fix lint

examples/grpo/cosyvoice2/infer_dataset.py

@@ -105,6 +105,7 @@ def extract_speech_ids(speech_tokens_str):
             print(f"Unexpected token: {token_str}")
     return speech_ids
 
+
 def convert_cosy2_tokens_to_speech_id_str(cosy2_tokens):
     """Convert CosyVoice2 tokens to speech IDs string like <|s_23456|>"""
     speech_id_str = ""
@@ -182,14 +183,13 @@ def get_args():
     return args
 
 
-
 def data_collator(batch, tokenizer, s3_tokenizer):
     """Simplified data collator for batch_size=1 processing"""
     target_sample_rate = 16000  # CosyVoice2 uses 16kHz for prompt audio
     device = s3_tokenizer.device if s3_tokenizer is not None else torch.device("cpu")
     input_ids_list, prompt_audio_list, prompt_text_list = [], [], []
     mels, prompt_audio_cosy2tokens_list = [], []
-    for i, item in enumerate(batch):
+    for item in batch:
         prompt_text, target_text = (
             item["prompt_text"],
             item["target_text"],
@@ -227,7 +227,7 @@ def data_collator(batch, tokenizer, s3_tokenizer):
         codes, codes_lens = s3_tokenizer.quantize(mels.to(device), mels_lens.to(device))
         for i in range(len(codes)):
             prompt_audio_cosy2tokens_list.append(codes[i, :codes_lens[i].item()])
-    for i, prompt_audio_cosy2tokens in enumerate(prompt_audio_cosy2tokens_list):
+    for prompt_audio_cosy2tokens in prompt_audio_cosy2tokens_list:
         prompt_audio_cosy2_id_str = convert_cosy2_tokens_to_speech_id_str(prompt_audio_cosy2tokens)
         # Create chat template for LLM generation
         chat = [
@@ -244,7 +244,6 @@ def data_collator(batch, tokenizer, s3_tokenizer):
         )
         input_ids_list.append(input_ids.squeeze(0))
 
-
     # For batch_size=1, no need to pad
     if len(input_ids_list) == 1:
         input_ids = input_ids_list[0].unsqueeze(0)
@@ -256,7 +255,7 @@ def data_collator(batch, tokenizer, s3_tokenizer):
             for input_ids in input_ids_list
         ]
         input_ids = torch.stack(input_ids_list)
-    
+
     ids = [item["id"] for item in batch]
 
     return {
@@ -287,7 +286,7 @@ def main():
     assert torch.cuda.is_available()
     world_size, local_rank, rank = init_distributed()
     device = torch.device(f"cuda:{local_rank}")
-    
+
     # Load LLM model and tokenizer directly
     tokenizer = AutoTokenizer.from_pretrained(args.llm_model_name_or_path)
     model = AutoModelForCausalLM.from_pretrained(args.llm_model_name_or_path)
@@ -329,7 +328,7 @@ def main():
     for batch in dataloader:
         with torch.no_grad():
             input_ids = batch["input_ids"].to(device)
-            
+
             # Generate speech tokens using LLM
             outputs = model.generate(
                 input_ids,
@@ -339,31 +338,31 @@ def main():
                 temperature=args.temperature,
                 top_k=args.top_k,
             )
-            
+
             # Process each sample in the batch
             for i in range(len(batch["ids"])):
                 # Extract generated tokens (excluding input)
                 input_length = input_ids[i].shape[0]
                 generated_ids = outputs[i][input_length:-1]  # Remove last token if needed
                 speech_tokens_str = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
-                
+
                 # Extract speech IDs from token strings like <|s_23456|>
                 speech_ids = extract_speech_ids(speech_tokens_str)
-                
+
                 if len(speech_ids) == 0:
                     print(f"Warning: No speech tokens generated for sample {batch['ids'][i]}, skipping")
                     continue
-                
+
                 # Convert to tensor for CosyVoice2
                 audio_tokens = torch.tensor(speech_ids, dtype=torch.long, device=device).unsqueeze(0)
-                
+
                 if args.prompt_text is not None:
                     current_prompt_text = args.prompt_text
                     current_prompt_audio = prompt_speech_16k
                 else:
                     current_prompt_text = batch["prompt_text"][i]
                     current_prompt_audio = batch["prompt_audio_list"][i]
-                
+
                 if current_prompt_audio is not None:
                     # Generate audio using CosyVoice2
                     audio_hat = audio_decode_cosyvoice2(
@@ -372,18 +371,17 @@ def main():
                         current_prompt_audio,
                         cosyvoice_codec,
                     )
-                    
+
                     # Convert to numpy and save
                     generated_wave = audio_hat.squeeze(0).cpu().numpy()
                     target_sample_rate = 24000
-                    
+
                     utt = batch["ids"][i]
                     sf.write(f"{args.output_dir}/{utt}.wav", generated_wave, target_sample_rate)
 
                     print(f"Generated audio for sample {utt} with {len(speech_ids)} tokens")
                 else:
                     print(f"Warning: No prompt audio available for sample {batch['ids'][i]}, skipping")
-                        
 
         if rank == 0:
             progress_bar.update(world_size * len(batch["ids"]))