Prepare bounded speech diagnostics and document mixed routing risks

tools/speech-eval/README.md

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+# Actual local speech diagnostic
+
+Research harness only; transcription is not implemented in searchi yet.
+See [the integration proposal](../../docs/SPEECH-ALIGNMENT.md). No actual results
+are checked in until genuine runs complete. Google remains the semantic model.
+
+`run.py` requires separately provisioned, pinned whisper.cpp v1.9.5 CLI, multilingual
+base/small model and existing verified FFmpeg/FFprobe. It never builds/downloads.
+Record the CLI build SHA beforehand and pass that hash; a matching hash alone does
+not establish upstream provenance or dependency closure. Verify the build recipe
+and dynamic libraries separately. CPU slot coordination is required before runs.
+
+```powershell
+$env:SEARCHI_FORCE_CPU = '1'
+python tools/speech-eval/run.py --whisper PATH_TO_PINNED_CLI --whisper-sha256 RECORDED_BUILD_SHA --ffmpeg PATH_TO_VERIFIED_FFMPEG --ffprobe PATH_TO_VERIFIED_FFPROBE --model PATH_TO_VERIFIED_BASE --candidate base --scratch-parent .searchi/speech-scratch --output .searchi/qa/speech-base.json
+python -m unittest discover -s tools/speech-eval -p 'test_*.py'
+```
+
+The CLI receives pinned-source-confirmed `--no-gpu`, one thread and one processor.
+Source and model SHA/size are checked; NASA, both real instrumental music assets
+and both non-speech effects come from unchanged media-v1. Each source is limited
+to ten minutes for this proof, decoded into 30-second 16kHz mono signed-16 WAV
+windows with bounded owned scratch. CLI processes restart per window: cost includes
+model load, not retained-session throughput. This deliberately simple experiment
+does not implement overlap/dedup or production budgets. Segment JSON offsets are
+already milliseconds and get the window offset added exactly once.
+
+Each completed window writes an incomplete checkpoint; the final report marks
+complete. Errors retain bounded owned logs outside temporary scratch, which cleans
+decoded WAV/JSON on exit. Cancellation terminates only owned subprocess trees.
+Limits are supervisory checks, not OS resource sandbox guarantees. Recognized text
+is private local diagnostic data; never commit raw logs/transcripts by default.
+Music/non-speech output requires actual audio review: nonempty recognized text is
+not automatically proof of hallucination and empty output is not universal safety.
+
+`score.py` consumes **externally ranked genuine Google transcript results**, using
+the existing eight frozen NASA intervals unchanged. Input has `fixture_sha256`,
+`asr_report_sha256`, `google_worker_sha256`, and `rankings` mapping query ids to
+ordered `{asset,start_ms,end_ms}` records. It reports top1 overlap and strict start
+point hits; it never chooses a segment by looking at the answer intervals.
+Hash fields must be checked against retained artifacts by the evaluator; they are
+provenance declarations, not a replacement for inspecting those artifacts.
+
+Unit tests use explicitly synthetic structural data to verify bounds/units and
+scoring behavior. They provide no speech-recognition or search quality evidence.
+The first proof must manually inspect NASA recognition against independently
+published captions, then run real Google ranking. Eight paired queries from one
+English recording do not validate German speech or general transcription quality.
+
+Pinned CLI flag/output reference:
+[upstream cli.cpp](https://github.com/ggml-org/whisper.cpp/blob/d1be6fde11ac6e0407606b4e42fe72d34add8037/examples/cli/cli.cpp).