video_scores: Run FER on a YouTube video using a Hugging Face CLIP model

In transforEmotion: Sentiment Analysis for Text, Image and Video using Transformer Models

Run FER on a YouTube video using a Hugging Face CLIP model

Description

This function retrieves facial expression recognition (FER) scores from a specific number of frames extracted from a YouTube video using a specified Hugging Face CLIP model. It utilizes Python libraries for facial recognition and emotion detection in text, images, and video.

Usage

video_scores(
  video,
  classes,
  nframes = 100,
  face_selection = "largest",
  start = 0,
  end = -1,
  uniform = FALSE,
  ffreq = 15,
  save_video = FALSE,
  save_frames = FALSE,
  save_dir = "temp/",
  video_name = "temp",
  model = "oai-base",
  local_model_path = NULL
)

Arguments

video	The URL of the YouTube video to analyze.
classes	A character vector specifying the classes to analyze.
nframes	The number of frames to analyze in the video. Default is 100.
face_selection	The method for selecting faces in the video. Options are "largest", "left", "right", or "none". Default is "largest". Use "none" to classify the whole frame without face cropping.
start	The start time of the video range to analyze. Default is 0.
end	The end time of the video range to analyze. Default is -1 and this means that video won't be cut. If end is a positive number greater than start, the video will be cut from start to end.
uniform	Logical indicating whether to uniformly sample frames from the video. Default is FALSE.
ffreq	The frame frequency for sampling frames from the video. Default is 15.
save_video	Logical indicating whether to save the analyzed video. Default is FALSE.
save_frames	Logical indicating whether to save the analyzed frames. Default is FALSE.
save_dir	The directory to save the analyzed frames. Default is "temp/".
video_name	The name of the analyzed video. Default is "temp".
model	A string specifying the vision model to use. Options include: Built-in models: "oai-base" (default), "oai-large", "eva-8B", "jina-v2" Any valid HuggingFace model ID Custom registered models (see register_vision_model) Use list_vision_models to see all available models. Note: Video processing is memory-intensive, so use caution with large models.
local_model_path	Optional. Path to a local directory containing a pre-downloaded HuggingFace model. If provided, the model will be loaded from this directory instead of being downloaded from HuggingFace. This is useful for offline usage or for using custom fine-tuned models. On Linux/Mac, look in ~/.cache/huggingface/hub/ folder for downloaded models. Navigate to the snapshots folder for the relevant model and point to the directory which contains the config.json file. For example: "/home/username/.cache/huggingface/hub/models–cross-encoder–nli-distilroberta-base/snapshots/b5b020e8117e1ddc6a0c7ed0fd22c0e679edf0fa/" On Windows, the base path is C:\Users\USERNAME\.cache\huggingface\transformers\ Warning: Using very large models from local paths may cause memory issues or crashes depending on your system's resources, especially when processing videos with many frames.

Value

A result object containing the analyzed video scores.

Data Privacy

All processing is done locally with the downloaded model, and your video frames are never sent to any remote server or third-party.

Author(s)

Aleksandar Tomasevic <[e-mail]>

transforEmotion documentation built on Jan. 8, 2026, 5:06 p.m.