Freeze the speech report against the commit that fixes the prose

The report is regenerated after 10a39c5 so its stamp names the code it
describes. Speed is quoted as a range in the checklist because transcribe
seconds move with machine load (3.26s then 3.12s on this box) while WER,
denominators and word counts do not move at all.

Numbers: 258.18s of audio, WER 0.242 over 533 reference words, 80 audio atoms
and 563 word timestamps read back from the store, 0 out of order and 0 before
their own segment measured on the stored words.

CHECKLIST.md

@@ -159,8 +159,9 @@
   - 验收：`pytest backend/tests/test_asr.py` 绿（13 个测试）；ASR **实跑**，不是 DEFERRED
   - 实测证据（`bench/reports/ASR.md`，`.venv/bin/python -m bench.asr_check` 生成）：3 段 Video-MME 真视频
     **258.18 秒**音频（容器时长 88.93 / 109.18 / 60.07），对着第三方字幕 WER **0.242**——分母是
-    **533 个参考词**（听到 563 个词），三行各自的参考词数是 201 / 296 / 36。CPU int8 4 线程
-    3.26 秒转完，逐行 63.9× / 82.4× / 112.2× 实时。走产品路径摄入后从库里读回：**80 个 audio atom**、
+    **533 个参考词**（听到 563 个词），三行各自的参考词数是 201 / 296 / 36。CPU int8 4 线程跑完这 258 秒
+    音频：两份已提交报告分别 3.26 秒与 3.12 秒（逐行 63.9–112.2× 与 68.4–111.2× 实时——转写秒数随
+    机器负载浮动，WER 与词数不随负载动）。走产品路径摄入后从库里读回：**80 个 audio atom**、
     **563 个词级时间戳**、0 个时序倒错、0 个早于自己所属片段（总共 133 个 atom）。模型
     `Systran/faster-whisper-tiny`（78.2MB，model.bin sha256 dcb76c6586fc06cb…），远低于 500MB 上限。
   - 可复现性是量出来的：解码参数钉死 `beam_size=1, temperature=(0.0,), condition_on_previous_text=False,
@@ -169,6 +170,12 @@
     直接 `TypeError`）。同一台机器连跑两遍：WER 0.242 → 0.242，转写文本逐字节相同。
   - 字幕不是逐字稿，所以这个 WER **不是任何方向的界**：字幕漏词也会掩盖模型漏词。
     报告把分母写在表里，不写「上界」这种断言。
+  - review 第二轮 FAIL 的 4 条全是数字与措辞，也都改了：docstring 里的 1.7 秒是钉参数前那次运行的残留
+    （现在不写数字，只指向报告；并且不再把容器时长叫「语音时长」，因为 `vad_filter=False`）；
+    CHECKLIST 里的 3.55 秒 / 61.6× 与「31 段语音」同样是旧运行残留（钉死后是 38/39/3 段，加起来
+    正好是库里的 80 个 atom）；报告那句「库里有 80 个 atom、0 个时序倒错」原本把只在模型输出上
+    算过的不变量说成库侧的，现在 harness 对库里存的 words 重算（`out_of_order_in_store` /
+    `before_their_segment_in_store`）。速度只报区间，因为转写秒数跟机器负载走。
   - 偏差与根因：
     - 验收路径 `tests/test_audio_ingest.py` 不存在，实际是 `backend/tests/test_asr.py`。
     - `Modality` 只加 `audio`，没加 `transcript`：一份转写就是带时间戳的 audio atom，

bench/reports/ASR.md

@@ -1,21 +1,21 @@
-# ASR: what the speech reader hears (commit 709104e80)
+# ASR: what the speech reader hears (commit 10a39c512)
 
-- generated 2026-10-08T02:24:47+00:00
+- generated 2026-10-08T02:37:10+00:00
 - model: Systran/faster-whisper-tiny (cpu int8, 4 threads), model.bin sha256 dcb76c6586fc06cb…
 - decode: word_timestamps=True, beam_size=1, temperature=(0.0,), condition_on_previous_text=False, vad_filter=False
 - reference: Video-MME community subtitles: third-party captions, not verbatim transcripts
-- machine: {'system': 'Linux', 'release': '6.18.40.1-microsoft-standard-WSL2', 'cpus': 32, 'threads_per_query': 4, 'load_average_1_5_15': [9.65, 2.54, 0.88]}
+- machine: {'system': 'Linux', 'release': '6.18.40.1-microsoft-standard-WSL2', 'cpus': 32, 'threads_per_query': 4, 'load_average_1_5_15': [7.73, 2.66, 1.58]}
 
