Add rage table-flip state; wire gateway to live Speaches; add CI pipeline

- Face: new 'rage' state — 3-frame kaomoji loop (stare, flip the table,
  put it back) for in-flight request failures; 'error' stays the quiet
  persistent face for a dead link. Sim + artifact + design doc updated.
- Gateway: stt.py/tts.py are now pluggable backends. Default 'speaches'
  talks OpenAI-format HTTP to the live container on :8601
  (faster-whisper-small STT, Kokoro bm_george TTS with 24->16 kHz
  audioop resample); 'embedded' fallback kept behind the [speech] extra.
  Verified with a live TTS->STT round trip (warm: STT 0.27s, TTS 1.9s).
  Docker image is now slim (no CUDA/ML deps). Python pinned to 3.12
  (system 3.8 too old, audioop gone in 3.13).
- CI: .gitea/workflows/build.yml — lint+test on main pushes; on v* tags
  test, build gateway image, push to registry, release, and trigger
  Watchtower (tatlock pattern; needs REGISTRY_USER/REGISTRY_PASSWORD/
  WATCHTOWER_TOKEN secrets). Runtime stack in deploy/desklock-gateway.yml.
- architecture.md: measured speech latencies, deployed-Speaches status,
  CI & deployment section.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

.gitea/workflows/build.yml

@@ -0,0 +1,71 @@
+name: Test, Build and Push
+
+on:
+  push:
+    branches:
+      - main
+    tags:
+      - 'v[0-9]*'
+
+jobs:
+  test-gateway:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install
+        run: pip install -e "./gateway[dev]"
+
+      - name: Lint and test
+        working-directory: gateway
+        run: |
+          ruff check src tests
+          ruff format --check src tests
+          pytest
+
+  release:
+    runs-on: ubuntu-latest
+    if: startsWith(github.ref, 'refs/tags/')
+    steps:
+      - name: Create Gitea Release
+        run: |
+          curl -sf -X POST \
+            -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
+            -H "Content-Type: application/json" \
+            -d '{"tag_name": "${{ github.ref_name }}", "name": "Release ${{ github.ref_name }}", "body": "Automated release for ${{ github.ref_name }}"}' \
+            "${{ github.server_url }}/api/v1/repos/${{ github.repository }}/releases"
+
+  build-gateway:
+    runs-on: ubuntu-latest
+    needs: test-gateway
+    if: startsWith(github.ref, 'refs/tags/')
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Login to Gitea Registry
+        uses: docker/login-action@v3
+        with:
+          registry: git.schweitz.internal
+          username: ${{ secrets.REGISTRY_USER }}
+          password: ${{ secrets.REGISTRY_PASSWORD }}
+
+      - name: Build and push
+        uses: docker/build-push-action@v6
+        with:
+          context: gateway
+          push: true
+          provenance: false
+          sbom: false
+          tags: |
+            git.schweitz.internal/jpmschweitzer/desklock-gateway:latest
+            git.schweitz.internal/jpmschweitzer/desklock-gateway:${{ github.ref_name }}
+
+      - name: Trigger Watchtower update
+        if: success()
+        run: |
+          curl -sf -H "Authorization: Bearer ${{ secrets.WATCHTOWER_TOKEN }}" \
+            http://watchtower:8080/v1/update

AGENTS.md

@@ -13,10 +13,11 @@ one repo:
   (3.4" round 800×800 touch display, dual mics + ES7210 AEC, ES8311 codec + speaker).
 - `gateway/` — Python FastAPI container on tower-of-joy orchestrating STT → chat
   (Tatlock `/v1/chat/completions`) → TTS. Listens on port **8600**. STT/TTS models live
-  in a shared **Speaches** container (proposed port 8601, OpenAI-format API), not in the
-  gateway image; `stt.py`/`tts.py` are pluggable backends (`speaches` default,
-  `embedded` fallback for dev). See docs/architecture.md — the scaffold currently
-  implements only `embedded`.
+  in the shared **Speaches** container (live on port 8601, OpenAI-format API), not in
+  the gateway image; `stt.py`/`tts.py` are pluggable backends (`speaches` default,
+  `embedded` fallback needing the `[speech]` extra). Gateway runs on **Python 3.12
+  exactly** — system python3 on tower-of-joy is 3.8, and `audioop` (used for TTS
+  resampling) is removed in 3.13.
 
