wehub-resource-sync
61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
# Project constants
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# SigLIP model
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MODEL_NAME = "google/siglip-so400m-patch14-384"
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# S3 paths
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S3_VIDEOS_PREFIX = "stock-videos/"
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S3_EMBEDDINGS_PREFIX = "embeddings/"
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# Scene change detection
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SCENE_CHANGE_THRESHOLD = 0.15 # EMA score threshold for detecting scene changes
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EMA_ALPHA = 0.9 # EMA decay factor (higher = slower adaptation)
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# Pexels API
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PEXELS_API_BASE = "https://api.pexels.com/videos"
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# Concurrency limits
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MAX_CONCURRENT_DOWNLOADS = 5
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MAX_CONCURRENT_UPLOADS = 5
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# Video normalization defaults (384x384 matches model input size for fastest inference)
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NORMALIZE_WIDTH = 384
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NORMALIZE_HEIGHT = 384
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NORMALIZE_FPS = 30
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# Video search queries (for downloading stock videos)
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SEARCH_QUERIES = [
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"kitchen cooking",
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"office meeting",
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"street city traffic",
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"living room home",
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"restaurant cafe",
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"parking lot cars",
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"classroom students",
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"warehouse industrial",
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"grocery store shopping",
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"gym exercise workout",
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"person speaking",
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"crowd people walking",
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"laptop computer work",
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"outdoor nature",
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"presentation business",
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"conversation talking",
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"running jogging",
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"dining food",
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"shopping mall",
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"park outdoor",
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]
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# Video chunking defaults
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DEFAULT_NUM_FRAMES = 16
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DEFAULT_CHUNK_DURATION = 10.0
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# FFmpeg configuration, restricting to 2 threads to avoid over subscription.
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FFMPEG_THREADS = 6
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# Setting this to 2 because i am assuming average
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# video length in my corpus is 20 seconds.
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# 20/10(default chunk duration) = 2
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# this means we want to set num_cpus to 4 for deployment.
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NUM_WORKERS = 3
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