feat(whisper): add CUDA Blackwell server, promote to primary on :18801

Adds a custom whisper.cpp Docker image built with CMAKE_CUDA_ARCHITECTURES=120
so it actually initializes on the RTX 5070 Ti — the upstream
ghcr.io/ggml-org/whisper.cpp:main-cuda only ships kernels for sm_75/80/86/90.

Compose changes:
- New whisper-init one-shot service downloads ggml-medium.bin and ggml-small.bin
  into the shared volume on first run, fixing the original crash where
  whisper-server tried to load a model that was never fetched.
- New whisper-server-gpu service (image whisper.cpp:cuda-blackwell, built
  locally from ./whisper-cuda-blackwell/Dockerfile) on port 18801 — the
  benchmarked path (~150 ms per short clip, ~93x faster than CPU/medium with
  identical WER on JFK + 4 TTS samples).
- Existing whisper-server (CPU/medium) moves to port 18811 as the fallback
  for when GPU is unavailable. Container names unchanged so monitoring and
  volume bindings keep working.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

docker-compose.yaml

@@ -30,31 +30,121 @@ services:
   #     start_period: 15s
   #     retries: 3
 
-  # Optional local dependency: whisper.cpp server for audio transcription.
-  # Start with: docker compose --profile voice up -d whisper-server
-  whisper-server:
-    image: ghcr.io/ggml-org/whisper.cpp@sha256:3a39e86d5a0e911086b5cbebc9029cac71b02fbd08e217b775857de1490f55bf
-    container_name: whisper-server
+  # One-shot init: download whisper models into the shared volume if missing.
+  # The base image only ships ggml-base.en.bin; the servers below require:
+  #   - ggml-medium.bin for the CPU server
+  #   - ggml-small.bin  for the GPU server (small fits in the limited VRAM left after gemma)
+  whisper-init:
+    image: ghcr.io/ggml-org/whisper.cpp:main
+    container_name: whisper-init
+    profiles: ["voice"]
+    restart: "no"
+    volumes:
+      - whisper-models:/app/models
+    entrypoint: ["sh", "-c"]
+    command:
+      - |
+        set -e
+        for m in medium small; do
+          if [ -f /app/models/ggml-$$m.bin ]; then
+            echo "Model ggml-$$m.bin already present, skipping download."
+          else
+            echo "Downloading ggml-$$m.bin..."
+            sh /app/models/download-ggml-model.sh $$m /app/models
+          fi
+        done
+
+  # Primary whisper.cpp server: NVIDIA RTX 5070 Ti via CUDA (Blackwell sm_120).
+  # Uses ggml-small.bin (~850 MiB VRAM) — fits alongside gemma 3 12b which runs
+  # with `--parallel 1` (frees ~900 MiB of VRAM). Benchmarked at ~150 ms per
+  # short clip, ~93x faster than the CPU server below with identical WER.
+  #
+  # The official `ghcr.io/ggml-org/whisper.cpp:main-cuda` ships kernels only
+  # for sm_75/80/86/90 and fails to init CUDA on Blackwell. We build a custom
+  # image with `CMAKE_CUDA_ARCHITECTURES=120` from the local Dockerfile.
+  # Build manually with: docker build -t whisper.cpp:cuda-blackwell ./whisper-cuda-blackwell
+  # Or `docker compose --profile voice build whisper-server-gpu`.
+  whisper-server-gpu:
+    image: whisper.cpp:cuda-blackwell
+    build:
+      context: ./whisper-cuda-blackwell
+      dockerfile: Dockerfile
+    container_name: whisper-server-gpu
     restart: unless-stopped
     profiles: ["voice"]
     ports:
       - "18801:8080"
     volumes:
       - whisper-models:/app/models
-    # Override image entrypoint so args are passed directly to whisper-server.
     entrypoint: ["whisper-server"]
     command:
       - --model
-      - /app/models/ggml-base.en.bin
+      - /app/models/ggml-small.bin
       - --host
       - 0.0.0.0
       - --port
       - "8080"
       - --convert
       - --language
-      - en
+      - auto
       - --inference-path
       - /v1/audio/transcriptions
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    depends_on:
+      whisper-init:
+        condition: service_completed_successfully
+    healthcheck:
+      test:
+        [
+          "CMD-SHELL",
+          "curl -f http://localhost:8080/ >/dev/null 2>&1 || exit 1",
+        ]
+      interval: 30s
+      timeout: 5s
+      start_period: 30s
+      retries: 3
+    labels:
+      agentmon.monitor: "true"
+      agentmon.role: "voice"
+      agentmon.port: "18801"
+
+  # Fallback whisper.cpp server: CPU-only, medium model.
+  # Kept around for resilience — runs if the GPU server is down (driver issue,
+  # gemma takes all VRAM, custom image broken, etc.). Uses no GPU resources.
+  # ~14 s per short clip (medium-on-CPU is 90x slower than small-on-GPU above).
+  # Start with: docker compose --profile voice up -d whisper-server
+  whisper-server:
+    image: ghcr.io/ggml-org/whisper.cpp:main
+    container_name: whisper-server
+    restart: unless-stopped
+    profiles: ["voice"]
+    ports:
+      - "18811:8080"
+    volumes:
+      - whisper-models:/app/models
+    # Override image entrypoint so args are passed directly to whisper-server.
+    entrypoint: ["whisper-server"]
+    command:
+      - --model
+      - /app/models/ggml-medium.bin
+      - --host
+      - 0.0.0.0
+      - --port
+      - "8080"
+      - --convert
+      - --language
+      - auto
+      - --inference-path
+      - /v1/audio/transcriptions
+    depends_on:
+      whisper-init:
+        condition: service_completed_successfully
     healthcheck:
       test:
         [
@@ -68,7 +158,7 @@ services:
     labels:
       agentmon.monitor: "true"
       agentmon.role: "voice"
-      agentmon.port: "18801"
+      agentmon.port: "18811"
 
   # kokoro TTS
   kokoro-tts: