From 52556a6de9803b994bf96a9c15cb9cc70b015bf8 Mon Sep 17 00:00:00 2001 From: root Date: Mon, 8 Sep 2025 09:59:58 +0000 Subject: [PATCH] fix lint --- runtime/triton_trtllm/offline_inference.py | 4 ++-- runtime/triton_trtllm/token2wav.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/runtime/triton_trtllm/offline_inference.py b/runtime/triton_trtllm/offline_inference.py index 853aefe..6f1a836 100644 --- a/runtime/triton_trtllm/offline_inference.py +++ b/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 diff --git a/runtime/triton_trtllm/token2wav.py b/runtime/triton_trtllm/token2wav.py index 86e4625..09b6db6 100644 --- a/runtime/triton_trtllm/token2wav.py +++ b/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: