[fix] 修复了sensevoice STT重复下载模型文件和缺失依赖项的问题

astrbot/core/provider/sources/sensevoice_selfhosted_source.py

@@ -1,78 +1,173 @@
 """Author: diudiu62
 Date: 2025-02-24 18:04:18
-LastEditTime: 2025-02-25 14:06:30
+LastEditTime: 2026-08-31 
+LastEdit / Blame: xiewoc
 """
 
 import asyncio
 import re
-from typing import cast
+import os
+import shutil
+from typing import Optional, TYPE_CHECKING
 
-from funasr_onnx import SenseVoiceSmall
-from funasr_onnx.utils.postprocess_utils import rich_transcription_postprocess
-
-from astrbot.core import logger
+from astrbot.core.utils.pip_installer import PipInstaller
 from astrbot.core.utils.media_utils import MediaResolver
+from astrbot.core.utils.astrbot_path import get_astrbot_data_path
+from astrbot.core import logger
 
 from ..entities import ProviderType
 from ..provider import STTProvider
 from ..register import register_provider_adapter
 
+# 仅在类型检查时导入，避免运行时循环依赖或导入失败
+if TYPE_CHECKING:
+    from funasr_onnx import SenseVoiceSmall
+
+_REQUIRED_MODULES = [
+    "funasr", "funasr_onnx", "torch",  # "torchaudio", <- we don't need this, use FFmpeg instead
+    "onnxruntime", "modelscope", "onnxscript"
+]
+
+# 模块级缓存，避免重复安装/导入
+_sense_voice_cls = None
+_postprocess_fn = None
+_snapshot_download_fn = None
+
+
+def _check_ffmpeg() -> None:
+    """检测系统是否安装了 FFmpeg，未安装则记录错误并抛出异常阻断流程。"""
+    if shutil.which("ffmpeg") is None:
+        msg = (
+            "未检测到 FFmpeg！SenseVoice STT 依赖 FFmpeg 进行音频转码。"
+            "请安装 FFmpeg 并将其添加到系统 PATH 环境变量中。"
+            "参考: https://ffmpeg.org/download.html"
+        )
+        logger.error(msg)
+        raise RuntimeError(msg)
+
+
+async def _install_dependencies():
+    """异步安装依赖库"""
+    pip = PipInstaller(pip_install_arg="")
+    for item in _REQUIRED_MODULES:
+        try:
+            await pip.install(item)
+        except Exception as e:
+            logger.error(f"安装依赖 {item} 失败: {e}")
+            raise
+
+
+def _load_sense_voice_modules():
+    """延迟加载 SenseVoice 相关模块，支持自动安装重试"""
+    global _sense_voice_cls, _postprocess_fn, _snapshot_download_fn
+
+    if _sense_voice_cls is not None:
+        return _sense_voice_cls, _postprocess_fn, _snapshot_download_fn
+
+    try:
+        from modelscope import snapshot_download
+        from funasr_onnx import SenseVoiceSmall
+        from funasr_onnx.utils.postprocess_utils import rich_transcription_postprocess
+
+        _sense_voice_cls = SenseVoiceSmall
+        _postprocess_fn = rich_transcription_postprocess
+        _snapshot_download_fn = snapshot_download
+        return _sense_voice_cls, _postprocess_fn, _snapshot_download_fn
+
+    except ImportError:
+        logger.info("SenseVoice 依赖未安装，正在尝试自动安装...")
+        try:
+            # 注意：在已有事件循环中应使用 run_coroutine_threadsafe 或 nest_asyncio
+            # 此处保留原逻辑但增加提示，实际部署建议改为插件初始化钩子
+            loop = asyncio.get_event_loop()
+            if loop.is_running():
+                logger.warning("检测到运行中的事件循环，自动安装可能失败。建议手动安装依赖。")
+            asyncio.run(_install_dependencies())
+        except Exception as e:
+            logger.error(f"自动安装依赖失败: {e}")
+            raise ImportError(
+                "SenseVoice 依赖安装失败，请手动执行: pip install " + " ".join(_REQUIRED_MODULES)
+            ) from e
+
+        # 重试导入
+        from modelscope import snapshot_download
+        from funasr_onnx import SenseVoiceSmall
+        from funasr_onnx.utils.postprocess_utils import rich_transcription_postprocess
+
+        _sense_voice_cls = SenseVoiceSmall
+        _postprocess_fn = rich_transcription_postprocess
+        _snapshot_download_fn = snapshot_download
+        return _sense_voice_cls, _postprocess_fn, _snapshot_download_fn
+
 
