fix: remove repetition_penalty from vLLM services (fixes #2950 CUDA assert)

- repetition_penalty is incompatible with enable_prompt_embeds=True in vLLM
- Removed from both serve_realtime_ws.py and serve_vllm.py
- Added truncate_repetition() post-processing to serve_vllm.py as alternative
- serve_realtime_ws.py already has detect_and_fix_hallucination() for this

Root cause: vLLM's repetition penalty implementation does scatter on prompt
token IDs, but prompt_embeds mode has no token IDs, causing index OOB.

Author: LauraGPT

examples/industrial_data_pretraining/fun_asr_nano/serve_realtime_ws.py

@@ -318,7 +318,6 @@ def decode(self, is_final=False):
                 hotwords=self.asr_kwargs.get("hotwords"),
                 language=self.asr_kwargs.get("language"),
                 max_new_tokens=200,
-                repetition_penalty=1.3,
             )
             text = results[0]["text"] if results else ""
             text = _clean_asr_text(text)
@@ -359,7 +358,6 @@ def _decode_segment(self, seg):
                 hotwords=self.asr_kwargs.get("hotwords"),
                 language=self.asr_kwargs.get("language"),
                 max_new_tokens=512,
-                repetition_penalty=1.3,
             )
             text = results[0]["text"] if results else ""
             text = _clean_asr_text(text)

examples/industrial_data_pretraining/fun_asr_nano/serve_vllm.py

@@ -32,6 +32,20 @@
 logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')
 logger = logging.getLogger(__name__)
 
+def truncate_repetition(text, min_repeat_len=3, max_repeats=3):
+    """Detect and truncate repetitive patterns in ASR output."""
+    if not text or len(text) < 20:
+        return text
+    n = len(text)
+    for length in range(min_repeat_len, min(n // max_repeats, 30)):
+        for start in range(n - length * max_repeats):
+            chunk = text[start:start + length]
+            if text[start:start + length * max_repeats] == chunk * max_repeats:
+                return text[:start + length]
+    return text
+
+
+
 try:
     from fastapi import FastAPI, File, UploadFile, Form, WebSocket, WebSocketDisconnect
     from fastapi.responses import JSONResponse
@@ -107,7 +121,7 @@ def process_audio(audio_data, sr=16000, language=None, hotwords=None,
         return {"text": "", "segments": [], "duration": len(audio_data) / sr}
 
     # vLLM batch ASR
-    gen_kwargs = {"max_new_tokens": 500, "repetition_penalty": 1.3}
+    gen_kwargs = {"max_new_tokens": 500}
     if language:
         gen_kwargs["language"] = language
     if hotwords:
@@ -120,6 +134,7 @@ def process_audio(audio_data, sr=16000, language=None, hotwords=None,
     full_text_parts = []
 
     for i, (r, (start_ms, end_ms)) in enumerate(zip(results, seg_times)):
+        r["text"] = truncate_repetition(r["text"])
         seg_info = {
             "text": r["text"],
             "start": start_ms / 1000,