 The device and gateway speak a WebSocket protocol defined in `docs/architecture.md`.
 **That doc is the contract** — update it in the same change as any protocol edit on
@@ -87,9 +88,16 @@ make typecheck      # mypy
   and defaults.
 - `stt.py` / `tts.py` defer their heavy imports so the app boots without the `speech`
   extra — keep it that way so protocol tests stay fast.
-- Deployment: Docker image built from `gateway/Dockerfile`, deployed like other
-  tower-of-joy stacks (see `/mnt/media/Projects/system-admin-toj/containers/`). Register
-  the service + port in `CONTAINERS.md` when it first deploys.
+- Deployment is CI-driven: pushing a `v*` tag makes Gitea Actions test, build, and push
+  `desklock-gateway:{latest,tag}` to the registry and trigger Watchtower
+  (`.gitea/workflows/build.yml`; needs `REGISTRY_USER`/`REGISTRY_PASSWORD`/
+  `WATCHTOWER_TOKEN` secrets). Plain pushes to `main` run lint + tests only. The stack
+  file is `deploy/desklock-gateway.yml` — copy into
+  `system-admin-toj/containers/stacks/` and register the service + port 8600 in
+  `CONTAINERS.md` on first deploy.
+- Verify speech changes against the live Speaches container with a real round trip
+  (TTS → STT of a known phrase, expect the transcript back); warm timings to expect:
+  STT ~0.3 s, TTS ~2 s per sentence.
 
 ## Homelab context
 

deploy/desklock-gateway.yml

@@ -0,0 +1,21 @@
+# DeskLock gateway stack.
+# To deploy: copy into system-admin-toj/containers/stacks/ (Portainer) and
+# register the service + port 8600 in CONTAINERS.md.
+#
+# Image is built and pushed by .gitea/workflows/build.yml on version tags;
+# Watchtower picks up :latest afterwards.
+services:
+  desklock-gateway:
+    image: git.schweitz.internal/jpmschweitzer/desklock-gateway:latest
+    container_name: desklock-gateway
+    restart: unless-stopped
+    ports:
+      - "8600:8600"
+    environment:
+      # LAN IP of tower-of-joy: *.schweitz.internal resolves to localhost on the
+      # host, which is wrong inside a container. Switch to http://speaches:8000
+      # (and the tatlock service name) if this stack joins their docker networks.
+      - DESKLOCK_TATLOCK_BASE_URL=http://192.168.86.149:8000
+      - DESKLOCK_SPEACHES_BASE_URL=http://192.168.86.149:8601
+    labels:
+      - com.centurylinklabs.watchtower.enable=true

docs/architecture.md

@@ -87,9 +87,6 @@ models — the container stays a slim pure-Python image with no CUDA/ML dependen
 - `embedded` — in-process faster-whisper / Piper. Kept as a fallback so the gateway can
   run standalone (dev on a laptop, speech container down), at the cost of a fat image.
 
-> **Status note:** the initial scaffold implements only the `embedded` path; the
-> backend switch and Speaches client are the next gateway task.
-
 The gateway is stateless apart from in-flight conversations; it can restart freely.
 
 ### 3. Speech layer — Speaches (container, GPU)
@@ -99,9 +96,15 @@ is a self-hosted, OpenAI-API-compatible speech server: STT via faster-whisper, T
 Kokoro/Piper, dynamic model load/offload with a TTL, and a `/v1/realtime` WebSocket API
 we may adopt later for streaming transcription.
 
