NeMo CuratorLearn what video curation is and how you use NeMo Curator to turn long videos into high‑quality, searchable clips. Depending on the use case, this can involve processing 100+ PB of videos. To efficiently process this quantity of videos, NeMo Curator provides highly optimized curation pipelines.
Identify when to use NeMo Curator by matching your goals to common video curation scenarios.
Understand how components work together so you can plan, scale, and troubleshoot video pipelines. The following diagram outlines NeMo Curator’s video curation architecture:
Video pipelines use the XennaExecutor backend by default, which provides optimized support for GPU-accelerated video processing including hardware decoders and encoders. You do not need to import or configure the executor unless you want to use an alternative backend. For more information about customizing backends, refer to Pipeline Execution Backends.
Get oriented and prepare your environment so you can start curating videos with confidence.
Learn about the architecture, stages, pipelines, and data flow for video curation stages pipelines ray
Install NeMo Curator, configure storage, prepare data, and run your first video pipeline.
Follow task-based guides to load, process, and write curated video data end to end.
Bring videos into your pipeline from local paths or remote sources you control.
Load videos from local paths or S3-compatible and HTTP(S) URLs. local s3 file-list
Provide an explicit JSON file list for remote datasets under a root prefix. file-list s3
Transform raw videos into curated clips, frames, embeddings, and metadata you can use.
Split long videos into shorter clips using fixed stride or scene-change detection. clips fixed-stride transnetv2
Encode clips to H.264 using CPU or GPU encoders and tune performance. clips h264_nvenc libopenh264 libx264
Apply motion-based filtering and aesthetic filtering to improve dataset quality. clips frames motion aesthetic
Extract frames from clips or full videos for embeddings, filtering, and analysis. frames nvdec ffmpeg fps
Generate clip-level embeddings with Cosmos-Embed1 for search and duplicate removal. clips cosmos-embed1
Generate Qwen‑VL captions and optional WebP previews; optionally enhance with Qwen‑LM. captions previews qwen webp
Remove near-duplicates using semantic clustering and similarity with generated embeddings. clips semantic pairwise kmeans
Save outputs in formats your training or retrieval systems can consume at scale.
Practice with guided, hands-on examples to build, customize, and run video pipelines.
