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/datasets/base.py

@@ -0,0 +1,34 @@
+"""数据集适配器基类与音频物料化工具。"""
+
+from __future__ import annotations
+
+import os
+from collections.abc import Iterator
+
+import numpy as np
+import soundfile as sf
+
+from ..manifest import Sample
+
+TARGET_SR = 16000
+
+
+class Dataset:
+    name: str = "base"
+
+    def samples(self, limit: int | None = None) -> Iterator[Sample]:
+        raise NotImplementedError
+
+
+def write_wav_16k(array: np.ndarray, sr: int, out_path: str) -> None:
+    """把（可能任意采样率的）单/多声道数组写成 16k/mono/16bit wav。"""
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+    data = np.asarray(array, dtype=np.float32)
+    if data.ndim > 1:
+        data = data.mean(axis=1)
+    if sr != TARGET_SR:
+        import soxr
+
+        data = soxr.resample(data, sr, TARGET_SR)
+    data = np.clip(data, -1.0, 1.0)
+    sf.write(out_path, (data * 32767.0).astype(np.int16), TARGET_SR, subtype="PCM_16")