fix(ai_auto_response): avoid forced split of single short replies

plugins/ai_auto_response/core/reply_formatter.py

@@ -12,11 +12,11 @@ def finalize_reply(response: str, reply_mode: str) -> List[str]:
     text = text.replace("\n", " ").strip()
 
     if reply_mode == "social_short":
-        return split_reply_chunks(text, sentence_limit=2, char_limit=24, chunk_limit=2)
+        return split_reply_chunks(text, sentence_limit=2, char_limit=24, chunk_limit=2, allow_clip_split=False)
     if reply_mode == "qa_fast":
-        return split_reply_chunks(text, sentence_limit=2, char_limit=32, chunk_limit=2)
+        return split_reply_chunks(text, sentence_limit=2, char_limit=32, chunk_limit=2, allow_clip_split=False)
     if reply_mode == "qa_with_context":
-        return split_reply_chunks(text, sentence_limit=2, char_limit=40, chunk_limit=2)
+        return split_reply_chunks(text, sentence_limit=2, char_limit=40, chunk_limit=2, allow_clip_split=False)
     return [take_first_sentence(text, 28).strip()]
 
 
@@ -45,14 +45,22 @@ def take_first_sentence(text: str, limit: int) -> str:
     return smart_clip(first, limit)
 
 
-def split_reply_chunks(text: str, sentence_limit: int, char_limit: int, chunk_limit: int) -> List[str]:
+def split_reply_chunks(
+    text: str,
+    sentence_limit: int,
+    char_limit: int,
+    chunk_limit: int,
+    allow_clip_split: bool = True,
+) -> List[str]:
     parts = [item.strip() for item in re.split(r"(?<=[。！？!?；;])", text) if item.strip()]
     if not parts:
         short = text.strip()
         clipped = smart_clip(short, char_limit)
-        remainder = short[len(clipped):].strip("，,、；;：: ")
         if not short:
             return []
+        if not allow_clip_split:
+            return [clipped] if clipped else []
+        remainder = short[len(clipped):].strip("，,、；;：: ")
         return [item for item in [clipped, smart_clip(remainder, char_limit)] if item][:chunk_limit]
 
     chunks: List[str] = []
@@ -67,6 +75,8 @@ def split_reply_chunks(text: str, sentence_limit: int, char_limit: int, chunk_li
                 clipped = current[:char_limit].rstrip("，,、；;：: ").strip()
             if clipped:
                 chunks.append(clipped)
+            if not allow_clip_split:
+                break
             current = current[len(clipped):].strip("，,、；;：: ")
     return chunks[:chunk_limit] or [smart_clip(text, char_limit)]
 

test/voxcpm_test.py

@@ -0,0 +1,67 @@
+from voxcpm import VoxCPM
+import soundfile as sf
+import torch
+from pathlib import Path
+import sys
+
+BASE_DIR = Path(__file__).resolve().parent
+DEVICE = "cuda"
+PROMPT_WAV = BASE_DIR / "lzl.wav"
+CACHE_PATH = BASE_DIR / "lzl_prompt_cache.pt"
+VOICE_DESIGN_PATH = BASE_DIR / "voice_design.wav"
+HIFI_CLONE_PATH = BASE_DIR / "hifi_clone.wav"
+PROMPT_TEXT = "亲爱的，今天的你要出发，挣钱喽，都没有系好安全带。 乖乖仔，我要到了，奇怪还没有分开就开始想你了，注意一下，闯红灯拍照，我可不喜欢明知孤犯的小坏蛋，安全带系一好，我们这边要出发喽。 小坏蛋，前方有限速拍照，姐姐给你盯着呢，车速太快了啊，慢一点慢一点降下来，不要让我害怕好吗？ 过最堵的路段，千万不要着急，姐姐会一直陪着你。 今日导航就先到这里了，哥哥注意安全停车哦。"
+TARGET_TEXT = "慢慢来吧，额度还在就好～"
+
+if DEVICE != "cuda":
+    raise RuntimeError(f"Unsupported device: {DEVICE}. This script only supports CUDA.")
+
+if not torch.cuda.is_available():
+    raise RuntimeError(
+        "This script requires CUDA. "
+        f"Current python: {sys.executable}, torch: {torch.__version__}. "
+        "Please run it with a CUDA-enabled Python environment."
+    )
+
+model = VoxCPM.from_pretrained("openbmb/VoxCPM2", load_denoiser=False)
+if model.tts_model.device != DEVICE:
+    raise RuntimeError(f"Expected VoxCPM to run on {DEVICE}, got {model.tts_model.device}")
+print(f"VoxCPM loaded on {model.tts_model.device} with torch {torch.__version__}")
+
+wav = model.generate(
+    text="(A young woman, gentle and sweet voice)Hello, welcome to VoxCPM!",
+    cfg_value=2.0,
+    inference_timesteps=10,
+)
+sf.write(str(VOICE_DESIGN_PATH), wav, model.tts_model.sample_rate)
+print(f"Saved voice design to {VOICE_DESIGN_PATH}")
+
+# Build and persist the prompt cache so later runs can skip prompt encoding.
+if CACHE_PATH.exists():
+    prompt_cache = torch.load(CACHE_PATH, map_location="cpu")
+    print(f"Loaded prompt cache from {CACHE_PATH}")
+else:
+    prompt_cache = model.tts_model.build_prompt_cache(
+        prompt_wav_path=str(PROMPT_WAV),
+        prompt_text=PROMPT_TEXT,
+        reference_wav_path=str(PROMPT_WAV),
+    )
+    torch.save(prompt_cache, CACHE_PATH)
+    print(f"Built and saved prompt cache to {CACHE_PATH}")
+
+cache_devices = {
+    key: str(value.device)
+    for key, value in prompt_cache.items()
+    if isinstance(value, torch.Tensor)
+}
+print(f"Prompt cache tensor devices: {cache_devices}")
+
+wav, _, _ = model.tts_model.generate_with_prompt_cache(
+    target_text=TARGET_TEXT,
+    prompt_cache=prompt_cache,
+    cfg_value=2.0,
+    inference_timesteps=10,
+)
+wav = wav.detach().cpu().float().squeeze(0).numpy()
+sf.write(str(HIFI_CLONE_PATH), wav, model.tts_model.sample_rate)
+print(f"Saved cloned audio to {HIFI_CLONE_PATH}")