Point this box at the workstation transcriber (V-486)

The block is inert until the code in PR #208 lands, and deleting it sends
every utterance back to mavsttd, which is what the box does today.

Port 8081 and not mavgpud's 8080, because whisper.cpp cannot load
CrisperWhisper 2.0 at all and it runs under transformers as its own service.
The token comes from deploy/telegram.env like every other secret here. It is
what stops anything on the LAN posting audio to that port.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013ptwopxyo3Z2kwFckHkLvN

deploy/mavend.json

@@ -78,10 +78,30 @@
     "Addressed by LAN address, not container name: mavgpud runs on another",
     "machine and there is no shared docker network to name it on."
   ],
+  "//workstation.stt": [
+    "CrisperWhisper 2.0 turbo on the same machine, a second service on port",
+    "8081 and not a second endpoint on mavgpud. whisper.cpp cannot load CW2 at",
+    "all: it derives its language count from the vocabulary size, and CW2's",
+    "51897 tokens shift seven special token ids. So it runs under transformers",
+    "there and mavsttd stays whisper.cpp here.",
+    "Worth the second service: CW2 turbo scores 10.4% WER in Russian against",
+    "27.5% for the ggml-small.bin mavsttd loads, measured on 200 Golos clips",
+    "in docs/evals/2026-08-09-crisperwhisper2-russian-wer.md.",
+    "Deleting this block sends every utterance to mavsttd, which is what the",
+    "box did before it existed. A worse transcript is still a turn, so the",
+    "fallback is silent and Kami is never told which machine heard him.",
+    "The token is what stops anything on the LAN posting audio to that port."
+  ],
   "workstation": {
     "url": "http://192.168.1.105:8080",
     "probe": "15s",
-    "timeout": "90s"
+    "timeout": "90s",
+    "stt": {
+      "url": "http://192.168.1.105:8081/transcribe",
+      "token": "${MAVEN_STT_TOKEN}",
+      "probe": "15s",
+      "timeout": "10s"
+    }
   },
 
   "//search": [

docs/offload.md

@@ -1,6 +1,6 @@
 # Offloading model work to the workstation
 
-*Last verified: 2026-08-05 @ b789676. Living doc: correct it in place, do not append.*
+*Last verified: 2026-08-09 @ 50c6637. Living doc: correct it in place, do not append.*
 
 Owner's call, 2026-08-02. Vikunja #483 is the umbrella. Tasks #484 to #487 are the
 work, and this file holds the shape and the rules all four must obey.
@@ -149,6 +149,25 @@ flips. It is wired anyway: `PhraseReminder` is on the same transport and is on.
 Then the embedder above, **whisper.cpp** in `mavsttd`, and **piper** in `mavttsd`.
 `mavwaked` uses no model at all: an energy-threshold VAD over 30ms frames.
 
+Speech-to-text is wired as of 09-08-2026, and it takes only the silent half of the
+rule. A worse transcript is still a turn, so there is nothing to name a gap about
+and `stt.Pair` has no `TranscribeRemote`. `sttSeam` in `cmd/mavend/voicewire.go`
+builds it, beside `modelSeam` and at the same place in `wireVoice`, so the voice
+path and the meeting recorder still share one transcriber.
+
+The remote is not a second endpoint on mavgpud. whisper.cpp cannot load
+CrisperWhisper 2.0 at all. It derives its language count from the vocabulary
+size, and CW2's 51897 tokens shift seven special token ids. So CW2 runs under
+transformers as its own service on port 8081, and `stt.HTTPTranscriber` is the
+second transport for the same seam. It posts raw PCM with the format in headers.
+It carries a bearer token, because audio is the most sensitive thing that
+crosses here.
+
+The margin is the reason: CW2 turbo scores 10.4% WER in Russian against 27.5% for
+the `ggml-small.bin` mavsttd loads, over 200 Golos clips
+(`docs/evals/2026-08-09-crisperwhisper2-russian-wer.md`). Text-to-speech has not
+moved and piper on homesrv is still the only synthesizer.
+
 Speech-to-text stays two stages when it moves. One call carrying both a clip and the router
 prompt was measured on 05-08-2026. It scores 54.2% intent-only against 84.7% for whisper on
 homesrv, on the same 72 cases. The model transcribes clips it then routes wrong, so a long
@@ -173,8 +192,9 @@ cleaner transcripts, not accuracy. See `docs/evals/2026-08-05-audio-in-routing.m
    which fixes what the 1.7B gets wrong: world knowledge, and the persona the CPT
    targets. The degradation path is already written and measured, since the
    classifier scores 68.8% full accuracy at p50 16.6µs on its own.
-3. **Speech-to-text and text-to-speech** (#486). They gain a real margin, but on
-   quality alone, and both already work.
+3. **Speech-to-text and text-to-speech** (#486). Speech-to-text is wired, see
+   above. Text-to-speech is not, and piper is good enough that nothing argues
+   for moving it yet.
 4. **The wake word** (#487). Independent of all of the above.
 
 ## Assumptions