fix lint

runtime/triton_trtllm/offline_inference.py

@@ -180,7 +180,7 @@ def data_collator(batch, tokenizer, s3_tokenizer):
     input_ids_list, prompt_audio_list, prompt_text_list = [], [], []
     prompt_text_after_apply_template_list = []
     mels, prompt_audio_cosy2tokens_list, full_text_list = [], [], []
-    for i, item in enumerate(batch):
+    for _, item in enumerate(batch):
         audio_processing_start_time = time.time()
         prompt_text, target_text = (
             item["prompt_text"],
@@ -402,7 +402,7 @@ def main(args):
                     )
                     torch.cuda.synchronize()
                 elif args.backend == "trtllm":
-                    batch_input_ids = [ids for ids in batch["input_ids"]]
+                    batch_input_ids = list(batch["input_ids"])
                     input_lengths = [x.size(0) for x in batch_input_ids]
 
                     end_id = tokenizer.convert_tokens_to_ids("<|eos1|>") if "<|eos1|>" in tokenizer.get_vocab() else tokenizer.eos_token_id

runtime/triton_trtllm/token2wav.py

@@ -286,7 +286,7 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
 
 def collate_fn(batch):
     ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = [], [], [], []
-    for i, item in enumerate(batch):
+    for _, item in enumerate(batch):
         generated_speech_tokens_list.append(item['target_audio_cosy2_tokens'])
         audio = torch.from_numpy(item['prompt_audio']['array']).float()
         prompt_audios_list.append(audio)
@@ -319,7 +319,7 @@ if __name__ == "__main__":
 
     data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, collate_fn=collate_fn, num_workers=0)
 
-    for epoch in range(args.warmup):
+    for _ in range(args.warmup):
         start_time = time.time()
 
         for batch in data_loader: