Embeddings#

Generate clip-level embeddings for search, question answering, filtering, and duplicate removal.

Use Cases#

• Prepare semantic vectors for search, clustering, and near-duplicate detection.
• Score optional text prompts against clip content.
• Enable downstream filtering or retrieval tasks that need clip-level vectors.

Before You Start#

• Create clips upstream. Refer to Clipping.
• Provide frames for embeddings or sample at the required rate. Refer to Frame Extraction.
• Access to model weights on each node (the stages download weights if missing).

Quickstart#

Use the pipeline stages or the example script flags to generate clip-level embeddings.

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.video.clipping.clip_frame_extraction import ClipFrameExtractionStage
from nemo_curator.utils.decoder_utils import FrameExtractionPolicy, FramePurpose
from nemo_curator.stages.video.embedding.cosmos_embed1 import (
    CosmosEmbed1FrameCreationStage,
    CosmosEmbed1EmbeddingStage,
)

pipe = Pipeline(name="video_embeddings_example")
pipe.add_stage(
    ClipFrameExtractionStage(
        extraction_policies=(FrameExtractionPolicy.sequence,),
        extract_purposes=(FramePurpose.EMBEDDINGS,),
        target_res=(-1, -1),
        verbose=True,
    )
)
pipe.add_stage(CosmosEmbed1FrameCreationStage(model_dir="/models", variant="224p", target_fps=2.0, verbose=True))
pipe.add_stage(CosmosEmbed1EmbeddingStage(model_dir="/models", variant="224p", gpu_memory_gb=20.0, verbose=True))
pipe.run()

# Cosmos-Embed1 (224p)
python tutorials/video/getting-started/video_split_clip_example.py \
  ... \
  --generate-embeddings \
  --embedding-algorithm cosmos-embed1-224p \
  --embedding-gpu-memory-gb 20.0

Embedding Options#

Cosmos-Embed1#

1. Add CosmosEmbed1FrameCreationStage to transform extracted frames into model-ready tensors.

   from nemo_curator.stages.video.embedding.cosmos_embed1 import (
       CosmosEmbed1FrameCreationStage,
       CosmosEmbed1EmbeddingStage,
   )
   
   frames = CosmosEmbed1FrameCreationStage(
       model_dir="/models",
       variant="224p",  # or 336p, 448p
       target_fps=2.0,
       verbose=True,
   )

2. Add CosmosEmbed1EmbeddingStage to generate clip.cosmos_embed1_embedding and optional clip.cosmos_embed1_text_match.

   embed = CosmosEmbed1EmbeddingStage(
       model_dir="/models",
       variant="224p",
       gpu_memory_gb=20.0,
       verbose=True,
   )

Parameters#

Outputs#

• clip.cosmos_embed1_frames → temporary tensors used by the embedding stage
• clip.cosmos_embed1_embedding → final clip-level vector (NumPy array)
• Optional: clip.cosmos_embed1_text_match

Troubleshooting#

• Not enough frames for embeddings: Increase target_fps during frame extraction or adjust clip length so that the model receives the required number of frames.
• Out of memory during embedding: Lower gpu_memory_gb, reduce batch size if exposed, or use a smaller resolution variant.
• Weights not found on node: Confirm model_dir and network access. The stages download weights if missing.

Next Steps#

• Use embeddings for duplicate removal. Refer to Duplicate Removal.
• Generate captions and previews for review workflows. Refer to Captions & Preview.