diff --git a/whisperx/asr.py b/whisperx/asr.py
index 0ea03b6..95ba098 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -6,6 +6,9 @@ import ctranslate2
 import faster_whisper
 import numpy as np
 import torch
+from faster_whisper.tokenizer import Tokenizer
+from faster_whisper.transcribe import (TranscriptionOptions,
+                                       get_ctranslate2_storage)
 from transformers import Pipeline
 from transformers.pipelines.pt_utils import PipelineIterator
 
@@ -28,7 +31,13 @@ class WhisperModel(faster_whisper.WhisperModel):
     Currently only works in non-timestamp mode and fixed prompt for all samples in batch.
     '''
 
-    def generate_segment_batched(self, features: np.ndarray, tokenizer: faster_whisper.tokenizer.Tokenizer, options: faster_whisper.transcribe.TranscriptionOptions, encoder_output = None):
+    def generate_segment_batched(
+        self,
+        features: np.ndarray,
+        tokenizer: Tokenizer,
+        options: TranscriptionOptions,
+        encoder_output=None,
+    ):
         batch_size = features.shape[0]
         all_tokens = []
         prompt_reset_since = 0
@@ -81,7 +90,7 @@ class WhisperModel(faster_whisper.WhisperModel):
         # unsqueeze if batch size = 1
         if len(features.shape) == 2:
             features = np.expand_dims(features, 0)
-        features = faster_whisper.transcribe.get_ctranslate2_storage(features)
+        features = get_ctranslate2_storage(features)
 
         return self.model.encode(features, to_cpu=to_cpu)
 
@@ -193,17 +202,23 @@ class FasterWhisperPipeline(Pipeline):
         if self.tokenizer is None:
             language = language or self.detect_language(audio)
             task = task or "transcribe"
-            self.tokenizer = faster_whisper.tokenizer.Tokenizer(self.model.hf_tokenizer,
-                                                                self.model.model.is_multilingual, task=task,
-                                                                language=language)
+            self.tokenizer = Tokenizer(
+                self.model.hf_tokenizer,
+                self.model.model.is_multilingual,
+                task=task,
+                language=language,
+            )
         else:
             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)
-                
+                self.tokenizer = Tokenizer(
+                    self.model.hf_tokenizer,
+                    self.model.model.is_multilingual,
+                    task=task,
+                    language=language,
+                )
+
         if self.suppress_numerals:
             previous_suppress_tokens = self.options.suppress_tokens
             numeral_symbol_tokens = find_numeral_symbol_tokens(self.tokenizer)
@@ -297,7 +312,7 @@ def load_model(whisper_arch,
                          local_files_only=local_files_only,
                          cpu_threads=threads)
     if language is not None:
-        tokenizer = faster_whisper.tokenizer.Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task=task, language=language)
+        tokenizer = Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task=task, language=language)
     else:
         print("No language specified, language will be first be detected for each audio file (increases inference time).")
         tokenizer = None
@@ -338,7 +353,7 @@ def load_model(whisper_arch,
     suppress_numerals = default_asr_options["suppress_numerals"]
     del default_asr_options["suppress_numerals"]
 
-    default_asr_options = faster_whisper.transcribe.TranscriptionOptions(**default_asr_options)
+    default_asr_options = TranscriptionOptions(**default_asr_options)
 
     default_vad_options = {
         "vad_onset": 0.500,