feat(eval): ASR 模型评估框架

横向对比云端 gummy 与本地开源模型(faster-whisper/SenseVoice/Paraformer)，
重点覆盖中英混说，产出准确率(CER/WER/MER)/速度(延迟/RTF)/资源(cpu/mem/模型大小)
对比报告。公共集(ASCEND/AISHELL/LibriSpeech)统一走 HF 适配器 + 自定义 JSONL manifest。
gummy 引擎对照 server/internal/asr/gummy.go 协议移植。

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

eval/asr_eval/engines/funasr.py

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+"""本地 FunASR 引擎：SenseVoice-Small（多语，中英混说强）与 Paraformer-zh（纯中 SOTA）。
+
+依赖：pip install '.[funasr]'
+两者都走 funasr.AutoModel，离线整段识别。SenseVoice 输出带 <|zh|><|EMO|> 等富标签，需剥离。
+"""
+
+from __future__ import annotations
+
+import os
+import re
+import time
+
+from ..audio import duration_sec
+from ..metrics.resource import dir_size_mb
+from .base import Engine, Transcript
+
+_TAG = re.compile(r"<\|[^|]*\|>")
+
+
+def _strip_tags(text: str) -> str:
+    return _TAG.sub("", text).strip()
+
+
+class _FunASRBase(Engine):
+    kind = "offline"
+    is_local = True
+    repo: str = ""
+
+    def __init__(self, name: str, device: str = "cpu", model_dir: str | None = None):
+        self.name = name
+        self.device = device
+        self.model_dir = model_dir
+        self.model = None
+        self._weights_path: str | None = None
+
+    def load(self) -> None:
+        from funasr import AutoModel
+
+        kwargs = {"model": self.repo, "disable_update": True, "device": self.device}
+        if self.model_dir:
+            kwargs["cache_dir"] = self.model_dir
+        self.model = AutoModel(**kwargs)
+        self._resolve_weights_path()
+
+    def _resolve_weights_path(self) -> None:
+        # funasr 默认从 modelscope 下载；尝试常见属性与缓存目录（best-effort）
+        for attr in ("model_path", "model_pth", "kwargs"):
+            val = getattr(self.model, attr, None)
+            if isinstance(val, str) and os.path.exists(val):
+                self._weights_path = val
+                return
+            if isinstance(val, dict) and isinstance(val.get("model_path"), str):
+                self._weights_path = val["model_path"]
+                return
+        self._weights_path = self.model_dir
+
+    def transcribe(self, audio_path: str) -> Transcript:
+        if self.model is None:
+            self.load()
+        t0 = time.monotonic()
+        res = self.model.generate(input=audio_path)
+        proc = time.monotonic() - t0
+        text = ""
+        if res and isinstance(res, list) and res[0].get("text"):
+            text = _strip_tags(res[0]["text"])
+        return Transcript(text=text, audio_sec=duration_sec(audio_path), proc_sec=proc)
+
+    def model_size_mb(self) -> float | None:
+        size = dir_size_mb(self._weights_path) if self._weights_path else 0.0
+        return size or None
+
+    def unload(self) -> None:
+        self.model = None
+
+
+class SenseVoiceEngine(_FunASRBase):
+    repo = "iic/SenseVoiceSmall"
+
+    def __init__(self, name: str = "sensevoice", device: str = "cpu", model_dir: str | None = None):
+        super().__init__(name=name, device=device, model_dir=model_dir)
+
+
+class ParaformerEngine(_FunASRBase):
+    repo = "paraformer-zh"
+
+    def __init__(self, name: str = "paraformer-zh", device: str = "cpu", model_dir: str | None = None):
+        super().__init__(name=name, device=device, model_dir=model_dir)