deepgeek/CosyVoice
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fix lint

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2025-09-08 09:59:58 +00:00
parent 66ef5a097b
commit 52556a6de9
2 changed files with 4 additions and 4 deletions
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4
runtime/triton_trtllm/offline_inference.py
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@@ -180,7 +180,7 @@ def data_collator(batch, tokenizer, s3_tokenizer):
input_ids_list, prompt_audio_list, prompt_text_list = [], [], [] input_ids_list, prompt_audio_list, prompt_text_list = [], [], []
prompt_text_after_apply_template_list = [] prompt_text_after_apply_template_list = []
mels, prompt_audio_cosy2tokens_list, full_text_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() audio_processing_start_time = time.time()
prompt_text, target_text = ( prompt_text, target_text = (
item["prompt_text"], item["prompt_text"],
@@ -402,7 +402,7 @@ def main(args):
) )
torch.cuda.synchronize() torch.cuda.synchronize()
elif args.backend == "trtllm": 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] 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 end_id = tokenizer.convert_tokens_to_ids("<|eos1|>") if "<|eos1|>" in tokenizer.get_vocab() else tokenizer.eos_token_id

4
runtime/triton_trtllm/token2wav.py
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@@ -286,7 +286,7 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
def collate_fn(batch): def collate_fn(batch):
ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = [], [], [], [] 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']) generated_speech_tokens_list.append(item['target_audio_cosy2_tokens'])
audio = torch.from_numpy(item['prompt_audio']['array']).float() audio = torch.from_numpy(item['prompt_audio']['array']).float()
prompt_audios_list.append(audio) 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) 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() start_time = time.time()
for batch in data_loader: for batch in data_loader: