koko210Serve
9e5511da21
- miku-stt: switch PyTorch CUDA -> CPU-only (~2.5 GB savings) - Silero VAD already runs on CPU via ONNX (onnx=True), CUDA PyTorch was waste - faster-whisper/CTranslate2 uses CUDA directly, no PyTorch GPU needed - torch+torchaudio layer: 3.3 GB -> 796 MB; total image 9+ GB -> 6.83 GB - Tested: Silero VAD loads (ONNX), Whisper loads on cuda, server ready - llama-swap-rocm: add root .dockerignore to fix 31 GB build context - Dockerfile clones all sources from git, never COPYs from context - 19 GB of GGUF model files were being transferred on every build - Now excludes everything (*), near-zero context transfer - anime-face-detector: add .dockerignore to exclude accumulated outputs - api/outputs/ (56 accumulated detection files) no longer baked into image - api/__pycache__/ and images/ also excluded - .gitignore: remove .dockerignore exclusion so these files are tracked
17 lines
335 B
Plaintext
17 lines
335 B
Plaintext
# Low-latency STT dependencies
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websockets>=12.0
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numpy>=1.24.0
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# Faster-whisper backend (GPU accelerated)
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faster-whisper>=1.0.0
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ctranslate2>=4.4.0
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# Audio processing
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soundfile>=0.12.0
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# VAD - Silero (loaded via torch.hub, runs on CPU via ONNX)
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# Requires torch (CPU-only) - see requirements-gpu-torch.txt
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# Utilities
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aiohttp>=3.9.0
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