feat: log the resolved whisper device on bridge load

The bridge only logged Whisper's device on the CPU-fallback path, so a
successful GPU (or silent CPU) load was invisible. Print the CTranslate2-
resolved device on success and on the fallback load, so it is verifiable that
STT is actually running on cuda alongside ollama (GPU) and MeloTTS (cuda).

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

Dockerfile

@@ -10,8 +10,14 @@ ENV DEBIAN_FRONTEND=noninteractive \
     DISPLAY=:1 \
     PLAYWRIGHT_SKIP_BROWSER_DOWNLOAD=1 \
     PATH=/opt/venv/bin:/root/.bun/bin:/usr/local/bin:/usr/bin:/bin \
-    NVIDIA_VISIBLE_DEVICES=all \
+    NVIDIA_VISIBLE_DEVICES=all
-    NVIDIA_DRIVER_CAPABILITIES=compute,utility
+
+# `video` is REQUIRED for NVENC/NVDEC: it tells the NVIDIA Container Toolkit to
+# inject libnvidia-encode.so.1 / libnvidia-decode.so.1 into the container. With
+# only `compute,utility` you get CUDA (ollama/whisper/melo) + nvidia-smi, but the
+# Go-Live broadcast's h264_nvenc fails with "Cannot load libnvidia-encode.so.1".
+# Applies on both Linux (CDI) and Windows Docker Desktop (WSL2).
+ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
 
 # --- System packages: desktop, VNC, Chrome deps, ffmpeg, python, ocr ---
 RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -63,7 +69,11 @@ RUN ls -d /opt/venv/lib/python*/site-packages/nvidia/cublas/lib \
 #     Heavy layer (torch CPU + transformers + MeCab); placed before the app
 #     COPY so it stays cached across source-only changes. ---
 COPY docker/setup-melo.sh /app/docker/setup-melo.sh
-RUN bash /app/docker/setup-melo.sh
+# Strip CR before running: a Windows checkout (autocrlf) yields CRLF, which makes
+# bash read line 18 as `set -euxo pipefail\r` and abort with
+# "set: pipefail: invalid option name". .gitattributes pins *.sh to LF, but this
+# keeps the build working even on a not-yet-renormalised working tree.
+RUN sed -i 's/\r$//' /app/docker/setup-melo.sh && bash /app/docker/setup-melo.sh
 
 # --- Human input + window management for the on-screen Chrome control tool.
 #     Placed AFTER the heavy melo layer so it doesn't bust that cache. xdotool
@@ -81,6 +91,11 @@ RUN cd /app/bot && bun install --frozen-lockfile || bun install
 COPY . /app
 WORKDIR /app
 
+# Normalise all container shell scripts to LF. On a Windows checkout (autocrlf)
+# these arrive as CRLF, which would break their shebangs at runtime (entrypoint,
+# run-*.sh) the same way it broke setup-melo.sh at build time.
+RUN find /app/docker /app/scripts -name '*.sh' -exec sed -i 's/\r$//' {} +
+
 # --- Default Piper voice (best-effort at build; entrypoint retries if absent) ---
 RUN bash docker/download-piper.sh || true
 

bridge/server.py

@@ -150,12 +150,17 @@ def _ensure_brain():
             compute = os.environ.get("WHISPER_COMPUTE_TYPE", "auto")
             try:
                 whisper = WhisperModel(cfg.whisper_model, device=device, compute_type=compute)
+                # Log the device actually resolved by CTranslate2 (device="auto"
+                # picks cuda when available) so a silent CPU load is visible.
+                resolved = str(getattr(getattr(whisper, "model", None), "device", device)).lower()
+                print(f"[bridge] whisper loaded on {resolved} (compute={compute})", flush=True)
             except Exception as ge:
                 # GPU not available / unsupported -> fall back to CPU so the
                 # bridge still works without a GPU passed to the container.
                 if device != "cpu":
                     print(f"[bridge] whisper device='{device}' failed ({ge}); falling back to CPU", flush=True)
                     whisper = WhisperModel(cfg.whisper_model, device="cpu", compute_type="int8")
+                    print("[bridge] whisper loaded on cpu (compute=int8)", flush=True)
                 else:
                     raise