[index-tts-vllm] VLLM Generation Efficiency Enhancement

Thanks for bring up this vllm implements, the current implementation is generating excessive tokens, negating potential speed gains. The primary cause is the reliance on `max_tokens` instead of proper stopping via `stop_token_ids`, and inefficient post-processing. ### 1\. Model Configuration Fix: `model_vllm_v2.py` Ensure the `stop_token_ids` are correctly passed to **guarantee immediate stopping** by the VLLM engine. ```python # model_vllm_v2.py: def _create_sampling_params(self): """ Creates SamplingParams, ensuring VLLM stops at the stop token ID to prevent generation up to max_tokens. """ return SamplingParams( temperature=1.0, top_p=0.8, top_k=30, repetition_penalty=10.0, # VLLM now stops at the token ID, making this a functional upper bound, # not the typical stopping point. max_tokens=2048, # Sufficient for most cases, will stop at stop_mel_token, and 768 isn't enough for english stop_token