Source Gathering

To normalize meeting transcripts from Otter.ai, Fireflies.ai, or Gemini, this Skill ingests various file formats and applies content-based auto-detection. It parses speaker labels, dates, and structures, converting everything into a unified NormalizedTranscript schema.

Yes, you can automatically import and process .srt and .vtt files for meeting notes. The system handles local files including .txt and .md, intelligently detecting the transcript format to standardize speaker labels and structural consistency.

The best way to unify diverse meeting transcript formats for analysis is to gather and normalize them into a single schema. This process standardizes dates, speakers, and structures from platforms like Notion, ensuring consistent data output.

Yes, transcript normalization works with local .txt and .json files. The system supports multiple file extensions and platform exports, using robust parsing to ensure structural consistency across all imported meeting data.

You need to standardize speaker labels when gathering meeting transcripts to ensure consistent data for analysis. Normalization handles structural inconsistencies from diverse sources, mapping varying speaker formats into a unified schema.

To prepare a Fireflies.ai export for a meeting analysis workflow, process the file using auto-detection to normalize the transcript. This converts the data into a unified schema, standardizing dates and speakers for downstream analysis.