-- **Deployment**: its own stack in `system-admin-toj/containers/stacks/`, GPU-enabled.
-  Proposed host port **8601** (verified free; register in `CONTAINERS.md` at deploy).
-  LAN-only like the Tatlock internal route — do not expose through NPM without auth.
+- **Deployed 2026-07-14**: `ghcr.io/speaches-ai/speaches:latest-cuda` on host port
+  **8601**, with `Systran/faster-whisper-small` (STT) and
+  `speaches-ai/Kokoro-82M-v1.0-ONNX` (TTS, 24 kHz — the gateway resamples to the
+  16 kHz device contract; default voice `bm_george`, en-GB male). LAN-only like the
+  Tatlock internal route — do not expose through NPM without auth. Register in
+  `CONTAINERS.md`.
+- **Measured** (live round trip through the gateway code, warm): STT ~0.3 s for a
+  ~3 s utterance; TTS ~1.9 s for a ~3 s sentence. Cold start after model TTL offload
+  adds ~5–10 s to the first request.
 - **Why a shared layer instead of models inside the gateway**: one GPU-resident model
   instance serves the whole homelab. Open WebUI is currently configured with
   `AUDIO_STT_ENGINE=openai` / `AUDIO_TTS_ENGINE=openai` (OpenAI *cloud*) — pointing its
@@ -137,6 +140,7 @@ ported to LVGL. The `STATES` table in the sim defines the contract:
 | `pensive` | `·   ·` | `~` | 7 streams | cycling `...` thought dots |
 | `effort` | `>   <` | `~` | 40 fast streams | **orbit arc on bezel + `[ Ns ]` elapsed counter**, face jitter |
 | `speaking` | `^   ^` | cycles `o O - O = o` | 14 streams | mouth animates ~150 ms/frame |
+| `rage` | — | — | 34 fast streams | 3-frame kaomoji loop through the eyes slot: `(°□°) ┬─┬` → `(╯°□°)╯︵ ┻━┻` → `┬─┬ ノ( º_º ノ)` — flips the table, then composes itself and puts it back |
 | `error` | `x   x` | `-` | none (rain dies) | face dims to 45% |
 
 **Wait cues are a hard requirement** (user-stated): Tatlock turns take 10–25 s, so
@@ -145,8 +149,9 @@ elapsed-seconds counter, and max rain. Never a bare static face during a wait, a
 fake progress bars — only honest cues.
 
 **Protocol → face mapping**: gateway `state: thinking` → `effort`; transcription and
-other short local waits → `pensive`; `listening`/`speaking` map 1:1; WebSocket
-disconnected → `error`; otherwise `idle`.
+other short local waits → `pensive`; `listening`/`speaking` map 1:1; an in-flight
+request failure (STT/Tatlock/TTS error) → `rage` for a few loops, then `idle`;
+WebSocket disconnected → `error` (quiet, persistent); otherwise `idle`.
 
 **LVGL port notes** (for phase 2):
 
@@ -155,6 +160,7 @@ disconnected → `error`; otherwise `idle`.
 - Rain: `lv_canvas` (or a pooled label grid) with per-frame fade; orbit arc = `lv_arc`.
 - Fonts: generate a large monospace glyph font including the katakana subset used in
   `GLYPHS` via `lv_font_conv`; the built-in `unscii` fonts are too small for 800 px.
+  The `rage` frames additionally need `╯ ︵ ┻ ━ ┬ ─ ノ ° □ º` in the subset.
 - The sim's text glow (`text-shadow`) is browser flair — the device renders flat glyphs.
 
