add cosyvoice3 vllm example

cosyvoice/utils/file_utils.py

@@ -88,6 +88,7 @@ def convert_onnx_to_trt(trt_model, trt_kwargs, onnx_model, fp16):
     logging.info("Succesfully convert onnx to trt...")
 
 
+# NOTE do not support bistream inference as only speech token embedding/head is kept
 def export_cosyvoice2_vllm(model, model_path, device):
     if os.path.exists(model_path):
         return
@@ -98,11 +99,13 @@ def export_cosyvoice2_vllm(model, model_path, device):
 
     dtype = torch.bfloat16
     # lm_head
-    new_lm_head = torch.nn.Linear(in_features=feature_size, out_features=pad_vocab_size, bias=True)
+    use_bias = True if model.llm_decoder.bias is not None else False
+    new_lm_head = torch.nn.Linear(in_features=feature_size, out_features=pad_vocab_size, bias=use_bias)
     with torch.no_grad():
         new_lm_head.weight[:vocab_size] = model.llm_decoder.weight
-        new_lm_head.bias[:vocab_size] = model.llm_decoder.bias
         new_lm_head.weight[vocab_size:] = 0
+        if use_bias is True:
+            new_lm_head.bias[:vocab_size] = model.llm_decoder.bias
             new_lm_head.bias[vocab_size:] = 0
     model.llm.model.lm_head = new_lm_head
     new_codec_embed = torch.nn.Linear(in_features=feature_size, out_features=pad_vocab_size)
@@ -121,9 +124,12 @@ def export_cosyvoice2_vllm(model, model_path, device):
     del model.llm.model.config.eos_token_id
     model.llm.model.config.vocab_size = pad_vocab_size
     model.llm.model.config.tie_word_embeddings = False
-    model.llm.model.config.use_bias = True
+    model.llm.model.config.use_bias = use_bias
     model.llm.model.save_pretrained(model_path)
+    if use_bias is True:
         os.system('sed -i s@Qwen2ForCausalLM@CosyVoice2ForCausalLM@g {}/config.json'.format(os.path.abspath(model_path)))
+    else:
+        os.system('sed -i s@Qwen2ForCausalLM@Qwen2ForCausalLM@g {}/config.json'.format(os.path.abspath(model_path)))
     model.llm.model.config.vocab_size = tmp_vocab_size
     model.llm.model.config.tie_word_embeddings = tmp_tie_embedding
     model.llm.model.set_input_embeddings(embed_tokens)

example.py

@@ -88,7 +88,7 @@ def cosyvoice3_example():
         torchaudio.save('hotfix_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
 
 def main():
-    # cosyvoice_example()
+    cosyvoice_example()
     cosyvoice2_example()
     cosyvoice3_example()
 

vllm_example.py

@@ -4,20 +4,33 @@ from vllm import ModelRegistry
 from cosyvoice.vllm.cosyvoice2 import CosyVoice2ForCausalLM
 ModelRegistry.register_model("CosyVoice2ForCausalLM", CosyVoice2ForCausalLM)
 
-from cosyvoice.cli.cosyvoice import CosyVoice2
+from cosyvoice.cli.cosyvoice import CosyVoice2, CosyVoice3
-from cosyvoice.utils.file_utils import load_wav
 from cosyvoice.utils.common import set_all_random_seed
 from tqdm import tqdm
 
 
-def main():
+def cosyvoice2_example():
+    """ CosyVoice2 vllm usage
+    """
     cosyvoice = CosyVoice2('pretrained_models/CosyVoice2-0.5B', load_jit=True, load_trt=True, load_vllm=True, fp16=True)
+    for i in tqdm(range(100)):
+        set_all_random_seed(i)
+        for _, _ in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', stream=False)):
+            continue
+
+def cosyvoice3_example():
+    """ CosyVoice3 vllm usage
+    """
+    cosyvoice = CosyVoice3('pretrained_models/CosyVoice3-0.5B', load_jit=True, load_trt=True, load_vllm=True, fp16=True)
     prompt_speech_16k = load_wav('./asset/zero_shot_prompt.wav', 16000)
     for i in tqdm(range(100)):
         set_all_random_seed(i)
-        for _, _ in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=False)):
+        for _, _ in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', stream=False)):
             continue
 
+def main():
+    cosyvoice2_example()
+    cosyvoice3_example()
 
 if __name__ == '__main__':
     main()