📌 Project Overview

Linguistic fieldwork often results in hours of video footage that is difficult to process manually. I developed this workflow to streamline the extraction of high-quality audio-visual pairs from recordings of native speakers. This allows for the rapid creation of digital study assets (like Anki decks) from raw video interviews.

🛠 The Annotation Interface

To solve the problem of manual “timestamp hunting,” I built a custom HTML5-based annotation tool. It allows the user to:

  • Segment Audio: Visually define the precise start and end points of a spoken word or phrase.
  • Define Spatial Crops: Use a coordinate-based UI to draw a bounding box around the physical object or picture the speaker is pointing to.
  • Metadata Export: Save these time and coordinate data points as a structured configuration file for batch processing.

⚙️ The Processing Engine

Once the annotation is complete, a series of automated scripts (Python and Shell) handle the heavy lifting:

  • Precision Extraction: Uses FFmpeg to extract audio segments with sub-second accuracy.
  • Automated Cropping: Processes the video frames using ImageMagick or FFmpeg filters to generate high-resolution photos based on the user’s saved coordinates.
  • Asset Optimization: Normalizes audio levels and compresses images for immediate compatibility with flashcard apps like Anki or relational databases.

📈 Impact

This pipeline reduces the time required to process field video, transforming a tedious manual editing task into a streamlined “tag-and-export” workflow. It ensures that every audio clip is perfectly synced with its corresponding visual representation.

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