fix lint

runtime/triton_trtllm/model_repo/audio_tokenizer/1/model.py

@@ -35,33 +35,34 @@ import s3tokenizer
 
 ORIGINAL_VOCAB_SIZE = 151663
 
+
 class TritonPythonModel:
     """Triton Python model for audio tokenization.
-    
+
     This model takes reference audio input and extracts semantic tokens
     using s3tokenizer.
     """
 
     def initialize(self, args):
         """Initialize the model.
-        
+
         Args:
             args: Dictionary containing model configuration
         """
         # Parse model parameters
         parameters = json.loads(args['model_config'])['parameters']
         model_params = {k: v["string_value"] for k, v in parameters.items()}
-        
+
         self.device = torch.device("cuda")
         model_path = os.path.join(model_params["model_dir"], "speech_tokenizer_v2.onnx")
         self.audio_tokenizer = s3tokenizer.load_model(model_path).to(self.device)
 
     def execute(self, requests):
         """Execute inference on the batched requests.
-        
+
         Args:
             requests: List of inference requests
-            
+
         Returns:
             List of inference responses containing tokenized outputs
         """
@@ -79,18 +80,18 @@ class TritonPythonModel:
             # Prepare inputs
             wav = wav_array[:, :wav_len].squeeze(0)
             mels.append(s3tokenizer.log_mel_spectrogram(wav))
-            
+
         mels, mels_lens = s3tokenizer.padding(mels)
         codes, codes_lens = self.audio_tokenizer.quantize(mels.to(self.device), mels_lens.to(self.device))
         codes = codes.clone() + ORIGINAL_VOCAB_SIZE
-        
+
         responses = []
         for i in range(len(requests)):
-            prompt_speech_tokens = codes[i, :codes_lens[i].item()]            
+            prompt_speech_tokens = codes[i, :codes_lens[i].item()]
             prompt_speech_tokens_tensor = pb_utils.Tensor.from_dlpack(
                 "prompt_speech_tokens", to_dlpack(prompt_speech_tokens))
             inference_response = pb_utils.InferenceResponse(
                 output_tensors=[prompt_speech_tokens_tensor])
             responses.append(inference_response)
-                             
-        return responses
+
+        return responses