[Bugfix] CosyVoice3: wrap ref_text in instruction template (#4644) (#4756)

vllm_omni/model_executor/stage_input_processors/cosyvoice3.py

@@ -21,8 +21,6 @@ from vllm_omni.model_executor.models.cosyvoice3.utils import unpad_prompt_condit
 
 logger = init_logger(__name__)
 
-_COSYVOICE3_SPEECH_TOKEN_SIZE = 6561
-
 
 def _build_prompt_embed_struct(prompt_payload: dict[str, Any]) -> EmbeddingsStruct | None:
     """Wrap prompt_payload's flat speech_token/speech_feat/embedding tensors into EmbeddingsStruct."""
@@ -85,20 +83,6 @@ def _prompt_speech_token_ids(multi_modal_data: dict[str, Any]) -> list[int]:
     return _to_token_id_list(speech_token)
 
 
-def _has_speech_stop_token(output_ids: list[Any]) -> bool:
-    return any(token_id >= _COSYVOICE3_SPEECH_TOKEN_SIZE for token_id in _to_token_id_list(output_ids))
-
-
-def _set_non_stream_prompt_trim(additional_info: dict[str, Any], prompt_speech_len: int) -> None:
-    if prompt_speech_len <= 0:
-        return
-    meta = additional_info.get("meta")
-    if not isinstance(meta, dict):
-        meta = {}
-        additional_info["meta"] = meta
-    meta["talker_prefill_offset"] = prompt_speech_len
-
-
 def _to_cpu_tensor(x: Any) -> torch.Tensor | None:
     if isinstance(x, list):
         if not x:
@@ -154,8 +138,6 @@ def text2flow(
         output_ids = _strip_prompt_prefix(raw_output_ids, prefix_ids)
         output_ids = _strip_prompt_prefix(output_ids, prompt_speech_ids)
         additional_info = dict(multi_modal_data)
-        if _has_speech_stop_token(raw_output_ids):
-            _set_non_stream_prompt_trim(additional_info, len(prompt_speech_ids))
         additional_info.setdefault("ids", {})["prompt"] = prefix_ids
         engine_inputs.append(OmniTokensPrompt(prompt_token_ids=output_ids, additional_information=additional_info))
     return engine_inputs
@@ -389,8 +371,6 @@ def text2flow_token_only(
         prompt_speech_ids = _prompt_speech_token_ids(multi_modal_data)
         output_ids = _strip_prompt_prefix(output_ids, prompt_speech_ids)
         additional_info: dict[str, Any] = dict(multi_modal_data)
-        if _has_speech_stop_token(raw_output_ids):
-            _set_non_stream_prompt_trim(additional_info, len(prompt_speech_ids))
         additional_info.setdefault("ids", {})["prompt"] = prefix_ids
         engine_inputs.append(
             OmniTokensPrompt(