 @register_provider_adapter(
     "sensevoice_stt_selfhost",
-    "SenseVoice 自托管语音识别 模型部署",
+    "SenseVoice 自托管语音识别模型部署",
     provider_type=ProviderType.SPEECH_TO_TEXT,
 )
 class ProviderSenseVoiceSTTSelfHost(STTProvider):
-    def __init__(
-        self,
-        provider_config: dict,
-        provider_settings: dict,
-    ) -> None:
+    def __init__(self, provider_config: dict, provider_settings: dict) -> None:
         super().__init__(provider_config, provider_settings)
         self.set_model(provider_config["stt_model"])
-        self.model = None
-        self.is_emotion = provider_config.get("is_emotion", False)
+
+        self.model: Optional["SenseVoiceSmall"] = None
+        self.is_emotion: bool = provider_config.get("is_emotion", False)
+        self.model_path: str = os.path.join(get_astrbot_data_path(), "SenseVoiceSmall")
 
     async def initialize(self) -> None:
-        logger.info("下载或者加载 SenseVoice 模型中，这可能需要一些时间 ...")
+        # ✅ 优先检测 FFmpeg，缺失时快速失败，避免浪费模型下载/加载时间
+        _check_ffmpeg()
+
+        logger.info("正在下载或加载 SenseVoice 模型，首次可能需要较长时间...")
+
+        SenseVoiceSmall, _, snapshot_download = _load_sense_voice_modules()
+
+        # 模型下载（同步操作放入线程池）
+        if not os.path.exists(os.path.join(self.model_path, "configuration.json")):
+            loop = asyncio.get_running_loop()
+            await loop.run_in_executor(
+                None,
+                lambda: snapshot_download("iic/SenseVoiceSmall", local_dir=self.model_path),
+            )
 
-        # 将模型加载放到线程池中执行
+        # 模型加载（CPU/GPU 密集型操作放入线程池）
         self.model = await asyncio.get_running_loop().run_in_executor(
             None,
-            lambda: SenseVoiceSmall(self.model_name, quantize=True, batch_size=16),
+            lambda: SenseVoiceSmall(model_dir=self.model_path, quantize=True, batch_size=16),
         )
-
         logger.info("SenseVoice 模型加载完成。")
 
     async def get_text(self, audio_url: str) -> str:
+        if self.model is None:
+            raise RuntimeError("SenseVoice 模型未初始化，请先调用 initialize()")
+
+        # 局部变量绑定，确保类型收窄且避免 lambda 中的属性访问问题
+        model = self.model
+        _, postprocess, _ = _load_sense_voice_modules()
+
         try:
-            # 使用 run_in_executor 来调用模型进行识别
             loop = asyncio.get_running_loop()
+
             async with MediaResolver(
-                audio_url,
-                media_type="audio",
-                default_suffix=".wav",
+                audio_url, media_type="audio", default_suffix=".wav"
             ).as_path(target_format="wav") as audio:
                 res = await loop.run_in_executor(
-                    None,  # 使用默认的线程池
-                    lambda: cast(SenseVoiceSmall, self.model)(
-                        str(audio.path), language="auto", use_itn=True
-                    ),
+                    None,
+                    lambda: model(str(audio.path), language="auto", use_itn=True),
                 )
 
-            # res = self.model(audio_url, language="auto", use_itn=True)
-            logger.debug(f"SenseVoice识别到的文案：{res}")
-            text = rich_transcription_postprocess(res[0])
+            logger.debug(f"SenseVoice 原始识别结果: {res}")
+            text = postprocess(res[0])
+
             if self.is_emotion:
-                # 提取第二个匹配的值
                 matches = re.findall(r"<\|([^|]+)\|>", res[0])
                 if len(matches) >= 2:
-                    emotion = matches[1]
-                    text = f"(当前的情绪：{emotion}) {text}"
+                    text = f"(当前的情绪：{matches[1]}) {text}"
                 else:
-                    logger.warning("未能提取到情绪信息")
+                    logger.warning("未能从识别结果中提取情绪标签")
+
             return text
+
         except Exception as e:
-            logger.error(f"处理音频文件时出错: {e}")
-            raise
+            logger.error(f"SenseVoice 语音识别失败: {e}", exc_info=True)
+            raise