 | video | audio s | transcribe s | ×realtime | reference words | words | WER | mean word confidence |
 | --- | --- | --- | --- | --- | --- | --- | --- |
-| 0IdYJGBmguM | 88.93 | 1.39 | 63.9 | 201 | 219 | 0.229 | 0.861 |
-| 1q-5IIyZL20 | 109.18 | 1.33 | 82.4 | 296 | 313 | 0.253 | 0.821 |
-| 1tidNDIpKSY | 60.07 | 0.54 | 112.2 | 36 | 31 | 0.222 | 0.852 |
+| 0IdYJGBmguM | 88.93 | 1.3 | 68.4 | 201 | 219 | 0.229 | 0.861 |
+| 1q-5IIyZL20 | 109.18 | 1.28 | 85.4 | 296 | 313 | 0.253 | 0.821 |
+| 1tidNDIpKSY | 60.07 | 0.54 | 111.2 | 36 | 31 | 0.222 | 0.852 |
 
-Over 3 videos and 258.18 seconds of audio: word error rate **0.242** over 533 reference words (563 words heard), transcribed in 3.26 seconds.
+Over 3 videos and 258.18 seconds of audio: word error rate **0.242** over 533 reference words (563 words heard), transcribed in 3.12 seconds.
 
 Repeating the pass on the same machine gave WER 0.242 then 0.242; the transcripts were byte-identical. Decoding is greedy with no retry ladder, so a rerun is the same answer, not a new draw.
 
-Ingested through the product path, the store holds 80 audio atoms from 3 sources carrying 563 word timestamps (0 out of order, 0 before their own segment) out of 133 atoms in total.
+Ingested through the product path, the store holds 80 audio atoms from 3 sources carrying 563 word timestamps (0 out of order, 0 before their own segment) out of 133 atoms in total, checked on the words as stored rather than on the model's answer.
 
 The captions are not a transcript, so this WER is not a bound in either direction: a caption that drops words also hides words the model dropped. The third row has only 36 reference words, where one word moves its rate by several points — read the denominators, not the decimals.

bench/reports/asr.json

@@ -1,6 +1,6 @@
 {
-  "generated_utc": "2026-10-08T02:24:47+00:00",
-  "commit": "709104e80915e43a1095a270a87f09cbb611d51b",
+  "generated_utc": "2026-10-08T02:37:10+00:00",
+  "commit": "10a39c5125ebaf73e0ff7174b3af7af4c32bcf05",
   "model": {
     "id": "Systran/faster-whisper-tiny",
     "sha256_model_bin": "dcb76c6586fc06cbdac6dd21f14cfd129cc4cdd9dce19bf4ffa62e59cbe6e6d1",
@@ -21,9 +21,9 @@
     "cpus": 32,
     "threads_per_query": 4,
     "load_average_1_5_15": [
-      9.65,
-      2.54,
-      0.88
+      7.73,
+      2.66,
+      1.58
     ]
   },
   "passes": {
@@ -54,7 +54,7 @@
   "totals": {
     "videos": 3,
     "audio_seconds": 258.18,
-    "transcribe_seconds": 3.26,
+    "transcribe_seconds": 3.12,
     "word_error_rate": 0.242,
     "reference_words": 533,
     "hypothesis_words": 563,
@@ -64,14 +64,16 @@
     "atoms_all": 133,
     "speech_atoms": 80,
     "word_timestamps": 563,
-    "sources": 3
+    "sources": 3,
+    "out_of_order_in_store": 0,
+    "before_their_segment_in_store": 0
   },
   "videos": [
     {
       "video": "0IdYJGBmguM",
       "seconds": 88.93,
-      "transcribe_seconds": 1.39,
-      "real_time_factor": 63.9,
+      "transcribe_seconds": 1.3,
+      "real_time_factor": 68.4,
       "segments": 38,
       "reference_words": 201,
       "hypothesis_words": 219,
@@ -85,8 +87,8 @@
     {
       "video": "1q-5IIyZL20",
       "seconds": 109.18,
-      "transcribe_seconds": 1.33,
-      "real_time_factor": 82.4,
+      "transcribe_seconds": 1.28,
+      "real_time_factor": 85.4,
       "segments": 39,
       "reference_words": 296,
       "hypothesis_words": 313,
@@ -101,7 +103,7 @@
       "video": "1tidNDIpKSY",
       "seconds": 60.07,
       "transcribe_seconds": 0.54,
-      "real_time_factor": 112.2,
+      "real_time_factor": 111.2,
       "segments": 3,
       "reference_words": 36,
       "hypothesis_words": 31,