 ## Latency budget & streaming
@@ -164,8 +170,8 @@ GPU-resident benchmarks of 2026-07-14, gemma4:e2b at ~100 tok/s):
 
 | Stage | Cost |
 |-------|------|
-| STT (whisper `small`, GPU) | a few hundred ms for a ~5 s utterance |
-| TTS (Piper/Kokoro) | faster than realtime |
+| STT (Speaches whisper `small`) | ~0.3 s warm (measured) |
+| TTS (Speaches Kokoro) | ~1.9 s per ~3 s sentence, warm (measured) |
 | Tatlock Steward analysis | ~6 s warm |
 | **Tatlock, full local flow** | **11–25 s end-to-end** (librarian-routed ~20–25 s) |
 | Tatlock cold start (>2 h idle) | +~8 s (`OLLAMA_KEEP_ALIVE=2h`) |
@@ -239,3 +245,21 @@ it is the one contract between the two halves of the repo.
   gateway is also where a future second endpoint (kitchen, office) would connect.
 - **Monorepo**: the WS protocol couples firmware and gateway; versioning them together
   avoids contract drift.
+
+## CI & deployment
+
+Gitea Actions (`.gitea/workflows/build.yml`), following the tatlock/tatlock-ui pattern:
+
+- **Every push to `main`**: lint + tests for the gateway (Python 3.12).
+- **Version tags (`v0.1.0`, …)**: tests, then build `gateway/` into
+  `git.schweitz.internal/jpmschweitzer/desklock-gateway:{latest,tag}`, push to the
+  Gitea registry, create a release, and trigger Watchtower to roll the running
+  container.
+- Required repo/org secrets: `REGISTRY_USER`, `REGISTRY_PASSWORD`,
+  `WATCHTOWER_TOKEN` (same trio tatlock uses).
+- The runtime stack definition lives in `deploy/desklock-gateway.yml`; copy it into
+  `system-admin-toj/containers/stacks/` to deploy, and register port 8600 in
+  `CONTAINERS.md`.
+
+Firmware is not containerized: it's flashed over USB (`idf.py flash`), with OTA planned
+for phase 5.

gateway/Dockerfile

@@ -4,7 +4,9 @@ WORKDIR /app
 
 COPY pyproject.toml ./
 COPY src ./src
-RUN pip install --no-cache-dir ".[speech]"
+# slim by design: STT/TTS models live in the Speaches container, not this image.
+# For the embedded fallback backend build with ".[speech]" instead.
+RUN pip install --no-cache-dir .
 
 EXPOSE 8600
 CMD ["uvicorn", "desklock_gateway.main:app", "--host", "0.0.0.0", "--port", "8600"]

gateway/Makefile

@@ -1,7 +1,10 @@
 .PHONY: setup run test lint typecheck clean
 
+# audioop pins us below 3.13; system python3 on tower-of-joy is 3.8
+PYTHON ?= python3.12
+
 setup:
-	python3 -m venv .venv
+	$(PYTHON) -m venv .venv
 	.venv/bin/pip install -e ".[dev]"
 
 setup-speech:

gateway/src/desklock_gateway/config.py

@@ -6,10 +6,23 @@ class Settings(BaseSettings):
 
     tatlock_base_url: str = "http://tatlock.schweitz.internal:8000"
     tatlock_model: str = "Tatlock"
+
+    # PCM rate of the device WebSocket contract (docs/architecture.md)
     sample_rate: int = 16000
-    stt_model: str = "small"
-    stt_device: str = "cuda"
-    tts_voice: str = "en_GB-alan-medium"
+
+    # "speaches" (shared speech container) or "embedded" (in-process models)
+    stt_backend: str = "speaches"
+    tts_backend: str = "speaches"
+
+    speaches_base_url: str = "http://localhost:8601"
+    stt_model: str = "Systran/faster-whisper-small"
+    tts_model: str = "speaches-ai/Kokoro-82M-v1.0-ONNX"
+    tts_voice: str = "bm_george"
+
+    # embedded fallback only (requires the [speech] extra)
+    embedded_stt_model: str = "small"
+    embedded_stt_device: str = "cuda"
+    embedded_tts_voice: str = "en_GB-alan-medium"
 
     model_config = {"env_prefix": "DESKLOCK_"}
 

gateway/src/desklock_gateway/stt.py

@@ -1,24 +1,61 @@
-"""Speech-to-text: faster-whisper on the tower-of-joy GPU.
+"""Speech-to-text: Speaches over HTTP (default) or embedded faster-whisper.
 
