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/whisper.py

@@ -0,0 +1,70 @@
+"""本地 faster-whisper 引擎（CTranslate2，CPU-first）。多语，对中英混说有基础能力。
+
+依赖：pip install '.[whisper]'
+"""
+
+from __future__ import annotations
+
+import time
+
+from ..metrics.resource import dir_size_mb
+from .base import Engine, Transcript
+
+
+class WhisperEngine(Engine):
+    kind = "offline"
+    is_local = True
+
+    def __init__(
+        self,
+        name: str = "whisper-small",
+        model_size: str = "small",
+        device: str = "cpu",
+        compute_type: str = "int8",
+        model_dir: str | None = None,
+    ):
+        self.name = name
+        self.model_size = model_size
+        self.device = device
+        self.compute_type = compute_type
+        self.model_dir = model_dir
+        self.model = None
+        self._weights_path: str | None = None
+
+    def load(self) -> None:
+        from faster_whisper import WhisperModel
+
+        self.model = WhisperModel(
+            self.model_size,
+            device=self.device,
+            compute_type=self.compute_type,
+            download_root=self.model_dir,
+        )
+        self._resolve_weights_path()
+
+    def _resolve_weights_path(self) -> None:
+        # 解析磁盘权重路径用于报模型大小（best-effort）
+        try:
+            from huggingface_hub import snapshot_download
+
+            repo = f"Systran/faster-whisper-{self.model_size}"
+            self._weights_path = snapshot_download(repo, local_files_only=True, cache_dir=self.model_dir)
+        except Exception:
+            self._weights_path = self.model_dir
+
+    def transcribe(self, audio_path: str) -> Transcript:
+        if self.model is None:
+            self.load()
+        t0 = time.monotonic()
+        # language=None → 自动检测；中英混说交给模型自身
+        segments, info = self.model.transcribe(audio_path, language=None, beam_size=5)
+        text = "".join(seg.text for seg in segments)  # 生成器在此 join 时才真正解码
+        proc = time.monotonic() - t0
+        return Transcript(text=text.strip(), audio_sec=float(info.duration), 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