diff --git a/whisperx/asr.py b/whisperx/asr.py
index 713531c..d0e6962 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -13,8 +13,16 @@ from .audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
 from .vad import load_vad_model, merge_chunks
 from .types import TranscriptionResult, SingleSegment
 
-def load_model(whisper_arch, device, device_index=0, compute_type="float16", asr_options=None, language=None,
-               vad_options=None, model=None, task="transcribe"):
+def load_model(whisper_arch,
+               device,
+               device_index=0,
+               compute_type="float16",
+               asr_options=None,
+               language=None,
+               vad_options=None,
+               model=None,
+               task="transcribe",
+               download_root=None):
     '''Load a Whisper model for inference.
     Args:
         whisper_arch: str - The name of the Whisper model to load.
@@ -22,14 +30,19 @@ def load_model(whisper_arch, device, device_index=0, compute_type="float16", asr
         compute_type: str - The compute type to use for the model.
         options: dict - A dictionary of options to use for the model.
         language: str - The language of the model. (use English for now)
+        download_root: Optional[str] - The root directory to download the model to.
     Returns:
         A Whisper pipeline.
-    '''    
+    '''
 
     if whisper_arch.endswith(".en"):
         language = "en"
 
-    model = WhisperModel(whisper_arch, device=device, device_index=device_index, compute_type=compute_type)
+    model = WhisperModel(whisper_arch,
+                         device=device,
+                         device_index=device_index,
+                         compute_type=compute_type,
+                         download_root=download_root)
     if language is not None:
         tokenizer = faster_whisper.tokenizer.Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task=task, language=language)
     else:
@@ -114,7 +127,7 @@ class WhisperModel(faster_whisper.WhisperModel):
                 # suppress_tokens=options.suppress_tokens,
                 # max_initial_timestamp_index=max_initial_timestamp_index,
             )
-    
+
         tokens_batch = [x.sequences_ids[0] for x in result]
 
         def decode_batch(tokens: List[List[int]]) -> str:
@@ -127,7 +140,7 @@ class WhisperModel(faster_whisper.WhisperModel):
         text = decode_batch(tokens_batch)
 
         return text
-    
+
     def encode(self, features: np.ndarray) -> ctranslate2.StorageView:
         # When the model is running on multiple GPUs, the encoder output should be moved
         # to the CPU since we don't know which GPU will handle the next job.
@@ -136,9 +149,9 @@ class WhisperModel(faster_whisper.WhisperModel):
         if len(features.shape) == 2:
             features = np.expand_dims(features, 0)
         features = faster_whisper.transcribe.get_ctranslate2_storage(features)
-        
+
         return self.model.encode(features, to_cpu=to_cpu)
-    
+
 class FasterWhisperPipeline(Pipeline):
     """
     Huggingface Pipeline wrapper for FasterWhisperModel.
@@ -176,7 +189,7 @@ class FasterWhisperPipeline(Pipeline):
                 self.device = torch.device(f"cuda:{device}")
         else:
             self.device = device
-        
+
         super(Pipeline, self).__init__()
         self.vad_model = vad
 
@@ -194,7 +207,7 @@ class FasterWhisperPipeline(Pipeline):
     def _forward(self, model_inputs):
         outputs = self.model.generate_segment_batched(model_inputs['inputs'], self.tokenizer, self.options)
         return {'text': outputs}
-    
+
     def postprocess(self, model_outputs):
         return model_outputs
 
@@ -218,7 +231,7 @@ class FasterWhisperPipeline(Pipeline):
     ) -> TranscriptionResult:
         if isinstance(audio, str):
             audio = load_audio(audio)
-        
+
         def data(audio, segments):
             for seg in segments:
                 f1 = int(seg['start'] * SAMPLE_RATE)