if self.tokenizer is None:
-            language = self.detect_language(audio)
-            self.tokenizer = faster_whisper.tokenizer.Tokenizer(self.model.hf_tokenizer, self.model.model.is_multilingual, task="transcribe", language=language)
-            del_tokenizer = True
-        else:
-            language = self.tokenizer.language_code
+        language = language or self.tokenizer.language_code
+        task = task or self.tokenizer.task
+        if task != self.tokenizer.task or language != self.tokenizer.language_code:
+            self.tokenizer = faster_whisper.tokenizer.Tokenizer(self.model.hf_tokenizer,
+                                                                self.model.model.is_multilingual, task=task,
+                                                                language=language)
 
         segments: List[SingleSegment] = []
         batch_size = batch_size or self._batch_size
@@ -250,9 +249,6 @@ class FasterWhisperPipeline(Pipeline):
                     "end": round(vad_segments[idx]['end'], 3)
                 }
             )
-        
-        if del_tokenizer:
-            self.tokenizer = None
 
         return {"segments": segments, "language": language}