fix white space

examples/grpo/cosyvoice2/README.md

@@ -36,7 +36,7 @@ Stage `0` converts raw JSONL files into the parquet format expected by veRL:
 ```bash
 bash run.sh 0 0
 ```
-Create two JSONL files—`train.jsonl` and `test.jsonl`.  
+Create two JSONL files—`train.jsonl` and `test.jsonl`.
 The script will then generate two Parquet files:
 
 ```
@@ -111,7 +111,7 @@ bash run.sh 5 5
 
 The script converts the Hugging Face checkpoint back into the format expected by the CosyVoice repository.
 > [!TIP]
->  However, we observed a slight accuracy drop when using the RL-trained model after conversion, compared with the Hugging Face format. 
+>  However, we observed a slight accuracy drop when using the RL-trained model after conversion, compared with the Hugging Face format.
 
 ## Results
 

examples/grpo/cosyvoice2/run.sh

@@ -33,7 +33,7 @@ fi
 
 if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
   log "stage -1: download official CosyVoice2-0.5B LLM model and convert to huggingface compatible checkpoint"
-  modelscope download --model iic/CosyVoice2-0.5B --local_dir $model_scope_model_path 
+  modelscope download --model iic/CosyVoice2-0.5B --local_dir $model_scope_model_path
   python3 pretrained_to_huggingface.py \
     --pretrained-cosyvoice2-path $model_scope_model_path \
     --save-path $sft_model_path
@@ -61,7 +61,7 @@ fi
 if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
   log "stage 1: start token2wav asr server for reward function"
   python3 token2wav_asr_server.py --number-of-devices 8
-fi 
+fi
 
 exp_name=official_llm_aishell3_grpo
 if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
@@ -125,7 +125,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
       --backend fsdp \
       --local_dir $llm_path/actor \
       --target_dir $llm_path/merged_hf_model || exit 1
-fi 
+fi
 
 if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
   log "stage 4: Test the model"

runtime/triton_trtllm/model_repo/cosyvoice2_dit/1/model.py

@@ -254,7 +254,7 @@ class TritonPythonModel:
         target_speech_tokens_tensor = pb_utils.Tensor.from_dlpack("target_speech_tokens", to_dlpack(target_speech_tokens))
         finalize_tensor = pb_utils.Tensor("finalize", np.array([[finalize]], dtype=np.bool_))
         inputs_tensor = [target_speech_tokens_tensor, reference_wav, reference_wav_len, finalize_tensor]
-        
+
         # Create and execute inference request
         inference_request = pb_utils.InferenceRequest(
             model_name='token2wav_dit',
@@ -362,7 +362,7 @@ class TritonPythonModel:
                         chunk_index += 1
                     else:
                         break
-            
+
             this_tts_speech_token = torch.tensor(semantic_token_ids_arr[token_offset:]).unsqueeze(dim=0).to(torch.int32).to(self.device)
             sub_tts_speech = await self.forward_token2wav(chunk_index, this_tts_speech_token, request_id, wav, wav_len, True)
             audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech))

runtime/triton_trtllm/model_repo/token2wav_dit/1/model.py

@@ -62,7 +62,7 @@ def get_spk_id_from_prompt_audio(tensor: torch.Tensor) -> str:
     # Create a SHA-256 hash of the byte string
     hasher = hashlib.sha256()
     hasher.update(tensor_bytes)
-    
+
     return hasher.hexdigest()
 
 class TritonPythonModel:
@@ -111,9 +111,9 @@ class TritonPythonModel:
             target_speech_tokens = target_speech_tokens.squeeze().tolist()
 
             finalize = pb_utils.get_input_tensor_by_name(request, "finalize").as_numpy().item()
-                
+
             request_id = request.request_id()
-               
+
 
             wav_array = pb_utils.get_input_tensor_by_name(
                 request, "reference_wav").as_numpy()

runtime/triton_trtllm/model_repo/token2wav_dit/1/token2wav_dit.py

@@ -133,7 +133,6 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
         option.intra_op_num_threads = 1
         self.spk_model = onnxruntime.InferenceSession(f"{model_dir}/campplus.onnx", sess_options=option,
                                                     providers=["CPUExecutionProvider"])
-        
         self.audio_tokenizer = s3tokenizer.load_model(f"{model_dir}/speech_tokenizer_v2_25hz.onnx").to(self.device).eval()
 
         gpu="l20"
@@ -253,7 +252,7 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
             speech_tokens_i = prompt_speech_tokens[i, :prompt_speech_tokens_lens[i].item()].tolist()
             prompt_speech_tokens_list.append(speech_tokens_i)
         return prompt_speech_tokens_list
-    
+
     def get_spk_emb(self, prompt_audios_list: list[torch.Tensor]) -> torch.Tensor:
         spk_emb_for_flow = []
         for audio in prompt_audios_list:
@@ -263,11 +262,11 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
             spk_emb = self.forward_spk_embedding(spk_feat)
 
             spk_emb_for_flow.append(spk_emb)
-        spk_emb_for_flow = torch.tensor(spk_emb_for_flow)  
+        spk_emb_for_flow = torch.tensor(spk_emb_for_flow)
         if self.dtype != torch.float32:
             spk_emb_for_flow = spk_emb_for_flow.to(self.dtype)
         return spk_emb_for_flow
-    
+
     def get_prompt_mels(self, prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int]):
         prompt_mels_for_flow = []
         prompt_mels_lens_for_flow = []
@@ -283,7 +282,7 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
         prompt_mels_for_flow = torch.nn.utils.rnn.pad_sequence(prompt_mels_for_flow, batch_first=True, padding_value=0)  # [B, T', num_mels=80]
