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/audio.py

@@ -0,0 +1,42 @@
+"""音频加载与重采样工具：统一到 16k/mono，供 gummy(pcm16) 与本地引擎(float) 使用。"""
+
+from __future__ import annotations
+
+import numpy as np
+import soundfile as sf
+
+TARGET_SR = 16000
+
+
+def _resample(data: np.ndarray, sr: int, target_sr: int) -> np.ndarray:
+    if sr == target_sr:
+        return data
+    import soxr
+
+    return soxr.resample(data, sr, target_sr)
+
+
+def load_pcm16(path: str, target_sr: int = TARGET_SR) -> tuple[np.ndarray, int]:
+    """读为 int16 单声道 PCM（gummy 推流用）。"""
+    data, sr = sf.read(path, dtype="float32", always_2d=False)
+    if data.ndim > 1:
+        data = data.mean(axis=1)
+    data = _resample(data, sr, target_sr)
+    # float32(-1,1) -> int16
+    pcm = np.clip(data, -1.0, 1.0)
+    pcm = (pcm * 32767.0).astype(np.int16)
+    return pcm, target_sr
+
+
+def load_float(path: str, target_sr: int = TARGET_SR) -> tuple[np.ndarray, int]:
+    """读为 float32 单声道（本地引擎用）。"""
+    data, sr = sf.read(path, dtype="float32", always_2d=False)
+    if data.ndim > 1:
+        data = data.mean(axis=1)
+    data = _resample(data, sr, target_sr)
+    return data.astype(np.float32), target_sr
+
+
+def duration_sec(path: str) -> float:
+    info = sf.info(path)
+    return info.frames / info.samplerate