-Import of faster_whisper is deferred so the gateway can run (health checks,
-protocol tests) without the heavy speech extras installed.
+The embedded import is deferred so the gateway runs without the heavy
+[speech] extras installed.
 """
 
+import io
+import wave
+
+import httpx
+
 from .config import settings
 
-_model = None
+_client: httpx.Client | None = None
+_embedded_model = None
 
 
-def transcribe(pcm: bytes, sample_rate: int | None = None) -> str:
+def transcribe(pcm: bytes, sample_rate: int) -> str:
     """Transcribe raw s16le mono PCM to text."""
-    global _model
-    if _model is None:
+    if settings.stt_backend == "speaches":
+        return _transcribe_speaches(pcm, sample_rate)
+    return _transcribe_embedded(pcm)
+
+
+def _wav_bytes(pcm: bytes, sample_rate: int) -> bytes:
+    buf = io.BytesIO()
+    with wave.open(buf, "wb") as w:
+        w.setnchannels(1)
+        w.setsampwidth(2)
+        w.setframerate(sample_rate)
+        w.writeframes(pcm)
+    return buf.getvalue()
+
+
+def _transcribe_speaches(pcm: bytes, sample_rate: int) -> str:
+    global _client
+    if _client is None:
+        _client = httpx.Client(base_url=settings.speaches_base_url, timeout=60.0)
+    response = _client.post(
+        "/v1/audio/transcriptions",
+        files={"file": ("utterance.wav", _wav_bytes(pcm, sample_rate), "audio/wav")},
+        data={"model": settings.stt_model, "language": "en"},
+    )
+    response.raise_for_status()
+    return response.json()["text"].strip()
+
+
+def _transcribe_embedded(pcm: bytes) -> str:
+    global _embedded_model
+    if _embedded_model is None:
         from faster_whisper import WhisperModel
 
-        _model = WhisperModel(settings.stt_model, device=settings.stt_device)
+        _embedded_model = WhisperModel(
+            settings.embedded_stt_model, device=settings.embedded_stt_device
+        )
 
     import numpy as np
 
     audio = np.frombuffer(pcm, dtype=np.int16).astype(np.float32) / 32768.0
-    segments, _info = _model.transcribe(audio, language="en")
+    segments, _info = _embedded_model.transcribe(audio, language="en")
     return " ".join(segment.text.strip() for segment in segments).strip()

gateway/src/desklock_gateway/tts.py

@@ -1,23 +1,64 @@
-"""Text-to-speech: Piper, resampled to the device sample rate.
+"""Text-to-speech: Speaches/Kokoro over HTTP (default) or embedded Piper.
 
-Import of piper is deferred so the gateway can run without the speech extras.
+Output is always s16le mono PCM at settings.sample_rate (the device WS contract).
+Kokoro synthesizes at 24 kHz, so the speaches path resamples via audioop —
+which pins the runtime to Python 3.12 (audioop is removed in 3.13).
 """
 
+import audioop
+import io
+import wave
+
+import httpx
+
 from .config import settings
 
-_voice = None
+_client: httpx.Client | None = None
+_embedded_voice = None
 
 
 def synthesize(text: str) -> bytes:
     """Synthesize text to raw s16le mono PCM at the configured sample rate."""
-    global _voice
-    if _voice is None:
+    if settings.tts_backend == "speaches":
+        return _synthesize_speaches(text)
+    return _synthesize_embedded(text)
+
+
+def _synthesize_speaches(text: str) -> bytes:
+    global _client
+    if _client is None:
+        _client = httpx.Client(base_url=settings.speaches_base_url, timeout=120.0)
+    response = _client.post(
+        "/v1/audio/speech",
+        json={
+            "model": settings.tts_model,
+            "voice": settings.tts_voice,
+            "input": text,
+            "response_format": "wav",
+        },
+    )
+    response.raise_for_status()
+
+    with wave.open(io.BytesIO(response.content), "rb") as w:
+        rate, channels, width = w.getframerate(), w.getnchannels(), w.getsampwidth()
+        frames = w.readframes(w.getnframes())
+    if width != 2:
+        frames = audioop.lin2lin(frames, width, 2)
+    if channels == 2:
+        frames = audioop.tomono(frames, 2, 0.5, 0.5)
+    if rate != settings.sample_rate:
+        frames, _state = audioop.ratecv(frames, 2, 1, rate, settings.sample_rate, None)
+    return frames
+
+
+def _synthesize_embedded(text: str) -> bytes:
+    global _embedded_voice
+    if _embedded_voice is None:
         from piper import PiperVoice
 
