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2 Commits
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677bfcd2a9 | ||
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e49be6d04e |
10
Dockerfile
10
Dockerfile
@@ -10,8 +10,14 @@ ENV DEBIAN_FRONTEND=noninteractive \
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DISPLAY=:1 \
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PLAYWRIGHT_SKIP_BROWSER_DOWNLOAD=1 \
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PATH=/opt/venv/bin:/root/.bun/bin:/usr/local/bin:/usr/bin:/bin \
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NVIDIA_VISIBLE_DEVICES=all \
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NVIDIA_DRIVER_CAPABILITIES=compute,utility
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NVIDIA_VISIBLE_DEVICES=all
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# `video` is REQUIRED for NVENC/NVDEC: it tells the NVIDIA Container Toolkit to
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# inject libnvidia-encode.so.1 / libnvidia-decode.so.1 into the container. With
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# only `compute,utility` you get CUDA (ollama/whisper/melo) + nvidia-smi, but the
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# Go-Live broadcast's h264_nvenc fails with "Cannot load libnvidia-encode.so.1".
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# Applies on both Linux (CDI) and Windows Docker Desktop (WSL2).
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ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
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# --- System packages: desktop, VNC, Chrome deps, ffmpeg, python, ocr ---
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RUN apt-get update && apt-get install -y --no-install-recommends \
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@@ -150,12 +150,17 @@ def _ensure_brain():
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compute = os.environ.get("WHISPER_COMPUTE_TYPE", "auto")
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try:
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whisper = WhisperModel(cfg.whisper_model, device=device, compute_type=compute)
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# Log the device actually resolved by CTranslate2 (device="auto"
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# picks cuda when available) so a silent CPU load is visible.
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resolved = str(getattr(getattr(whisper, "model", None), "device", device)).lower()
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print(f"[bridge] whisper loaded on {resolved} (compute={compute})", flush=True)
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except Exception as ge:
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# GPU not available / unsupported -> fall back to CPU so the
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# bridge still works without a GPU passed to the container.
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if device != "cpu":
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print(f"[bridge] whisper device='{device}' failed ({ge}); falling back to CPU", flush=True)
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whisper = WhisperModel(cfg.whisper_model, device="cpu", compute_type="int8")
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print("[bridge] whisper loaded on cpu (compute=int8)", flush=True)
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else:
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raise
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