fix lint

runtime/triton_trtllm/client_grpc.py

@@ -413,7 +413,7 @@ def run_sync_streaming_inference(
                 for i in range(1, len(audios)):
                     # Cross-fade section
                     cross_faded_overlap = (audios[i][:cross_fade_samples] * fade_in +
-                                        audios[i - 1][-cross_fade_samples:] * fade_out)
+                                           audios[i - 1][-cross_fade_samples:] * fade_out)
                     # Middle section of the current chunk
                     middle_part = audios[i][cross_fade_samples:-cross_fade_samples]
                     # Concatenate

runtime/triton_trtllm/model_repo/cosyvoice2/1/model.py

@@ -41,11 +41,11 @@ from transformers import AutoTokenizer
 import torchaudio
 
 
-
 from matcha.utils.audio import mel_spectrogram
 
 torch.set_num_threads(1)
 
+
 class TritonPythonModel:
     """Triton Python model for Spark TTS.
 
@@ -65,7 +65,7 @@ class TritonPythonModel:
         parameters = self.model_config['parameters']
         model_params = {k: v["string_value"] for k, v in parameters.items()}
         self.logger.log_info(f"model_params:{model_params}")
-        self.dynamic_chunk_strategy = model_params.get("dynamic_chunk_strategy", "exponential") # "exponential" or "time_based"
+        self.dynamic_chunk_strategy = model_params.get("dynamic_chunk_strategy", "exponential")  # "exponential" or "time_based"
         self.logger.log_info(f"Using dynamic chunk strategy: {self.dynamic_chunk_strategy}")
 
         # Initialize tokenizer
@@ -193,7 +193,6 @@ class TritonPythonModel:
 
         return prompt_speech_tokens
 
-
     def forward_speaker_embedding(self, wav):
         """Forward pass through the speaker embedding component.
 
@@ -219,7 +218,6 @@ class TritonPythonModel:
 
         return prompt_spk_embedding
 
-
     def forward_token2wav(
             self,
             prompt_speech_tokens: torch.Tensor,
@@ -254,7 +252,6 @@ class TritonPythonModel:
             inputs_tensor.append(token_offset_tensor)
             inputs_tensor.append(finalize_tensor)
 
-
         # Create and execute inference request
         inference_request = pb_utils.InferenceRequest(
             model_name='token2wav',
@@ -281,7 +278,6 @@ class TritonPythonModel:
         input_ids = torch.cat([input_ids, prompt_speech_tokens], dim=1)
         return input_ids
 
-
     def _extract_speech_feat(self, speech):
         speech_feat = mel_spectrogram(
             speech,
@@ -299,7 +295,6 @@ class TritonPythonModel:
         speech_feat = speech_feat.unsqueeze(dim=0)
         return speech_feat
 
-
     def _llm_gen_thread(self, generated_ids_iter, semantic_token_ids_arr, llm_is_done_flag):
         for generated_ids in generated_ids_iter:
             generated_ids = generated_ids.tolist()
@@ -338,9 +333,8 @@ class TritonPythonModel:
             prompt_speech_feat = speech_feat[:, :2 * token_len].contiguous().half()
             prompt_speech_tokens = prompt_speech_tokens[:, :token_len].contiguous()
 
-
             flow_prompt_speech_token_len = prompt_speech_tokens.shape[-1]
-            
+
             reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy()
             reference_text = reference_text[0][0].decode('utf-8')
 
@@ -385,7 +379,9 @@ class TritonPythonModel:
                         this_tts_speech_token = semantic_token_ids_arr[:token_offset + this_token_hop_len + self.flow_pre_lookahead_len]
                         this_tts_speech_token = torch.tensor(this_tts_speech_token).unsqueeze(dim=0).to(torch.int32).to(self.device)
 
-                        sub_tts_speech = self.forward_token2wav(prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding, this_tts_speech_token, request_id, token_offset, False)
+                        sub_tts_speech = self.forward_token2wav(
+                            prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding,
+                            this_tts_speech_token, request_id, token_offset, False)
 
                         audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech))
                         inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor])
@@ -413,7 +409,6 @@ class TritonPythonModel:
                                     else:
                                         this_token_hop_len = self.token_hop_len
                                     this_token_hop_len = max(self.token_hop_len, this_token_hop_len)
-                        
                         chunk_index += 1
                     else:
                         time.sleep(0.02)
@@ -423,7 +418,7 @@ class TritonPythonModel:
                 audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech))
                 inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor])
                 response_sender.send(inference_response)
-                
+
                 llm_thread.join()
                 response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL)
                 self.logger.log_info("send tritonserver_response_complete_final to end")

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

@@ -57,13 +57,13 @@ class TritonPythonModel:
         self.device = torch.device("cuda")
 
         model_dir = model_params["model_dir"]
-        gpu="l20"
+        gpu = "l20"
         enable_trt = True
         if enable_trt:
             self.load_spk_trt(f'{model_dir}/campplus.{gpu}.fp32.trt',
-                                f'{model_dir}/campplus.onnx',
-                                1,
-                                False)
+                              f'{model_dir}/campplus.onnx',
+                              1,
+                              False)
         else:
             campplus_model = f'{model_dir}/campplus.onnx'
             option = onnxruntime.SessionOptions()
@@ -121,7 +121,7 @@ class TritonPythonModel:
                     assert spk_model.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True
                     torch.cuda.current_stream().synchronize()
                 self.spk_model.release_estimator(spk_model, stream)
-        
+
         return embedding.half()
 
     def execute(self, requests):
@@ -142,7 +142,6 @@ class TritonPythonModel:
             wav_array = torch.from_numpy(wav_array).to(self.device)
 
             embedding = self._extract_spk_embedding(wav_array)
- 
 
             prompt_spk_embedding_tensor = pb_utils.Tensor.from_dlpack(
                 "prompt_spk_embedding", to_dlpack(embedding))

runtime/triton_trtllm/model_repo/token2wav/1/model.py

@@ -49,6 +49,7 @@ logger = logging.getLogger(__name__)
 ORIGINAL_VOCAB_SIZE = 151663
 torch.set_num_threads(1)
 
+
 class CosyVoice2:
 
     def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, trt_concurrent=1, device='cuda'):
@@ -123,7 +124,6 @@ class CosyVoice2Model:
         input_names = ["x", "mask", "mu", "cond"]
         return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}
 
-
     def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0):
         with torch.cuda.amp.autocast(self.fp16):
             tts_mel, _ = self.flow.inference(token=token.to(self.device),