-        _voice = PiperVoice.load(settings.tts_voice)
+        _embedded_voice = PiperVoice.load(settings.embedded_tts_voice)
 
     chunks = bytearray()
-    for chunk in _voice.synthesize_stream_raw(text):
+    for chunk in _embedded_voice.synthesize_stream_raw(text):
         chunks.extend(chunk)
-    # TODO: resample from the Piper voice's native rate to settings.sample_rate
     return bytes(chunks)

sim/face/index.html

@@ -74,6 +74,9 @@
   body[data-state="effort"] #elapsed { display: block; }
   @keyframes spin { to { transform: rotate(360deg); } }
 
+  body[data-state="rage"] #eyes  { font-size: 70px; }
+  body[data-state="rage"] #mouth { display: none; }
+
   @keyframes breathe { 0%,100% { transform: translateY(0); } 50% { transform: translateY(9px); } }
   @keyframes jitter  { 0% { transform: translate(1px,-1px); } 100% { transform: translate(-1px,1px); } }
 
@@ -116,9 +119,13 @@ const STATES = {
   effort:    { eyes: ">   <", mouth: "~",    blink: false, rain: { streams: 40, speed: 16 } },
   speaking:  { eyes: "^   ^", mouth: "o",    blink: true,  talk: true,
                                                             rain: { streams: 14, speed: 9  } },
+  rage:      { mouth: "", blink: false,      rain: { streams: 34, speed: 20 },
+               frames: [ { t: "(°□°)   ┬─┬",     ms: 900  },
+                         { t: "(╯°□°)╯︵ ┻━┻",   ms: 1300 },
+                         { t: "┬─┬ ノ( º_º ノ)", ms: 1400 } ] },
   error:     { eyes: "x   x", mouth: "-",    blink: false, rain: { streams: 0,  speed: 0  } },
 };
-const ORDER = ["idle", "listening", "pensive", "effort", "speaking", "error"];
+const ORDER = ["idle", "listening", "pensive", "effort", "speaking", "rage", "error"];
 const TALK = ["o", "O", "-", "O", "=", "o"];
 const GLYPHS = "アイウエオカキクケコサシスセソタチツテトナニヌネノ0123456789ACEFHKZ$#%*+=<>";
 
@@ -161,12 +168,27 @@ function frame(now) {
 
 /* ---- face behaviour ---- */
 function applyFace() {
-  eyesEl.textContent = st().eyes;
+  eyesEl.textContent = st().eyes || "";
   mouthEl.textContent = st().mouth;
   thoughtEl.textContent = "";
   document.body.dataset.state = cur;
   document.querySelectorAll("#controls button[data-state]").forEach(
     (b) => b.classList.toggle("active", b.dataset.state === cur));
+  playFrames();
+}
+
+/* whole-line kaomoji sequences (rage) render through the eyes slot */
+let animGen = 0;
+function playFrames() {
+  const gen = ++animGen;
+  const frames = st().frames;
+  if (!frames) return;
+  let i = 0;
+  (function step() {
+    if (gen !== animGen) return;
+    eyesEl.textContent = frames[i % frames.length].t;
+    setTimeout(step, frames[i++ % frames.length].ms);
+  })();
 }
 function setState(name) { cur = name; stateSince = performance.now(); applyFace(); }