         prompt_mels_lens_for_flow = torch.tensor(prompt_mels_lens_for_flow)
         return prompt_mels_for_flow, prompt_mels_lens_for_flow
-    
+
     def forward_flow(self, prompt_speech_tokens_list: list[list[int]], generated_speech_tokens_list: list[list[int]], prompt_mels_for_flow: torch.Tensor, prompt_mels_lens_for_flow: torch.Tensor, spk_emb_for_flow: torch.Tensor):
         batch_size = prompt_mels_for_flow.shape[0]
         flow_inputs = []
@@ -318,28 +317,24 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
         self, generated_speech_tokens_list: list[list[int]], prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int]
     ):
         assert all(sample_rate == 16000 for sample_rate in prompt_audios_sample_rate)
-        
 
         prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow = self.prepare_prompt_audio(prompt_audios_list, prompt_audios_sample_rate)
 
         generated_mels, generated_mels_lens = self.forward_flow(prompt_speech_tokens_list, generated_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow)
 
         generated_wavs = self.forward_hift(generated_mels, generated_mels_lens, prompt_mels_lens_for_flow)
-        
         return generated_wavs
 
     def prepare_prompt_audio(
         self, prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int]
     ):
         assert all(sample_rate == 16000 for sample_rate in prompt_audios_sample_rate)
-        
 
         prompt_speech_tokens_list = self.prompt_audio_tokenization(prompt_audios_list)
 
         prompt_mels_for_flow, prompt_mels_lens_for_flow = self.get_prompt_mels(prompt_audios_list, prompt_audios_sample_rate)
 
         spk_emb_for_flow = self.get_spk_emb(prompt_audios_list)
-        
         return prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow
 
 
@@ -365,7 +360,7 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
     @torch.inference_mode()
     def forward_streaming(
         self, generated_speech_tokens: list[int], last_chunk: bool, request_id: str, speaker_id: str, prompt_audio: torch.Tensor = None, prompt_audio_sample_rate: int = 16000
-    ):  
+    ):
         if speaker_id not in self.speaker_cache:
             assert prompt_audio is not None, "prompt_audio is required for new speaker"
             assert prompt_audio_sample_rate == 16000
@@ -384,7 +379,7 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
         if request_id not in self.streaming_flow_cache:
             self.streaming_flow_cache[request_id] = {k: v.clone() for k, v in self.speaker_cache[speaker_id]['cache_dict'].items()}
             self.hift_cache_dict[request_id] = dict(
-            mel = torch.zeros(1, 80, 0, device='cuda'), 
+            mel = torch.zeros(1, 80, 0, device='cuda'),
             source = torch.zeros(1, 1, 0, device='cuda'),
             speech = torch.zeros(1, 0, device='cuda'),
             )
@@ -445,7 +440,7 @@ def collate_fn(batch):
     ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = [], [], [], []
     for i, item in enumerate(batch):
         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_sample_rate.append(item['prompt_audio']['sampling_rate'])
         ids.append(item['id'])
@@ -473,20 +468,20 @@ 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):
         start_time = time.time()
-        
+
         for batch in data_loader:
             ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = batch
 
             generated_wavs = model(generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate)
-            
+
 
             for id, wav in zip(ids, generated_wavs):
                 torchaudio.save(f"{args.output_dir}/{id}.wav", wav.cpu(), 24000)
-        
+
         end_time = time.time()
         epoch_time = end_time - start_time
         print(f"Measurement epoch time taken: {epoch_time:.4f} seconds")

runtime/triton_trtllm/offline_inference.py

@@ -365,7 +365,6 @@ def main(args):
         runner = None
     else:
         raise ValueError(f"Unsupported backend: {args.backend}")
-    
     if 'Step-Audio-2-mini' in args.token2wav_path:
         from token2wav_dit import CosyVoice2_Token2Wav
     else:

runtime/triton_trtllm/streaming_inference.py

@@ -14,7 +14,7 @@ def collate_fn(batch):
     prompt_speech_tokens_list, prompt_text_list = [], []
     for i, item in enumerate(batch):
         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_sample_rate.append(item['prompt_audio']['sampling_rate'])
         ids.append(item['id'])
@@ -37,7 +37,7 @@ def get_args():
 
 if __name__ == "__main__":
     args = get_args()
-    
+
     if not os.path.exists(args.output_dir):
         os.makedirs(args.output_dir)
 
@@ -46,7 +46,7 @@ if __name__ == "__main__":
     data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, collate_fn=collate_fn, num_workers=0)
 
     token2wav_model = CosyVoice2_Token2Wav(model_dir=args.model_dir, enable_trt=args.enable_trt, streaming=True)
-    
+
     CHUNK_SIZE = 25
     token_frame_rate = 25
     OVERLAP_SIZE = 0
@@ -68,7 +68,7 @@ if __name__ == "__main__":
 
             semantic_token_ids_arr, token_offset = [], 0
             flow_prompt_speech_token_len = len(prompt_speech_tokens)
-    
+
             buffer = generated_speech_tokens
             output_wavs = []
             chunk_index = 0
@@ -97,7 +97,7 @@ if __name__ == "__main__":
                 output_wavs[i] = wav.cpu().numpy().squeeze()
 
 
-            audios = output_wavs            
+            audios = output_wavs
             reconstructed_audio = np.concatenate(audios)
             sf.write(os.path.join(args.output_dir, f"{id}.wav"), reconstructed_audio, 24000, "PCM_16")